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1. High-resolution epidemiological landscape from ~290,000 SARS-CoV-2 genomes from Denmark

Author: Mark P. Khurana, Jacob Curran-Sebastian, Neil Scheidwasser, Christian Morgenstern, Morten Rasmussen, Jannik Fonager, Marc Stegger, Man-Hung Eric Tang, Jonas L. Juul, Leandro Andrés Escobar-Herrera, Frederik Trier Møller, The Danish COVID-19 Genome Consortium (DCGC), Mads Albertsen, Moritz U. G. Kraemer, Louis du Plessis, Pikka Jokelainen, Sune Lehmann, Tyra G. Krause, Henrik Ullum, David A. Duchêne, Laust H. Mortensen, and Samir Bhatt
Subjects: Science
Abstract: Abstract Vast amounts of pathogen genomic, demographic and spatial data are transforming our understanding of SARS-CoV-2 emergence and spread. We examined the drivers of molecular evolution and spread of 291,791 SARS-CoV-2 genomes from Denmark in 2021. With a sequencing rate consistently exceeding 60%, and up to 80% of PCR-positive samples between March and November, the viral genome set is broadly whole-epidemic representative. We identify a consistent rise in viral diversity over time, with notable spikes upon the importation of novel variants (e.g., Delta and Omicron). By linking genomic data with rich individual-level demographic data from national registers, we find that individuals aged 75 years had a lower contribution to molecular change (i.e., branch lengths) compared to other age groups, but similar molecular evolutionary rates, suggesting a lower likelihood of introducing novel variants. Similarly, we find greater molecular change among vaccinated individuals, suggestive of immune evasion. We also observe evidence of transmission in rural areas to follow predictable diffusion processes. Conversely, urban areas are expectedly more complex due to their high mobility, emphasising the role of population structure in driving virus spread. Our analyses highlight the added value of integrating genomic data with detailed demographic and spatial information, particularly in the absence of structured infection surveys.
Published: 2024
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2. Human movement and environmental barriers shape the emergence of dengue

Author: Vinyas Harish, Felipe J. Colón-González, Filipe R. R. Moreira, Rory Gibb, Moritz U. G. Kraemer, Megan Davis, Robert C. Reiner, David M. Pigott, T. Alex Perkins, Daniel J. Weiss, Isaac I. Bogoch, Gonzalo Vazquez-Prokopec, Pablo Manrique Saide, Gerson L. Barbosa, Ester C. Sabino, Kamran Khan, Nuno R. Faria, Simon I. Hay, Fabián Correa-Morales, Francisco Chiaravalloti-Neto, and Oliver J. Brady
Subjects: Science
Abstract: Abstract Understanding how emerging infectious diseases spread within and between countries is essential to contain future pandemics. Spread to new areas requires connectivity between one or more sources and a suitable local environment, but how these two factors interact at different stages of disease emergence remains largely unknown. Further, no analytical framework exists to examine their roles. Here we develop a dynamic modelling approach for infectious diseases that explicitly models both connectivity via human movement and environmental suitability interactions. We apply it to better understand recently observed (1995-2019) patterns as well as predict past unobserved (1983-2000) and future (2020-2039) spread of dengue in Mexico and Brazil. We find that these models can accurately reconstruct long-term spread pathways, determine historical origins, and identify specific routes of invasion. We find early dengue invasion is more heavily influenced by environmental factors, resulting in patchy non-contiguous spread, while short and long-distance connectivity becomes more important in later stages. Our results have immediate practical applications for forecasting and containing the spread of dengue and emergence of new serotypes. Given current and future trends in human mobility, climate, and zoonotic spillover, understanding the interplay between connectivity and environmental suitability will be increasingly necessary to contain emerging and re-emerging pathogens.
Published: 2024
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3. Drivers and impact of the early silent invasion of SARS-CoV-2 Alpha

Author: Benjamin Faucher, Chiara E. Sabbatini, Peter Czuppon, Moritz U. G. Kraemer, Philippe Lemey, Vittoria Colizza, François Blanquart, Pierre-Yves Boëlle, and Chiara Poletto
Subjects: Science
Abstract: Abstract SARS-CoV-2 variants of concern (VOCs) circulated cryptically before being identified as a threat, delaying interventions. Here we studied the drivers of such silent spread and its epidemic impact to inform future response planning. We focused on Alpha spread out of the UK. We integrated spatio-temporal records of international mobility, local epidemic growth and genomic surveillance into a Bayesian framework to reconstruct the first three months after Alpha emergence. We found that silent circulation lasted from days to months and decreased with the logarithm of sequencing coverage. Social restrictions in some countries likely delayed the establishment of local transmission, mitigating the negative consequences of late detection. Revisiting the initial spread of Alpha supports local mitigation at the destination in case of emerging events.
Published: 2024
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4. Characterizing collective physical distancing in the U.S. during the first nine months of the COVID-19 pandemic

Author: Brennan Klein, Timothy LaRock, Stefan McCabe, Leo Torres, Lisa Friedland, Maciej Kos, Filippo Privitera, Brennan Lake, Moritz U. G. Kraemer, John S. Brownstein, Richard Gonzalez, David Lazer, Tina Eliassi-Rad, Samuel V. Scarpino, Alessandro Vespignani, and Matteo Chinazzi
Subjects: Computer applications to medicine. Medical informatics, R858-859.7
Published: 2024

5. A systematic review of the data, methods and environmental covariates used to map Aedes-borne arbovirus transmission risk

Author: Ah-Young Lim, Yalda Jafari, Jamie M. Caldwell, Hannah E. Clapham, Katy A. M. Gaythorpe, Laith Hussain-Alkhateeb, Michael A. Johansson, Moritz U. G. Kraemer, Richard J. Maude, Clare P. McCormack, Jane P. Messina, Erin A. Mordecai, Ingrid B. Rabe, Robert C. Reiner, Sadie J. Ryan, Henrik Salje, Jan C. Semenza, Diana P. Rojas, and Oliver J. Brady
Subjects: Aedes-borne diseases, Arboviruses, Risk mapping, Geostatistical models, Predictive modelling, Dengue, Infectious and parasitic diseases, RC109-216
Abstract: Abstract Background Aedes (Stegomyia)-borne diseases are an expanding global threat, but gaps in surveillance make comprehensive and comparable risk assessments challenging. Geostatistical models combine data from multiple locations and use links with environmental and socioeconomic factors to make predictive risk maps. Here we systematically review past approaches to map risk for different Aedes-borne arboviruses from local to global scales, identifying differences and similarities in the data types, covariates, and modelling approaches used. Methods We searched on-line databases for predictive risk mapping studies for dengue, Zika, chikungunya, and yellow fever with no geographical or date restrictions. We included studies that needed to parameterise or fit their model to real-world epidemiological data and make predictions to new spatial locations of some measure of population-level risk of viral transmission (e.g. incidence, occurrence, suitability, etc.). Results We found a growing number of arbovirus risk mapping studies across all endemic regions and arboviral diseases, with a total of 176 papers published 2002–2022 with the largest increases shortly following major epidemics. Three dominant use cases emerged: (i) global maps to identify limits of transmission, estimate burden and assess impacts of future global change, (ii) regional models used to predict the spread of major epidemics between countries and (iii) national and sub-national models that use local datasets to better understand transmission dynamics to improve outbreak detection and response. Temperature and rainfall were the most popular choice of covariates (included in 50% and 40% of studies respectively) but variables such as human mobility are increasingly being included. Surprisingly, few studies (22%, 31/144) robustly tested combinations of covariates from different domains (e.g. climatic, sociodemographic, ecological, etc.) and only 49% of studies assessed predictive performance via out-of-sample validation procedures. Conclusions Here we show that approaches to map risk for different arboviruses have diversified in response to changing use cases, epidemiology and data availability. We identify key differences in mapping approaches between different arboviral diseases, discuss future research needs and outline specific recommendations for future arbovirus mapping.
Published: 2023
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6. Forecasting hospital-level COVID-19 admissions using real-time mobility data

Author: Brennan Klein, Ana C. Zenteno, Daisha Joseph, Mohammadmehdi Zahedi, Michael Hu, Martin S. Copenhaver, Moritz U. G. Kraemer, Matteo Chinazzi, Michael Klompas, Alessandro Vespignani, Samuel V. Scarpino, and Hojjat Salmasian
Subjects: Medicine
Abstract: Klein et al. use mobility data to forecast COVID-19 admissions for five Massachusetts hospitals. Combining aggregated mobile device data about users’ contact patterns, commuting volume, and mobility range with COVID hospitalizations and test-positivity data increases the lead-time of accurate predictions for individual hospitals.
Published: 2023
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7. Global disparities in SARS-CoV-2 genomic surveillance

Author: Anderson F. Brito, Elizaveta Semenova, Gytis Dudas, Gabriel W. Hassler, Chaney C. Kalinich, Moritz U. G. Kraemer, Joses Ho, Houriiyah Tegally, George Githinji, Charles N. Agoti, Lucy E. Matkin, Charles Whittaker, Bulgarian SARS-CoV-2 sequencing group, Communicable Diseases Genomics Network (Australia and New Zealand), COVID-19 Impact Project, Danish Covid-19 Genome Consortium, Fiocruz COVID-19 Genomic Surveillance Network, GISAID core curation team, Network for Genomic Surveillance in South Africa (NGS-SA), Swiss SARS-CoV-2 Sequencing Consortium, Benjamin P. Howden, Vitali Sintchenko, Neta S. Zuckerman, Orna Mor, Heather M. Blankenship, Tulio de Oliveira, Raymond T. P. Lin, Marilda Mendonça Siqueira, Paola Cristina Resende, Ana Tereza R. Vasconcelos, Fernando R. Spilki, Renato Santana Aguiar, Ivailo Alexiev, Ivan N. Ivanov, Ivva Philipova, Christine V. F. Carrington, Nikita S. D. Sahadeo, Ben Branda, Céline Gurry, Sebastian Maurer-Stroh, Dhamari Naidoo, Karin J. von Eije, Mark D. Perkins, Maria van Kerkhove, Sarah C. Hill, Ester C. Sabino, Oliver G. Pybus, Christopher Dye, Samir Bhatt, Seth Flaxman, Marc A. Suchard, Nathan D. Grubaugh, Guy Baele, and Nuno R. Faria
Subjects: Science
Abstract: In this study, the authors provide a global overview of SARS-CoV-2 genome sequencing, and estimate the proportion of cases sequenced and time to genome upload. They identify disparities and highlight the need to strengthen surveillance in lower and middle income countries.
Published: 2022
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8. Malaria elimination on Hainan Island despite climate change

Author: Huaiyu Tian, Naizhe Li, Yapin Li, Moritz U. G. Kraemer, Hua Tan, Yonghong Liu, Yidan Li, Ben Wang, Peiyi Wu, Bernard Cazelles, José Lourenço, Dongqi Gao, Dingwei Sun, Wenjing Song, Yuchun Li, Oliver G. Pybus, Guangze Wang, and Christopher Dye
Subjects: Medicine
Abstract: Tian et al. use mathematical modelling to estimate the impact of various interventions on malaria incidence on Hainan Island, also taking into account climate change. They find that although malaria transmission has been exacerbated by climate change, insecticide-treated bed nets and other interventions were effective in controlling the disease.
Published: 2022
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9. Trade-offs between individual and ensemble forecasts of an emerging infectious disease

Author: Rachel J. Oidtman, Elisa Omodei, Moritz U. G. Kraemer, Carlos A. Castañeda-Orjuela, Erica Cruz-Rivera, Sandra Misnaza-Castrillón, Myriam Patricia Cifuentes, Luz Emilse Rincon, Viviana Cañon, Pedro de Alarcon, Guido España, John H. Huber, Sarah C. Hill, Christopher M. Barker, Michael A. Johansson, Carrie A. Manore, Robert C. Reiner, Jr., Isabel Rodriguez-Barraquer, Amir S. Siraj, Enrique Frias-Martinez, Manuel García-Herranz, and T. Alex Perkins
Subjects: Science
Abstract: Newly emerged pathogens are inherently difficult to forecast, due to many unknowns about their biology early in an epidemic. Here, the authors assess forecasts of a suite of models during the Zika epidemic in Colombia, finding that the models that performed best changed over the course of the epidemic.
Published: 2021
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10. Using digital surveillance tools for near real-time mapping of the risk of infectious disease spread

Author: Sangeeta Bhatia, Britta Lassmann, Emily Cohn, Angel N. Desai, Malwina Carrion, Moritz U. G. Kraemer, Mark Herringer, John Brownstein, Larry Madoff, Anne Cori, and Pierre Nouvellet
Subjects: Computer applications to medicine. Medical informatics, R858-859.7
Abstract: Abstract Data from digital disease surveillance tools such as ProMED and HealthMap can complement the field surveillance during ongoing outbreaks. Our aim was to investigate the use of data collected through ProMED and HealthMap in real-time outbreak analysis. We developed a flexible statistical model to quantify spatial heterogeneity in the risk of spread of an outbreak and to forecast short term incidence trends. The model was applied retrospectively to data collected by ProMED and HealthMap during the 2013–2016 West African Ebola epidemic and for comparison, to WHO data. Using ProMED and HealthMap data, the model was able to robustly quantify the risk of disease spread 1–4 weeks in advance and for countries at risk of case importations, quantify where this risk comes from. Our study highlights that ProMED and HealthMap data could be used in real-time to quantify the spatial heterogeneity in risk of spread of an outbreak.
Published: 2021
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11. Endogenous social distancing and its underappreciated impact on the epidemic curve

Author: Marko Gosak, Moritz U. G. Kraemer, Heinrich H. Nax, Matjaž Perc, and Bary S. R. Pradelski
Subjects: Medicine, Science
Abstract: Abstract Social distancing is an effective strategy to mitigate the impact of infectious diseases. If sick or healthy, or both, predominantly socially distance, the epidemic curve flattens. Contact reductions may occur for different reasons during a pandemic including health-related mobility loss (severity of symptoms), duty of care for a member of a high-risk group, and forced quarantine. Other decisions to reduce contacts are of a more voluntary nature. In particular, sick people reduce contacts consciously to avoid infecting others, and healthy individuals reduce contacts in order to stay healthy. We use game theory to formalize the interaction of voluntary social distancing in a partially infected population. This improves the behavioral micro-foundations of epidemiological models, and predicts differential social distancing rates dependent on health status. The model’s key predictions in terms of comparative statics are derived, which concern changes and interactions between social distancing behaviors of sick and healthy. We fit the relevant parameters for endogenous social distancing to an epidemiological model with evidence from influenza waves to provide a benchmark for an epidemic curve with endogenous social distancing. Our results suggest that spreading similar in peak and case numbers to what partial immobilization of the population produces, yet quicker to pass, could occur endogenously. Going forward, eventual social distancing orders and lockdown policies should be benchmarked against more realistic epidemic models that take endogenous social distancing into account, rather than be driven by static, and therefore unrealistic, estimates for social mixing that intrinsically overestimate spreading.
Published: 2021
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12. Asynchronicity of endemic and emerging mosquito-borne disease outbreaks in the Dominican Republic

Author: Mary E. Petrone, Rebecca Earnest, José Lourenço, Moritz U. G. Kraemer, Robert Paulino-Ramirez, Nathan D. Grubaugh, and Leandro Tapia
Subjects: Science
Abstract: Dengue is endemic in the Dominican Republic, and causes regular outbreaks, whereas Zika and chikungunya are emerging infections in the area. Here, the authors show that outbreaks of the emerging infections could not be predicted by seasonal dengue dynamics.
Published: 2021
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13. Dynamics of conflict during the Ebola outbreak in the Democratic Republic of the Congo 2018–2019

Author: Moritz U. G. Kraemer, David M. Pigott, Sarah C. Hill, Samantha Vanderslott, Robert C. Reiner, Stephanie Stasse, John S. Brownstein, Bernardo Gutierrez, Francis Dennig, Simon I. Hay, G. R. William Wint, Oliver G. Pybus, Marcia C. Castro, Patrick Vinck, Phuong N. Pham, Eric J. Nilles, and Simon Cauchemez
Subjects: Ebola, Conflict, Violence, Democratic Republic of the Congo, Outbreak, Medicine
Abstract: Abstract Background The 2018–2019 Ebola virus disease (EVD) outbreak in North Kivu and Ituri provinces in the Democratic Republic of the Congo (DRC) is the largest ever recorded in the DRC. It has been declared a Public Health Emergency of International Concern. The outbreak emerged in a region of chronic conflict and insecurity, and directed attacks against health care workers may have interfered with disease response activities. Our study characterizes and quantifies the broader conflict dynamics over the course of the outbreak by pairing epidemiological and all available spatial conflict data. Methods We build a set of conflict variables by mapping the spatial locations of all conflict events and their associated deaths in each of the affected health zones in North Kivu and Ituri, eastern DRC, before and during the outbreak. Using these data, we compare patterns of conflict before and during the outbreak in affected health zones and those not affected. We then test whether conflict is correlated with increased EVD transmission at the health zone level. Findings The incidence of conflict events per capita is ~ 600 times more likely in Ituri and North Kivu than for the rest of the DRC. We identified 15 time periods of substantial uninterrupted transmission across 11 health zones and a total of 120 bi-weeks. We do not find significant short-term associations between the bi-week reproduction numbers and the number of conflicts. However, we do find that the incidence of conflict per capita was correlated with the incidence of EVD per capita at the health zone level for the entire outbreak (Pearson’s r = 0.33, 95% CI 0.05–0.57). In the two provinces, the monthly number of conflict events also increased by a factor of 2.7 in Ebola-affected health zones (p value
Published: 2020
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14. Tracking the international spread of SARS-CoV-2 lineages B.1.1.7 and B.1.351/501Y-V2 with grinch [version 2; peer review: 3 approved]

Author: Áine O'Toole, Verity Hill, Oliver G. Pybus, Alexander Watts, Issac I. Bogoch, Kamran Khan, Jane P. Messina, The COVID-19 Genomics UK (COG-UK) consortium, Network for Genomic Surveillance in South Africa (NGS-SA), Brazil-UK CADDE Genomic Network, Houriiyah Tegally, Richard R. Lessells, Jennifer Giandhari, Sureshnee Pillay, Kefentse Arnold Tumedi, Gape Nyepetsi, Malebogo Kebabonye, Maitshwarelo Matsheka, Madisa Mine, Sima Tokajian, Hamad Hassan, Tamara Salloum, Georgi Merhi, Jad Koweyes, Jemma L. Geoghegan, Joep de Ligt, Xiaoyun Ren, Matthew Storey, Nikki E. Freed, Chitra Pattabiraman, Pramada Prasad, Anita S. Desai, Ravi Vasanthapuram, Thomas F. Schulz, Lars Steinbrück, Tanja Stadler, Swiss Viollier Sequencing Consortium, Antonio Parisi, Angelica Bianco, Darío García de Viedma, Sergio Buenestado-Serrano, Vítor Borges, Joana Isidro, Sílvia Duarte, João Paulo Gomes, Neta S. Zuckerman, Michal Mandelboim, Orna Mor, Torsten Seemann, Alicia Arnott, Jenny Draper, Mailie Gall, William Rawlinson, Ira Deveson, Sanmarié Schlebusch, Jamie McMahon, Lex Leong, Chuan Kok Lim, Maria Chironna, Daniela Loconsole, Antonin Bal, Laurence Josset, Edward Holmes, Kirsten St. George, Erica Lasek-Nesselquist, Reina S. Sikkema, Bas Oude Munnink, Marion Koopmans, Mia Brytting, V. Sudha rani, S. Pavani, Teemu Smura, Albert Heim, Satu Kurkela, Massab Umair, Muhammad Salman, Barbara Bartolini, Martina Rueca, Christian Drosten, Thorsten Wolff, Olin Silander, Dirk Eggink, Chantal Reusken, Harry Vennema, Aekyung Park, Christine Carrington, Nikita Sahadeo, Michael Carr, Gabo Gonzalez, SEARCH Alliance San Diego, National Virus Reference Laboratory, SeqCOVID-Spain, Danish Covid-19 Genome Consortium (DCGC), Communicable Diseases Genomic Network (CDGN), Dutch National SARS-CoV-2 surveillance program, Division of Emerging Infectious Diseases (KDCA), Tulio de Oliveira, Nuno Faria, Andrew Rambaut, and Moritz U. G. Kraemer
Subjects: Medicine, Science
Abstract: Late in 2020, two genetically-distinct clusters of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) with mutations of biological concern were reported, one in the United Kingdom and one in South Africa. Using a combination of data from routine surveillance, genomic sequencing and international travel we track the international dispersal of lineages B.1.1.7 and B.1.351 (variant 501Y-V2). We account for potential biases in genomic surveillance efforts by including passenger volumes from location of where the lineage was first reported, London and South Africa respectively. Using the software tool grinch (global report investigating novel coronavirus haplotypes), we track the international spread of lineages of concern with automated daily reports, Further, we have built a custom tracking website (cov-lineages.org/global_report.html) which hosts this daily report and will continue to include novel SARS-CoV-2 lineages of concern as they are detected.
Published: 2021
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15. Tracking the international spread of SARS-CoV-2 lineages B.1.1.7 and B.1.351/501Y-V2 [version 1; peer review: 2 approved]

Author: Áine O'Toole, Verity Hill, Oliver G. Pybus, Alexander Watts, Issac I. Bogoch, Kamran Khan, Jane P. Messina, The COVID-19 Genomics UK (COG-UK) consortium, Network for Genomic Surveillance in South Africa (NGS-SA), Brazil-UK CADDE Genomic Network, Houriiyah Tegally, Richard R. Lessells, Jennifer Giandhari, Sureshnee Pillay, Kefentse Arnold Tumedi, Gape Nyepetsi, Malebogo Kebabonye, Maitshwarelo Matsheka, Madisa Mine, Sima Tokajian, Hamad Hassan, Tamara Salloum, Georgi Merhi, Jad Koweyes, Jemma L. Geoghegan, Joep de Ligt, Xiaoyun Ren, Matthew Storey, Nikki E. Freed, Chitra Pattabiraman, Pramada Prasad, Anita S. Desai, Ravi Vasanthapuram, Thomas F. Schulz, Lars Steinbrück, Tanja Stadler, Swiss Viollier Sequencing Consortium, Antonio Parisi, Angelica Bianco, Darío García de Viedma, Sergio Buenestado-Serrano, Vítor Borges, Joana Isidro, Sílvia Duarte, João Paulo Gomes, Neta S. Zuckerman, Michal Mandelboim, Orna Mor, Torsten Seemann, Alicia Arnott, Jenny Draper, Mailie Gall, William Rawlinson, Ira Deveson, Sanmarié Schlebusch, Jamie McMahon, Lex Leong, Chuan Kok Lim, Maria Chironna, Daniela Loconsole, Antonin Bal, Laurence Josset, Edward Holmes, Kirsten St. George, Erica Lasek-Nesselquist, Reina S. Sikkema, Bas Oude Munnink, Marion Koopmans, Mia Brytting, V. Sudha rani, S. Pavani, Teemu Smura, Albert Heim, Satu Kurkela, Massab Umair, Muhammad Salman, Barbara Bartolini, Martina Rueca, Christian Drosten, Thorsten Wolff, Olin Silander, Dirk Eggink, Chantal Reusken, Harry Vennema, Aekyung Park, Christine Carrington, Nikita Sahadeo, Michael Carr, Gabo Gonzalez, SEARCH Alliance San Diego, National Virus Reference Laboratory, SeqCOVID-Spain, Danish Covid-19 Genome Consortium (DCGC), Communicable Diseases Genomic Network (CDGN), Dutch National SARS-CoV-2 surveillance program, Division of Emerging Infectious Diseases (KDCA), Tulio de Oliveira, Nuno Faria, Andrew Rambaut, and Moritz U. G. Kraemer
Subjects: Medicine, Science
Abstract: Late in 2020, two genetically-distinct clusters of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) with mutations of biological concern were reported, one in the United Kingdom and one in South Africa. Using a combination of data from routine surveillance, genomic sequencing and international travel we track the international dispersal of lineages B.1.1.7 and B.1.351 (variant 501Y-V2). We account for potential biases in genomic surveillance efforts by including passenger volumes from location of where the lineage was first reported, London and South Africa respectively. Using the software tool grinch (global report investigating novel coronavirus haplotypes), we track the international spread of lineages of concern with automated daily reports, Further, we have built a custom tracking website (cov-lineages.org/global_report.html) which hosts this daily report and will continue to include novel SARS-CoV-2 lineages of concern as they are detected.
Published: 2021
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16. A dynamic neural network model for predicting risk of Zika in real time

Author: Mahmood Akhtar, Moritz U. G. Kraemer, and Lauren M. Gardner
Subjects: Zika, Epidemic risk prediction, Dynamic neural network, Medicine
Abstract: Abstract Background In 2015, the Zika virus spread from Brazil throughout the Americas, posing an unprecedented challenge to the public health community. During the epidemic, international public health officials lacked reliable predictions of the outbreak’s expected geographic scale and prevalence of cases, and were therefore unable to plan and allocate surveillance resources in a timely and effective manner. Methods In this work, we present a dynamic neural network model to predict the geographic spread of outbreaks in real time. The modeling framework is flexible in three main dimensions (i) selection of the chosen risk indicator, i.e., case counts or incidence rate; (ii) risk classification scheme, which defines the high-risk group based on a relative or absolute threshold; and (iii) prediction forecast window (1 up to 12 weeks). The proposed model can be applied dynamically throughout the course of an outbreak to identify the regions expected to be at greatest risk in the future. Results The model is applied to the recent Zika epidemic in the Americas at a weekly temporal resolution and country spatial resolution, using epidemiological data, passenger air travel volumes, and vector habitat suitability, socioeconomic, and population data for all affected countries and territories in the Americas. The model performance is quantitatively evaluated based on the predictive accuracy of the model. We show that the model can accurately predict the geographic expansion of Zika in the Americas with the overall average accuracy remaining above 85% even for prediction windows of up to 12 weeks. Conclusions Sensitivity analysis illustrated the model performance to be robust across a range of features. Critically, the model performed consistently well at various stages throughout the course of the outbreak, indicating its potential value at any time during an epidemic. The predictive capability was superior for shorter forecast windows and geographically isolated locations that are predominantly connected via air travel. The highly flexible nature of the proposed modeling framework enables policy makers to develop and plan vector control programs and case surveillance strategies which can be tailored to a range of objectives and resource constraints.
Published: 2019
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17. Author Correction: Malaria elimination on Hainan Island despite climate change

Author: Huaiyu Tian, Naizhe Li, Yapin Li, Moritz U. G. Kraemer, Hua Tan, Yonghong Liu, Yidan Li, Ben Wang, Peiyi Wu, Bernard Cazelles, José Lourenço, Dongqi Gao, Dingwei Sun, Wenjing Song, Yuchun Li, Oliver G. Pybus, Guangze Wang, and Christopher Dye
Subjects: Medicine
Published: 2022
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18. Projecting the end of the Zika virus epidemic in Latin America: a modelling analysis

Author: Kathleen M. O’Reilly, Rachel Lowe, W. John Edmunds, Philippe Mayaud, Adam Kucharski, Rosalind M. Eggo, Sebastian Funk, Deepit Bhatia, Kamran Khan, Moritz U. G. Kraemer, Annelies Wilder-Smith, Laura C. Rodrigues, Patricia Brasil, Eduardo Massad, Thomas Jaenisch, Simon Cauchemez, Oliver J. Brady, and Laith Yakob
Subjects: Zika virus, Epidemic, Mathematical modelling, Latin America and the Caribbean, Connectivity, Medicine
Abstract: Abstract Background Zika virus (ZIKV) emerged in Latin America and the Caribbean (LAC) region in 2013, with serious implications for population health in the region. In 2016, the World Health Organization declared the ZIKV outbreak a Public Health Emergency of International Concern following a cluster of associated neurological disorders and neonatal malformations. In 2017, Zika cases declined, but future incidence in LAC remains uncertain due to gaps in our understanding, considerable variation in surveillance and the lack of a comprehensive collation of data from affected countries. Methods Our analysis combines information on confirmed and suspected Zika cases across LAC countries and a spatio-temporal dynamic transmission model for ZIKV infection to determine key transmission parameters and projected incidence in 90 major cities within 35 countries. Seasonality was determined by spatio-temporal estimates of Aedes aegypti vectorial capacity. We used country and state-level data from 2015 to mid-2017 to infer key model parameters, country-specific disease reporting rates, and the 2018 projected incidence. A 10-fold cross-validation approach was used to validate parameter estimates to out-of-sample epidemic trajectories. Results There was limited transmission in 2015, but in 2016 and 2017 there was sufficient opportunity for wide-spread ZIKV transmission in most cities, resulting in the depletion of susceptible individuals. We predict that the highest number of cases in 2018 would present within some Brazilian States (Sao Paulo and Rio de Janeiro), Colombia and French Guiana, but the estimated number of cases were no more than a few hundred. Model estimates of the timing of the peak in incidence were correlated (p
Published: 2018
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19. Inferences about spatiotemporal variation in dengue virus transmission are sensitive to assumptions about human mobility: a case study using geolocated tweets from Lahore, Pakistan

Author: Moritz U. G. Kraemer, D. Bisanzio, R. C. Reiner, R. Zakar, J. B. Hawkins, C. C. Freifeld, D. L. Smith, S. I. Hay, J. S. Brownstein, and T. Alex Perkins
Subjects: Big data, Disease dynamics, Geo-located tweets, Gravity model, Human mobility, Radiation model, Computer applications to medicine. Medical informatics, R858-859.7
Abstract: Abstract Billions of users of mobile phones, social media platforms, and other technologies generate an increasingly large volume of data that has the potential to be leveraged towards solving public health challenges. These and other big data resources tend to be most successful in epidemiological applications when utilized within an appropriate conceptual framework. Here, we demonstrate the importance of assumptions about host mobility in a framework for dynamic modeling of infectious disease spread among districts within a large urban area. Our analysis focused on spatial and temporal variation in the transmission of dengue virus (DENV) during a series of large seasonal epidemics in Lahore, Pakistan during 2011–2014. Similar to many directly transmitted diseases, DENV transmission occurs primarily where people spend time during daytime hours, given that DENV is transmitted by a day-biting mosquito. We inferred spatiotemporal variation in DENV transmission under five different assumptions about mobility patterns among ten districts of Lahore: no movement among districts, movement following patterns of geo-located tweets, movement proportional to district population size, and movement following the commonly used gravity and radiation models. Overall, we found that inferences about spatiotemporal variation in DENV transmission were highly sensitive to this range of assumptions about intra-urban human mobility patterns, although the three assumptions that allowed for a modest degree of intra-urban mobility all performed similarly in key respects. Differing inferences about transmission patterns based on our analysis are significant from an epidemiological perspective, as they have different implications for where control efforts should be targeted and whether conditions for transmission became more or less favorable over time.
Published: 2018
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20. Air Passenger Travel and International Surveillance Data Predict Spatiotemporal Variation in Measles Importations to the United States

Author: Marya L. Poterek, Moritz U. G. Kraemer, Alexander Watts, Kamran Khan, and T. Alex Perkins
Subjects: air travel, importation, global health, measles, Medicine
Abstract: Measles incidence in the United States has grown dramatically, as vaccination rates are declining and transmission internationally is on the rise. Because imported cases are necessary drivers of outbreaks in non-endemic settings, predicting measles outbreaks in the US depends on predicting imported cases. To assess the predictability of imported measles cases, we performed a regression of imported measles cases in the US against an inflow variable that combines air travel data with international measles surveillance data. To understand the contribution of each data type to these predictions, we repeated the regression analysis with alternative versions of the inflow variable that replaced each data type with averaged values and with versions of the inflow variable that used modeled inputs. We assessed the performance of these regression models using correlation, coverage probability, and area under the curve statistics, including with resampling and cross-validation. Our regression model had good predictive ability with respect to the presence or absence of imported cases in a given state in a given year (area under the curve of the receiver operating characteristic curve (AUC) = 0.78) and the magnitude of imported cases (Pearson correlation = 0.84). By comparing alternative versions of the inflow variable averaging over different inputs, we found that both air travel data and international surveillance data contribute to the model’s ability to predict numbers of imported cases and individually contribute to its ability to predict the presence or absence of imported cases. Predicted sources of imported measles cases varied considerably across years and US states, depending on which countries had high measles activity in a given year. Our results emphasize the importance of the relationship between global connectedness and the spread of measles. This study provides a framework for predicting and understanding imported case dynamics that could inform future studies and outbreak prevention efforts.
Published: 2021
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21. Data curation during a pandemic and lessons learned from COVID-19.

Author: Moritz U. G. Kraemer, Samuel V. Scarpino, Vukosi Marivate, Bernardo Gutierrez, Bo Xu, Graham Lee, Jared B. Hawkins, Caitlin M. Rivers, David M. Pigott, Rebecca Katz, and John S. Brownstein
Published: 2021
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22. Establishment and cryptic transmission of Zika virus in Brazil and the Americas.

Author: Nuno R. Faria, Joshua Quick, I. M. Claro, Julien Thézé, J. G. de Jesus, Marta Giovanetti, Moritz U. G. Kraemer, S. C. Hill, Allison Black, A. C. Da Costa, L. C. Franco, S. P. Silva, Chieh-Hsi Wu, Jayna Raghwani, Simon Cauchemez, Louis du Plessis, M. P. Verotti, W. K. de Oliveira, E. H. Carmo, G. E. Coelho, A. C. F. S. Santelli, L. C. Vinhal, C. M. Henriques, J. T. Simpson, Matthew Loose, Kristian G. Andersen, N. D. Grubaugh, S. Somasekar, C. Y. Chiu, J. E. Muñoz-Medina, C. R. Gonzalez-Bonilla, Carolina Fócil Arias, L. L. Lewis-Ximenez, S. A. Baylis, A. O. Chieppe, S. F. Aguiar, C. A. Fernandes, P. S. Lemos, B. L. S. Nascimento, H. A. O. Monteiro, I. C. Siqueira, M. G. de Queiroz, T. R. de Souza, J. F. Bezerra, M. R. Lemos, G. F. Pereira, D. Loudal, L. C. Moura, R. Dhalia, R. F. França, T. Magalhães, E. T. Marques Jr, Thomas Jaenisch, Gabriel da Luz Wallau, M. C. de Lima, V. Nascimento, E. M. de Cerqueira, M. M. de Lima, D. L. Mascarenhas, J. P. Moura Neto, A. S. Levin, T. R. Tozetto-Mendoza, S. N. Fonseca, M. C. Mendes-Correa, F. P. Milagres, Aluisio Segurado, Edward C. Holmes, Andrew Rambaut, Trevor Bedford, M. R. T. Nunes, Ester C. Sabino, Luis Carlos Alcantara, Nicholas J. Loman, and Oliver G. Pybus
Published: 2017
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23. Cryptic transmission and re-emergence of Cosmopolitan genotype of Dengue Virus Serotype 2 within Ho Chi Minh City and Southern Vietnam

Author: Vi T. Tran, Rhys P. D. Inward, Bernardo Gutierrez, Nguyet M Nguyen, Isabelle Rajendiran, Phong Nguyen Thanh, Tam Cao Thi, Kien T.H. Duong, Moritz U. G. Kraemer, and Sophie Yacoub
Abstract: BackgroundDengue virus (DENV) is a major global public health threat and causes substantial morbidity and mortality in hyperendemic countries like Vietnam with its largest city, Ho Chi Minh City (HCMC), recently experiencing its largest DENV outbreak in over a decade. Despite this, there is currently a lack of data on circulating DENV serotypes/genotypes and monitoring of transmission dynamics within HCMC, which presents a challenge for the design and implementation of effective DENV mitigation strategies.MethodsWe generated 45 DENV envelope (E) gene sequences from human plasma samples collected in southern Vietnam between 2017 and 2022. We applied phylogenetic methods to infer the probable route of virus introductions into HCMC and its surrounding areas, as well as their approximate timing, using additional sequences from Southern and South-east Asia.FindingsWe found evidence of the co-circulation of multiple DENV serotypes/genotypes and the re-emergence of the DENV-2 Cosmopolitan genotype in southern Vietnam. Furthermore, we detected at least three independent seeding events of the Cosmopolitan genotype into Vietnam, the earliest of which is estimated to have occurred two years before the earliest sampling date, providing evidence of at least two seasons of cryptic transmission.InterpretationOur findings emphasize the urgent need for comprehensive DENV surveillance in HCMC and Vietnam to guide appropriate and effective public health responses and improve understanding of recent outbreak dynamics in Vietnam and neighbouring countries. Such efforts may also help predict epidemic dynamics of DENV in future seasons.FundingThis work was supported by the Wellcome Trust [106680] and Wellcome Trust [226052/Z/22/Z]Evidence before this studyDengue virus (DENV) is a significant threat to global health causing high levels of morbidity and economic damage. With the limited surveillance infrastructure for DENV, particularly during the COVID-19 pandemic, little is currently known about the transmission dynamics in Ho Chi Minh City (HCMC) and Vietnam presenting a challenge for the design and implementation of DENV mitigation strategies.Added value of this studyGenomic epidemiology is a powerful approach to gain insights into the spatio-temporal dynamics of viruses and to detect new viral variants. Our analyses shows the existence of multiple co-circulating DENV-2 genotypes in southern Vietnam with multiple distinct and continued introductions of the Cosmopolitan genotype into HCMC over multiple years.Implications of all the available evidenceFindings from this study will assist local and regional dengue surveillance and control programs as well as adding to our understanding on DENV genomic epidemiology and transmission dynamics. Critically, current dengue screening and surveillance methodology should be modified to enable detection of these novel lineages. The emergence of these genotypes and their impact on dengue evolution need to be explored on a larger scale. These results will also allow vital missing genomic data to be incorporated into models used for importation dynamics analysis. We plan to integrate these findings into a DENV forecasting tool being developed as part of Wellcome funded multidisciplinary project - DART (Dengue Advanced Readiness Tools), which aims to build an integrated digital system for dengue outbreak prediction and monitoring.
Published: 2023

24. A systematic review of the data, methods and environmental covariates used to mapAedes-borne arbovirus transmission risk

Author: Ah-Young Lim, Yalda Jafari, Jamie M. Caldwell, Hannah E. Clapham, Katy A. M. Gaythorpe, Laith Hussain-Alkhateeb, Michael A. Johansson, Moritz U. G. Kraemer, Richard J. Maude, Clare P. McCormack, Jane P. Messina, Erin A. Mordecai, Ingrid B. Rabe, Robert C. Reiner, Sadie J. Ryan, Henrik Salje, Jan C. Semenza, Diana P. Rojas, and Oliver J. Brady
Abstract: BackgroundAedes (Stegomyia)-borne diseases are an expanding global threat, but gaps in surveillance make comprehensive and comparable risk assessments challenging. Geostatistical models combine data from multiple locations and use links with environmental and socioeconomic factors to make predictive risk maps. Here we systematically review past approaches to map risk for differentAedes-borne arboviruses from local to global scales, identifying differences and similarities in the data types, covariates, and modelling approaches used.MethodsWe searched on-line databases for predictive risk mapping studies for dengue, Zika, chikungunya, and yellow fever with no geographical or date restrictions. We included studies that needed to parameterise or fit their model to real-world epidemiological data and make predictions to new spatial locations of some measure of population-level risk of viral transmission (e.g. incidence, occurrence, suitability, etc).ResultsWe found a growing number of arbovirus risk mapping studies across all endemic regions and arboviral diseases, with a total of 183 papers published 2002-2022 with the largest increases shortly following major epidemics. Three dominant use cases emerged: i) global maps to identify limits of transmission, estimate burden and assess impacts of future global change, ii) regional models used to predict the spread of major epidemics between countries and iii) national and sub-national models that use local datasets to better understand transmission dynamics to improve outbreak detection and response. Temperature and rainfall were the most popular choice of covariates (included in 50% and 40% of studies respectively) but variables such as human mobility are increasingly being included. Surprisingly, few studies (22%, 33/148) robustly tested combinations of covariates from different domains (e.g. climatic, sociodemographic, ecological, etc) and only 48% of studies assessed predictive performance via out-of-sample validation procedures.ConclusionsHere we show that approaches to map risk for different arboviruses have diversified in response to changing use cases, epidemiology and data availability. We outline specific recommendations for future studies regarding aims and data choice, covariate selection, model formulation and evaluation.Author SummaryAedes-borne arboviruses such as dengue, Zika, chikungunya, and yellow fever pose a growing global threat. It is crucial to map their risk to target interventions and control their spread. A review of 183 studies found that risk mapping methods have evolved over time to respond to changing epidemiology and data availability. Initially, mapping risk involved using data from multiple areas and satellite imagery to develop models predicting transmission risk on a global or continental scale. Following Zika and chikungunya epidemics, mechanistic models based on national-level incidence data have been utilised to track the spread of epidemics across countries. The use of case-based surveillance systems has enabled more precise and detailed predictions at sub-national levels. Of the studies reviewed, half included temperature and rainfall as covariates, and human mobility was increasingly accounted for in arbovirus risk mapping. However, only 33 of the 148 studies robustly selected the variables included in their predictions, and only half of the studies assessed their accuracy against new data. The review suggests that future risk mapping studies should consider the purpose of the map, data quality, and methodological innovations to improve accuracy of risk maps to ensure they are useful for informing control ofAedes-borne arboviruses.
Published: 2023

25. Shifts in global mobility dictate the synchrony of SARS-CoV-2 epidemic waves

Author: Houriiyah Tegally, Kamran Khan, Carmen Huber, Tulio de Oliveira, and Moritz U G Kraemer
Subjects: Travel, Air Travel, SARS-CoV-2, Communicable Disease Control, Humans, COVID-19, General Medicine, Pandemics
Abstract: Background Human mobility changed in unprecedented ways during the SARS-CoV-2 pandemic. In March and April 2020, when lockdowns and large travel restrictions began in most countries, global air-travel almost entirely halted (92% decrease in commercial global air travel in the months between February and April 2020). Initial recovery in global air travel started around July 2020 and subsequently nearly tripled between May and July 2021. Here, we aim to establish a preliminary link between global mobility patterns and the synchrony of SARS-CoV-2 epidemic waves across the world. Methods We compare epidemic peaks and human global mobility in two time periods: November 2020 to February 2021 (when just over 70 million passengers travelled) and November 2021 to February 2022 (when more than 200 million passengers travelled). We calculate the time interval during which continental epidemic peaks occurred for both of these time periods, and we calculate the pairwise correlations of epidemic waves between all pairs of countries for the same time periods. Results We find that as air travel increases at the end of 2021, epidemic peaks around the world are more synchronous with one another, both globally and regionally. Continental epidemic peaks occur globally within a 20 day interval at the end of 2021 compared with 73 days at the end of 2020, and epidemic waves globally are more correlated with one another at the end of 2021. Conclusions This suggests that the rebound in human mobility dictates the synchrony of global and regional epidemic waves. In line with theoretical work, we show that in a more connected world, epidemic dynamics are more synchronized.
Published: 2022

26. The Huanan Seafood Wholesale Market in Wuhan was the early epicenter of the COVID-19 pandemic

Author: Michael Worobey, Joshua I. Levy, Lorena Malpica Serrano, Alexander Crits-Christoph, Jonathan E. Pekar, Stephen A. Goldstein, Angela L. Rasmussen, Moritz U. G. Kraemer, Chris Newman, Marion P. G. Koopmans, Marc A. Suchard, Joel O. Wertheim, Philippe Lemey, David L. Robertson, Robert F. Garry, Edward C. Holmes, Andrew Rambaut, Kristian G. Andersen, and Virology
Subjects: China, Science & Technology, Multidisciplinary, TRANSMISSION, SARS-CoV-2, General Science & Technology, Prevention, COVID-19, Viral Zoonoses, Multidisciplinary Sciences, Vaccine Related, Emerging Infectious Diseases, Good Health and Well Being, Seafood, Biodefense, Science & Technology - Other Topics, Animals, Humans, Pandemics, Lung
Abstract: Understanding how severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) emerged in 2019 is critical to preventing future zoonotic outbreaks before they become the next pandemic. The Huanan Seafood Wholesale Market in Wuhan, China, was identified as a likely source of cases in early reports, but later this conclusion became controversial. We show here that the earliest known COVID-19 cases from December 2019, including those without reported direct links, were geographically centered on this market. We report that live SARS-CoV-2-susceptible mammals were sold at the market in late 2019 and that within the market, SARS-CoV-2-positive environmental samples were spatially associated with vendors selling live mammals. Although there is insufficient evidence to define upstream events, and exact circumstances remain obscure, our analyses indicate that the emergence of SARS-CoV-2 occurred through the live wildlife trade in China and show that the Huanan market was the epicenter of the COVID-19 pandemic. ispartof: SCIENCE vol:377 issue:6609 pages:951-959 ispartof: location:United States status: published
Published: 2022

27. Genomics and epidemiology of the P.1 SARS-CoV-2 lineage in Manaus, Brazil

Author: Esmenia C. Rocha, Vitor H. Nascimento, Pedro S. Peixoto, Thomas A. Mellan, Lucas A M Franco, Henrique Hoeltgebaum, Michaela A. C. Vollmer, Oliver Ratmann, Leonardo José Tadeu de Araújo, Nuno R. Faria, Philippe Lemey, Raphael Sonabend, Myuki A E Crispim, Nelson Gaburo, Charles Whittaker, Joice do P. Silva, Ruben J.G. Hulswit, Ricardo P Schnekenberg, Cecilia da C. Camilo, Mariana C. Pinho, Darlan da Silva Candido, Neil M. Ferguson, Helem M. dos Santos, Ester Cerdeira Sabino, Patrick G T Walker, Hannah M. Schlüter, Carlos A. Prete, Thais M. Coletti, Erika R. Manuli, Oliver G. Pybus, Samir Bhatt, Alessandro C. S. Ferreira, Mariana S. Ramundo, Danielle A G Zauli, Aline B. de Lima, Jaqueline Goes de Jesus, Iwona Hawryluk, Frederico S V Malta, Marc A. Suchard, Leandro Marques de Souza, Seth Flaxman, Moritz U. G. Kraemer, James A. Hay, Valentina S. Del Caro, Rosinaldo M. F. Filho, Axel Gandy, Pamela S Andrade, Andrew Rambaut, Ingra Morales Claro, Ana L. P. dos Santos, Christopher Dye, José Luiz Proença-Módena, Swapnil Mishra, Daniel J Laydon, William Marciel de Souza, Renato Santana Aguiar, Xenia Miscouridou, Camila A. M. Silva, Maria S. Vidal, John T. McCrone, Maria Perpétuo Socorro Sampaio Carvalho, Nicholas J. Loman, Giulia M. Ferreira, Bruce Walker Nelson, Chieh-Hsi Wu, Thomas A. Bowden, Melodie Monod, Helen Coupland, Rafael Henrique Moraes Pereira, Flavia C. S. Sales, Sergei L Kosakovsky Pond, Nelson Abrahim Fraiji, Bill & Melinda Gates Foundation, Wellcome Trust, and Medical Research Council (MRC)
Subjects: DYNAMICS, 0301 basic medicine, MATEMÁTICA APLICADA, Lineage (genetic), General Science & Technology, LOCAL TRANSMISSION, Genomics, Genome, Viral, Communicable Diseases, Emerging, Genome, DNA sequencing, 03 medical and health sciences, 0302 clinical medicine, Molecular evolution, Humans, 030212 general & internal medicine, Molecular clock, Molecular Epidemiology, SITES, Science & Technology, RECEPTOR-BINDING DOMAIN, Multidisciplinary, Molecular epidemiology, biology, SARS-CoV-2, MOLECULAR CLOCK, COVID-19, Models, Theoretical, Viral Load, PERFORMANCE, biology.organism_classification, Virology, 3. Good health, Multidisciplinary Sciences, 030104 developmental biology, Epidemiological Monitoring, Mutation, Spike Glycoprotein, Coronavirus, Science & Technology - Other Topics, Angiotensin-Converting Enzyme 2, RESPIRATORY-SYNDROME-CORONAVIRUS, Brazil, Betacoronavirus, Protein Binding
Abstract: Unmitigated spread in Brazil Despite an extensive network of primary care availability, Brazil has suffered profoundly during the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic. Using daily data from state health offices, Castro et al. analyzed the pattern of spread of COVID-19 cases and deaths in the country from February to October 2020. Clusters of deaths before cases became apparent indicated unmitigated spread. SARS-CoV-2 circulated undetected in Brazil for more than a month as it spread north from Sã o Paulo. In Manaus, transmission reached unprecedented levels after a momentary respite in mid-2020. Faria et al. tracked the evolution of a new, more aggressive lineage called P.1, which has 17 mutations, including three (K417T, E484K, and N501Y) in the spike protein. After a period of accelerated evolution, this variant emerged in Brazil during November 2020. Coupled with the emergence of P.1, disease spread was accelerated by stark local inequalities and political upheaval, which compromised a prompt federal response. Science , abh1558 and abh2644, this issue p. 821 and p. 815
Published: 2021

28. Forecasting hospital-level COVID-19 admissions using real-time mobility data

Author: Brennan Klein, Ana C. Zenteno, Daisha Joseph, Mohammadmehdi Zahedi, Michael Hu, Martin S. Copenhaver, Moritz U. G. Kraemer, Matteo Chinazzi, Michael Klompas, Alessandro Vespignani, Samuel V. Scarpino, and Hojjat Salmasian
Abstract: Background For each of the COVID-19 pandemic waves, hospitals have had to plan for deploying surge capacity and resources to manage large but transient increases in COVID-19 admissions. While a lot of effort has gone into predicting regional trends in COVID-19 cases and hospitalizations, there are far fewer successful tools for creating accurate hospital-level forecasts. Methods Large-scale, anonymized mobile phone data has been shown to correlate with regional case counts during the first two waves of the pandemic (spring 2020, and fall/winter 2021). Building off this success, we developed a multi-step, recursive forecasting model to predict individual hospital admissions; this model incorporates the following data: (i) hospital-level COVID-19 admissions, (ii) statewide test positivity data, and (iii) aggregate measures of large-scale human mobility, contact patterns, and commuting volume. Results Incorporating large-scale, aggregate mobility data as exogenous variables in prediction models allows us to make hospital-specific COVID-19 admission forecasts 21 days ahead. We show this through highly accurate predictions of hospital admissions for five hospitals in Massachusetts during the first year of the COVID-19 pandemic. Conclusions The high predictive capability of the model was achieved by combining anonymized, aggregated mobile device data about users’ contact patterns, commuting volume, and mobility range with COVID hospitalizations and test-positivity data. Mobility-informed forecasting models can increase the lead-time of accurate predictions for individual hospitals, giving managers valuable time to strategize how best to allocate resources to manage forthcoming surges.
Published: 2022

29. Using digital surveillance tools for near real-time mapping of the risk of infectious disease spread

Author: Lawrence C. Madoff, Mark Herringer, Malwina Carrion, Anne Cori, John S. Brownstein, Sangeeta N. Bhatia, Emily Cohn, Britta Lassmann, Pierre Nouvellet, Moritz U. G. Kraemer, Angel N. Desai, United States Agency for International Development (USAID), Medical Research Council (MRC), and International Society for Infectious Diseases
Subjects: 0301 basic medicine, Statistical methods, Computer applications to medicine. Medical informatics, R858-859.7, Medicine (miscellaneous), Health Informatics, Article, Vaccine Related, 03 medical and health sciences, 0302 clinical medicine, Health Information Management, 2.5 Research design and methodologies (aetiology), Biodefense, Incidence trends, 030212 general & internal medicine, Aetiology, Disease surveillance, Science & Technology, Prevention, Outbreak, Computer Science Applications, West african, 030104 developmental biology, Geography, Health Care Sciences & Services, Infectious Diseases, Emerging Infectious Diseases, Good Health and Well Being, Infectious disease (medical specialty), Infectious diseases, Infection, Cartography, Life Sciences & Biomedicine, Medical Informatics, 2.4 Surveillance and distribution
Abstract: Data from digital disease surveillance tools such as ProMED and HealthMap can complement the field surveillance during ongoing outbreaks. Our aim was to investigate the use of data collected through ProMED and HealthMap in real-time outbreak analysis. We developed a flexible statistical model to quantify spatial heterogeneity in the risk of spread of an outbreak and to forecast short term incidence trends. The model was applied retrospectively to data collected by ProMED and HealthMap during the 2013–2016 West African Ebola epidemic and for comparison, to WHO data. Using ProMED and HealthMap data, the model was able to robustly quantify the risk of disease spread 1–4 weeks in advance and for countries at risk of case importations, quantify where this risk comes from. Our study highlights that ProMED and HealthMap data could be used in real-time to quantify the spatial heterogeneity in risk of spread of an outbreak.
Published: 2021

30. Transmission of SARS-CoV-2 before and after symptom onset: impact of nonpharmaceutical interventions in China

Author: William P. Hanage, Moritz U. G. Kraemer, Hsiao-Han Chang, Mary Bushman, and Colin J. Worby
Subjects: Nonpharmaceutical interventions, 0301 basic medicine, 2019-20 coronavirus outbreak, China, medicine.medical_specialty, Databases, Factual, Population level, Epidemiology, Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), Psychological intervention, law.invention, 03 medical and health sciences, 0302 clinical medicine, law, Humans, Presymptomatic transmission, Medicine, 030212 general & internal medicine, Symptom onset, Models, Statistical, SARS-CoV-2, business.industry, Incidence, COVID-19, 030104 developmental biology, Transmission (mechanics), Quarantine, Emergency medicine, Contact Tracing, business, Contact tracing, Serial interval
Abstract: Nonpharmaceutical interventions, such as contact tracing and quarantine, have been the primary means of controlling the spread of SARS-CoV-2; however, it remains uncertain which interventions are most effective at reducing transmission at the population level. Using serial interval data from before and after the rollout of nonpharmaceutical interventions in China, we estimate that the relative frequency of presymptomatic transmission increased from 34% before the rollout to 71% afterward. The shift toward earlier transmission indicates a disproportionate reduction in transmission post-symptom onset. We estimate that, following the rollout of nonpharmaceutical interventions, transmission post-symptom onset was reduced by 82% whereas presymptomatic transmission decreased by only 16%. The observation that only one-third of transmission was presymptomatic at baseline, combined with the finding that NPIs reduced presymptomatic transmission by less than 20%, suggests that the overall impact of NPIs was driven in large part by reductions in transmission following symptom onset. This implies that interventions which limit opportunities for transmission in the later stages of infection, such as contact tracing and isolation, are particularly important for control of SARS-CoV-2. Interventions which specifically reduce opportunities for presymptomatic transmission, such as quarantine of asymptomatic contacts, are likely to have smaller, but non-negligible, effects on overall transmission. Supplementary Information The online version contains supplementary material available at 10.1007/s10654-021-00746-4.
Published: 2021

31. Endogenous social distancing and its underappreciated impact on the epidemic curve

Author: Matjaž Perc, Heinrich H. Nax, Marko Gosak, Moritz U. G. Kraemer, Bary S. R. Pradelski, University of Zurich, and Nax, Heinrich H
Subjects: 0301 basic medicine, Comparative statics, Science, Physical Distancing, Population, Article, 03 medical and health sciences, 0302 clinical medicine, Order (exchange), Influenza, Human, Pandemic, Humans, 030212 general & internal medicine, Statistical physics, thermodynamics and nonlinear dynamics, Epidemics, education, Pandemics, 10095 Institute of Sociology, 1000 Multidisciplinary, education.field_of_study, Multidisciplinary, SARS-CoV-2, Social distance, Computational science, COVID-19, 030104 developmental biology, Quarantine, Duty of care, Medicine, Social mixing, 150 Psychology, Psychology, Game theory, Social psychology
Abstract: Social distancing is an effective strategy to mitigate the impact of infectious diseases. If sick or healthy, or both, predominantly socially distance, the epidemic curve flattens. Contact reductions may occur for different reasons during a pandemic including health-related mobility loss (severity of symptoms), duty of care for a member of a high-risk group, and forced quarantine. Other decisions to reduce contacts are of a more voluntary nature. In particular, sick people reduce contacts consciously to avoid infecting others, and healthy individuals reduce contacts in order to stay healthy. We use game theory to formalize the interaction of voluntary social distancing in a partially infected population. This improves the behavioral micro-foundations of epidemiological models, and predicts differential social distancing rates dependent on health status. The model’s key predictions in terms of comparative statics are derived, which concern changes and interactions between social distancing behaviors of sick and healthy. We fit the relevant parameters for endogenous social distancing to an epidemiological model with evidence from influenza waves to provide a benchmark for an epidemic curve with endogenous social distancing. Our results suggest that spreading similar in peak and case numbers to what partial immobilization of the population produces, yet quicker to pass, could occur endogenously. Going forward, eventual social distancing orders and lockdown policies should be benchmarked against more realistic epidemic models that take endogenous social distancing into account, rather than be driven by static, and therefore unrealistic, estimates for social mixing that intrinsically overestimate spreading., Scientific Reports, 11 (1), ISSN:2045-2322
Published: 2021

32. Gaps in mobility data and implications for modelling epidemic spread: A scoping review and simulation study

Author: Jack Wardle, Sangeeta Bhatia, Moritz U G Kraemer, Pierre Nouvellet, and Anne Cori
Subjects: Infectious Diseases, Epidemiology, Virology, Public Health, Environmental and Occupational Health, Parasitology, Microbiology
Abstract: Reliable estimates of human mobility are important for understanding the spatial spread of infectious diseases and the effective targeting of control measures. However, when modelling infectious disease dynamics, data on human mobility at an appropriate temporal or spatial resolution are not always available, leading to the common use of model-derived mobility proxies. In this study we reviewed the different data sources and mobility models that have been used to characterise human movement in Africa. We then conducted a simulation study to better understand the implications of using human mobility proxies when predicting the spatial spread and dynamics of infectious diseases. We found major gaps in the availability of empirical measures of human mobility in Africa, leading to mobility proxies being used in place of data. Empirical data on subnational mobility were only available for 17/54 countries, and, in most instances, these data characterised long-term movement patterns, which were unsuitable for modelling the spread of pathogens with short generation times (time between infection of a case and their infector). Results from our simulation study demonstrated that using mobility proxies can have a substantial impact on the predicted epidemic dynamics, with complex and non-intuitive biases. In particular, the predicted times and order of epidemic invasion, and the time of epidemic peak in different locations can be underestimated or overestimated, depending on the types of proxies used and the country of interest. Our work underscores the need for regularly updated empirical measures of population movement within and between countries to aid the prevention and control of infectious disease outbreaks. At the same time, there is a need to establish an evidence base to help understand which types of mobility data are most appropriate for describing the spread of emerging infectious diseases in different settings.
Published: 2023

33. Crowding and the shape of COVID-19 epidemics

Author: Moritz U. G. Kraemer, Bernardo Gutierrez, Ben Adlam, Alexander E. Zarebski, Robert C. Reiner, Benjamin Rader, Anjalika Nande, Munik Shrestha, Oliver G. Pybus, David M. Pigott, Alison L. Hill, John S. Brownstein, Marcia C. Castro, Samuel V. Scarpino, Huaiyu Tian, and Christopher Dye
Subjects: 0301 basic medicine, China, Coronavirus disease 2019 (COVID-19), Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), Physical Distancing, Population, Public Policy, General Biochemistry, Genetics and Molecular Biology, Disease Outbreaks, law.invention, 03 medical and health sciences, 0302 clinical medicine, law, Urbanization, Pandemic, Humans, Human Activities, Cities, education, Pandemics, Demography, Population Density, Travel, education.field_of_study, Geography, SARS-CoV-2, COVID-19, Outbreak, General Medicine, Crowding, 3. Good health, 030104 developmental biology, Transmission (mechanics), 030220 oncology & carcinogenesis, Contact Tracing, Forecasting
Abstract: The coronavirus disease 2019 (COVID-19) pandemic is straining public health systems worldwide, and major non-pharmaceutical interventions have been implemented to slow its spread1,2,3,4. During the initial phase of the outbreak, dissemination of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was primarily determined by human mobility from Wuhan, China5,6. Yet empirical evidence on the effect of key geographic factors on local epidemic transmission is lacking7. In this study, we analyzed highly resolved spatial variables in cities, together with case count data, to investigate the role of climate, urbanization and variation in interventions. We show that the degree to which cases of COVID-19 are compressed into a short period of time (peakedness of the epidemic) is strongly shaped by population aggregation and heterogeneity, such that epidemics in crowded cities are more spread over time, and crowded cities have larger total attack rates than less populated cities. Observed differences in the peakedness of epidemics are consistent with a meta-population model of COVID-19 that explicitly accounts for spatial hierarchies. We paired our estimates with globally comprehensive data on human mobility and predict that crowded cities worldwide could experience more prolonged epidemics.
Published: 2020

34. Estimating global injuries morbidity and mortality

Author: Zohreh Arefi, Rasmus Havmoeller, Pietro Ferrara, Vijay Kumar Chattu, Payman Salamati, Agegnehu Bante Getenet, Seyedeh Zahra Masoumi, Alexander C. Tsai, Andrew T Olagunju, Carl Abelardo T. Antonio, Morteza Abdullatif Khafaie, Tim Driscoll, Reza Rawassizadeh, Vishnu Renjith, Taras Kavetskyy, Ejaz Ahmad Khan, Gebreamlak Gebremedhn Gebremeskel, Richard C. Franklin, Naznin Hossain, Bhaskaran Unnikrishnan, Fariborz Mansour-Ghanaei, Arash Tehrani-Banihashemi, Morteza Naserbakht, Sangram Kishor Patel, Vera Marisa Costa, Suzanne Polinder, Ziad El-Khatib, Ashish Pathak, Mika Shigematsu, Bryan L. Sykes, David M. Pigott, Zeleke Aschalew Melketsedik, Hadi Hassankhani, Nuworza Kugbey, Lal B. Rawal, Masoud Foroutan, Dejana Braithwaite, Muluken Bekele Sorrie, Grant M. A. Wyper, Joht Singh Chandan, Akbar Barzegar, Hamid Safarpour, Kate E. LeGrand, Giulio Castelpietra, Dina Nur Anggraini Ningrum, Seth Christopher Yaw Appiah, Amin Soheili, Azeem Majeed, Akshaya Srikanth Bhagavathula, Nikolay Ivanovich Briko, Vahid Yazdi-Feyzabadi, Walter Mendoza, Ghulam Mustafa, Cuong Tat Nguyen, Pengpeng Ye, Mahesh P A, Carlos Miguel Rios González, Ahmad Daryani, Ibrahim Abdollahpour, Masoud Moradi, Haileab Fekadu Wolde, Mohammad Rabiee, Jaya Prasad Tripathy, Jack T Fox, Diep Ngoc Nguyen, Pallab K. Maulik, Ai-Min Wu, Félix Carvalho, Yun Jin Kim, Fatemeh Rajati, Nathaniel J Henry, David C. Schwebel, Ali H. Mokdad, Akine Eshete Abosetugn, Leonardo Roever, Ketema Bizuwork Gebremedhin, Khalid A Altirkawi, Hayimro Edemealem Merie, Farhad Ghamari, Ramesh Holla, Arash Sarveazad, Devasahayam J. Christopher, Kedir Hussein Abegaz, Suresh Jungari, Rohan Borschmann, Irfan Ullah, Nitin Joseph, Catalina Liliana Andrei, Abdallah M. Samy, Roman Topor-Madry, Michal Grivna, Ivo Rakovac, Mohammad Zamani, Antonio Maria Borzì, Atif Amin Baig, Mohsen Bayati, Mohammad Ali Mansournia, Fatemeh Heydarpour, Juanita A. Haagsma, Segun Emmanuel Ibitoye, Mehdi Sayyah, Teshome Abegaz Kamil, Fakher Rahim, Dillon O Sylte, Feng Sha, Palash Chandra Banik, Mohan Bairwa, Mariya Vladimirovna Titova, Yong Yu, Delia Hendrie, Spencer L. James, Anusha Ganapati Bhat, Seok Jun Yoon, Siavash Rahimi, Jost B. Jonas, Michael R. Phillips, Diana Silva, Amir Hossein Goudarzian, Samath D Dharmaratne, Roba Khundkar, Jemal Abdu Mohammed, Joemer C. Maravilla, Eleonora Dubljanin, Arun Balachandran, Arman Latifi, Randah R. Hamadeh, G. K. Mini, Alessandra C. Goulart, Andreea Mirica, Fatemeh Zarei, Habte Belete, Telma Zahirian Moghadam, Maseer Khan, Morenike Oluwatoyin Folayan, Atte Meretoja, Jasvinder A. Singh, Guilherme Borges, Yasir Waheed, Fahad Alanezi, Masoud Moghadaszadeh, Guoqing Hu, Archith Boloor, Luca Ronfani, Olayinka Stephen Ilesanmi, Vahid Rashedi, Mehran Shams-Beyranvand, Trang Huyen Nguyen, Yonatal Mesfin Tefera, Yousef Khader, Adnan Kisa, Simon I. Hay, Takeshi Fukumoto, Shahrzad Bazargan-Hejazi, Yuichiro Yano, Carlos A Castañeda-Orjuela, Suzanne Barker-Collo, Ghasem Azarian, Sezer Kisa, Basema Saddik, Leake Gebremeskel, Yetunde O. John-Akinola, Jennifer Rickard, Ilais Moreno Velásquez, Priya Rathi, Ali Bijani, Smita Pakhale, Francesco Saverio Violante, Neeraj Bedi, Efat Mohamadi, Deborah Carvalho Malta, Marek Majdan, Feleke Mekonnen Demeke, Chukwudi A Nnaji, Richard Ofori-Asenso, Prateek Rastogi, Xuefeng Liu, Monika Sawhney, Ayman Grada, Prasanna Mithra, Shanshan Li, Masood Ali Shaikh, Erin B Hamilton, Hung Chak Ho, Zichen Liu, Irini Chatziralli, Paulo A. Lotufo, Yuming Guo, Daniel N. Kiirithio, Hamidreza Komaki, Aristidis Tsatsakis, Brijesh Sathian, Daniel Kim, Yoosik Youm, Kebreab Paulos, Saqib Ali, Beriwan Abdulqadir Ali, M. Mofizul Islam, Ahad Bakhtiari, Karen M. Tabb, Davood Anvari, Tomislav Mestrovic, Yunquan Zhang, Muhammad Aziz Rahman, Naohiro Yonemoto, Rupak Desai, Peter Njenga Keiyoro, Rajan Nikbakhsh, Zahra Sadat Dibaji Forooshani, Carlo La Vecchia, Amrit Banstola, Niguse Meles Alema, Shivakumar K.M. Kondlahalli, Soraya Seedat, Filippo Ariani, Chi Linh Hoang, Maziar Moradi-Lakeh, Erika Ota, Dimas Ria Angga Pribadi, Van C. Lansingh, Mohammed Madadin, Mostafa Dianati Nasab, Biniyam Sahiledengle Geberemariyam, Amanda J. Mason-Jones, Marcela Agudelo-Botero, Dinh-Toi Chu, Bach Xuan Tran, Till Bärnighausen, Mehdi Mirzaei-Alavijeh, Mohammad Ali Jahani, Bereket Duko Adema, In-Hwan Oh, Paul S. F. Yip, Ionut Negoi, Eduarda Fernandes, Reza Ghanei Gheshlagh, Kelly Jones, Mahdi Safdarian, Josep Maria Haro, Zahiruddin Quazi Syed, Diego De Leo, Khanh Bao Tran, Syed Mohamed Aljunid, Yasser S. Amer, Tommi Vasankari, Vasily Vlassov, Jose C. Adsuar, Zoubida Zaidi, Asadollah Gholamian, Theo Vos, Nauman Khalid, Malke Asaad, André Faro, Rushdia Ahmed, Tuomo J. Meretoja, Ireneous N. Soyiri, Ana Isabel Ribeiro, Hoa Thi Do, Temesgen Gebeyehu Wondmeneh, Vladimir Andreevich Korshunov, Manasi Kumar, Josip Car, Shoaib Hassan, Mukhammad David Naimzada, Gurudatta Naik, Ali Rajabpour-Sanati, Mustafa Z. Younis, Farid Najafi, Ester Cerin, Lorenzo Monasta, Amit Arora, Rizwan Suliankatchi Abdulkader, Huong Lan Thi Nguyen, Javad Nazari, Joel M. Francis, Martin Amogre Ayanore, Farahnaz Joukar, Babak Moazen, Kebede Deribe, Nithin Kumar, Seyed-Mohammad Fereshtehnejad, Getinet Ayano, Serge Resnikoff, Mark A. Stokes, Alaa Badawi, Narinder Kumar, Habibolah Khazaie, Sojib Bin Zaman, Faris Lami, Jens R. Chapman, Onome Bright Oghenetega, Dhirendra N Sinha, Engida Yisma, Aso Mohammad Darwesh, Demelash Abewa Elemineh, Junaid Khan, Shane D. Morrison, Mansour Ghafourifard, Ata Rafiee, Hadush Gebremariam, Oghenowede Eyawo, Amir Hasanzadeh, Ana Laura Manda, Muhammed Magdy Abd El Razek, Morteza Arab-Zozani, Narayana Manjunatha, Navid Manafi, Ritesh G. Menezes, Ferrán Catalá-López, Hossein Poustchi, Ghobad Moradi, Suliman Alghnam, Tinuke O Olagunju, Mohiuddin Ahsanul Kabir Chowdhury, Man Mohan Mehndiratta, Sorin Hostiuc, Jalal Arabloo, Shaun Wen Huey Lee, Chris D Castle, Joan B. Soriano, Syed Amir Gilani, Mariam Molokhia, Gaurav Gupta, Inga Dora Sigfusdottir, Isabela M. Benseñor, Desalegn Getnet Demsie, Atalay Goshu Muluneh, Young Eun Kim, Olatunde Aremu, Leeberk Raja Inbaraj, Jae Il Shin, Berhe Etsay Tesfay, Neha Chaudhary, Juan Sanabria, Soosanna Kumary Chattu, Sanni Yaya, Rekha Thapar, Biruk Wogayehu Taddele, Maciej Banach, Salman Rawaf, Nicholas L S Roberts, Jagadish Rao Padubidri, Manoochehr Karami, Derrick A Bennett, Duduzile Ndwandwe, Soheil Hassanipour, Ashish Badiye, Krittika Bhattacharyya, Obinna Onwujekwe, Kewal Krishan, João Coelho, Ahmed Abdelalim, Kerrie E. Doyle, Tomasz Miazgowski, Rahman Shiri, Demelash Woldeyohannes Handiso, Navid Rabiee, Nikita Otstavnov, Brian J. Hall, Olalekan A. Uthman, Rohollah Kalhor, Swayam Prakash, Beatriz Paulina Ayala Quintanilla, Gebre Teklemariam Demoz, Ted R. Miller, Rebecca Ivers, Chuanhua Yu, Pascual R. Valdez, Martha Híjar, Taweewat Wiangkham, Maysaa El Sayed Zaki, Rafael Tabarés-Seisdedos, Melkamu Merid Mengesha, Seyedmojtaba Seyedmousavi, Ravi Prakash Jha, Songhomitra Panda-Jonas, Aidin Abedi, Savita Lasrado, Veincent Christian Filipino Pepito, Amir Almasi-Hashiani, Burcu Kucuk Bicer, Hagazi Gebremedhin Kassaye, Yousef Veisani, Ayenew Kassie Tesema, Amir Radfar, Pawan Faris, Oladimeji Adebayo, Fares Alahdab, Heather Orpana, Narayan Bahadur Mahotra, Tripti Khanna, Girikumar Kumaresh, Alireza Rafiei, João Mauricio Castaldelli-Maia, Subramanian Senthilkumaran, Cyrus Alinia, Era Upadhyay, Stanislav S. Otstavnov, Ahmed Omar Bali, Farshad Farzadfar, Vivek Kumar, Sanghamitra Pati, Gholamreza Roshandel, Daiane Borges Machado, Yuan-Pang Wang, Rafael Alves Guimarães, Aziz Sheikh, Jeevan Pereira, Ahamarshan Jayaraman Nagarajan, Soraya Siabani, Kamarul Imran Musa, Farshad Pourmalek, Janet L Leasher, Shafiu Mohammed, Shyam Sundar Budhathoki, Mehdi Naderi, Mehdi Hosseinzadeh, Eun-Kee Park, Marwa Rashad Salem, Alireza Ansari-Moghaddam, Achala Upendra Jayatilleke, Paolo Lauriola, Amir Kasaeian, Adam Belay Wondmieneh, Tilahun Belete Mossie, Rovshan Khalilov, Milena M Santric Milicevic, Ai Koyanagi, Shailesh Advani, Felix Akpojene Ogbo, Nihad A. Almasri, Abdur Razzaque Sarker, Salman Khazaei, Narayanaswamy Venketasubramanian, Josephine W. Ngunjiri, Mihajlo Jakovljevic, Matthew Cunningham, Shaimaa I. El-Jaafary, Hosni Salem, Sharareh Eskandarieh, Muktar Beshir Ahmed, Amy E. Peden, Rajaram Yadav, Srikanta Banerjee, Kenean Getaneh Tlaye, Ronan A Lyons, Farzad Jalilian, Jacek Jerzy Jozwiak, Soufiane Boufous, Leila R Kalankesh, Abdelrahman Ibrahim Abushouk, Mikk Jürisson, Hamid Reza Tohidinik, Flavia M. Cicuttini, Zachary V Dingels, Florian Fischer, Saad M.A. Dahlawi, Rahmatollah Moradzadeh, Getnet Gedefaw, Tauseef Ahmad, Faramarz Shaahmadi, Hamed Zandian, Bruno Ramos Nascimento, Gregory J. Bertolacci, Hamid Yimam Hassen, Christian Lycke Ellingsen, Zulfiqar A Bhutta, Pankaj Bhardwaj, Dawit Habte Woldeyes, Robert C. Reiner, Rakhi Dandona, Akram Pourshams, Lorainne Tudor Car, Inbal Salz, Reza Mohammadpourhodki, Farnam Mohebi, A. A. Fomenkov, Abraham Getachew Kelbore, Benjamin B. Massenburg, Yousef Mohammad, Arash Ziapour, Zabihollah Yousefi, Biresaw Wassihun Alemu, Neeti Kapoor, Seyed Sina Naghibi Irvani, Vinod C. Nayak, Yoshan Moodley, Venkatesh Maled, Zhi-Jiang Zhang, Arielle Wilder Eagan, Saif Ullah, Anwar Rafay, Ali Koolivand, Ibrahim A Khalil, Parul Puri, Lidia Sanchez Riera, Vinay Nangia, Tanuj Kanchan, Mu'awiyyah Babale Sufiyan, Lidia Morawska, Saeed Shahabi, Tiffany K. Gill, Traolach S. Brugha, Michael K. Hole, Vahid Alipour, Sowmya J. Rao, Pankaj Singh, Rosario Cárdenas, Abdollah Mohammadian-Hafshejani, Sanjay Zodpey, Birhanu Geta, Amaha Kahsay, Moritz U. G. Kraemer, Claudio Alberto Dávila-Cervantes, Samer Hamidi, Gbenga A. Kayode, Miloud Taki Eddine Aichour, Peter Memiah, Erkin M. Mirrakhimov, Sheikh Mohammed Shariful Islam, Sadia Bibi, Bartosz Miazgowski, Daniel Y. Cho, Keyvan Pakshir, Aziz Rezapour, R V Polibin, Sudha Jayaraman, S. Mohammad Sajadi, Amir Manafi, Berhe Gebremichael, Ali Yadollahpour, Barthelemy Kuate Defo, Alan D. Lopez, Masoud Behzadifar, Mohammad A. Mohseni Bandpei, Ali Kabir, Jee-Young Jasmine Choi, Gebretsadkan Hintsa Tekulu, Kidanemaryam Berhe, Bill & Melinda Gates Foundation, Sanofi, HUS Comprehensive Cancer Center, Clinicum, University of Helsinki, Helsinki University Hospital Area, HUS Neurocenter, James, S. L., Castle, C. D., Dingels, Z. V., Fox, J. T., Hamilton, E. B., Liu, Z., Roberts, N. L. S., Sylte, D. O., Bertolacci, G. J., Cunningham, M., Henry, N. J., Legrand, K. E., Abdelalim, A., Abdollahpour, I., Abdulkader, R. S., Abedi, A., Abegaz, K. H., Abosetugn, A. E., Abushouk, A. I., Adebayo, O. M., Adsuar, J. C., Advani, S. M., Agudelo-Botero, M., Ahmad, T., Ahmed, M. B., Ahmed, R., Aichour, M. T. E., Alahdab, F., Alanezi, F. M., Alema, N. M., Alemu, B. W., Alghnam, S. A., Ali, B. A., Ali, S., Alinia, C., Alipour, V., Aljunid, S. M., Almasi-Hashiani, A., Almasri, N. A., Altirkawi, K., Amer, Y. S. A., Andrei, C. L., Ansari-Moghaddam, A., Antonio, C. A. T., Anvari, D., Appiah, S. C. Y., Arabloo, J., Arab-Zozani, M., Arefi, Z., Aremu, O., Ariani, F., Arora, A., Asaad, M., Quintanilla, B. P. A., Ayano, G., Ayanore, M. A., Azarian, G., Badawi, A., Badiye, A. D., Baig, A. A., Bairwa, M., Bakhtiari, A., Balachandran, A., Banach, M., Banerjee, S. K., Banik, P. C., Banstola, A., Barker-Collo, S. L., Barnighausen, T. W., Barzegar, A., Bayati, M., Bazargan-Hejazi, S., Bedi, N., Behzadifar, M., Belete, H., Bennett, D. A., Bensenor, I. M., Berhe, K., Bhagavathula, A. S., Bhardwaj, P., Bhat, A. G., Bhattacharyya, K., Bhutta, Z. A., Bibi, S., Bijani, A., Boloor, A., Borges, G., Borschmann, R., Borzi, A. M., Boufous, S., Braithwaite, D., Briko, N. I., Brugha, T., Budhathoki, S. S., Car, J., Cardenas, R., Carvalho, F., Castaldelli-Maia, J. M., Castaneda-Orjuela, C. A., Castelpietra, G., Catala-Lopez, F., Cerin, E., Chandan, J. S., Chapman, J. R., Chattu, V. K., Chattu, S. K., Chatziralli, I., Chaudhary, N., Cho, D. Y., Choi, J. -Y. J., Chowdhury, M. A. K., Christopher, D. J., Chu, D. -T., Cicuttini, F. M., Coelho, J. M., Costa, V. M., Dahlawi, S. M. A., Daryani, A., Davila-Cervantes, C. A., De Leo, D., Demeke, F. M., Demoz, G. T., Demsie, D. G., Deribe, K., Desai, R., Nasab, M. D., Da Silva, D. D., Forooshani, Z. S. D., Do, H. T., Doyle, K. E., Driscoll, T. R., Dubljanin, E., Adema, B. D., Eagan, A. W., Elemineh, D. A., El-Jaafary, S. I., El-Khatib, Z., Ellingsen, C. L., El Sayedzaki, M., Eskandarieh, S., Eyawo, O., Faris, P. S., Faro, A., Farzadfar, F., Fereshtehnejad, S. -M., Fernandes, E., Ferrara, P., Fischer, F., Folayan, M. O., Fomenkov, A. A., Foroutan, M., Francis, J. M., Franklin, R. C., Fukumoto, T., Geberemariyam, B. S., Gebremariam, H., Gebremedhin, K. B., Gebremeskel, L. G., Gebremeskel, G. G., Gebremichael, B., Gedefaw, G. A., Geta, B., Getenet, A. B., Ghafourifard, M., Ghamari, F., Gheshlagh, R. G., Gholamian, A., Gilani, S. A., Gill, T. K., Goudarzian, A. H., Goulart, A. C., Grada, A., Grivna, M., Guimaraes, R. A., Guo, Y., Gupta, G., Haagsma, J. A., Hall, B. J., Hamadeh, R. R., Hamidi, S., Handiso, D. W., Haro, J. M., Hasanzadeh, A., Hassan, S., Hassanipour, S., Hassankhani, H., Hassen, H. Y., Havmoeller, R., Hendrie, D., Heydarpour, F., Hijar, M., Ho, H. C., Hoang, C. L., Hole, M. K., Holla, R., Hossain, N., Hosseinzadeh, M., Hostiuc, S., Hu, G., Ibitoye, S. E., Ilesanmi, O. S., Inbaraj, L. R., Irvani, S. S. N., Islam, M. M., Islam, S. M. S., Ivers, R. Q., Jahani, M. A., Jakovljevic, M., Jalilian, F., Jayaraman, S., Jayatilleke, A. U., Jha, R. P., John-Akinola, Y. O., Jonas, J. B., Jones, K. M., Joseph, N., Joukar, F., Jozwiak, J. J., Jungari, S. B., Jurisson, M., Kabir, A., Kahsay, A., Kalankesh, L. R., Kalhor, R., Kamil, T. A., Kanchan, T., Kapoor, N., Karami, M., Kasaeian, A., Kassaye, H. G., Kavetskyy, T., Kayode, G. A., Keiyoro, P. N., Kelbore, A. G., Khader, Y. S., Khafaie, M. A., Khalid, N., Khalil, I. A., Khalilov, R., Khan, M., Khan, E. A., Khan, J., Khanna, T., Khazaei, S., Khazaie, H., Khundkar, R., Kiirithio, D. N., Kim, Y. -E., Kim, Y. J., Kim, D., Kisa, S., Kisa, A., Komaki, H., Kondlahalli, S. K. M., Koolivand, A., Korshunov, V. A., Koyanagi, A., Kraemer, M. U. G., Krishan, K., Defo, B. K., Bicer, B. K., Kugbey, N., Kumar, N., Kumar, M., Kumar, V., Kumaresh, G., Lami, F. H., Lansingh, V. C., Lasrado, S., Latifi, A., Lauriola, P., Vecchia, C. L., Leasher, J. L., Lee, S. W. H., Li, S., Liu, X., Lopez, A. D., Lotufo, P. A., Lyons, R. A., Machado, D. B., Madadin, M., Abd El Razek, M. M., Mahotra, N. B., Majdan, M., Majeed, A., Maled, V., Malta, D. C., Manafi, N., Manafi, A., Manda, A. -L., Manjunatha, N., Mansour-Ghanaei, F., Mansournia, M. A., Maravilla, J. C., Mason-Jones, A. J., Masoumi, S. Z., Massenburg, B. B., Maulik, P. K., Mehndiratta, M. M., Melketsedik, Z. A., Memiah, P. T. N., Mendoza, W., Menezes, R. G., Mengesha, M. M., Meretoja, T. J., Meretoja, A., Merie, H. E., Mestrovic, T., Miazgowski, B., Miazgowski, T., Miller, T. R., Mini, G. K., Mirica, A., Mirrakhimov, E. M., Mirzaei-Alavijeh, M., Mithra, P., Moazen, B., Moghadaszadeh, M., Mohamadi, E., Mohammad, Y., Darwesh, A. M., Mohammadian-Hafshejani, A., Mohammadpourhodki, R., Mohammed, S., Mohammed, J. A., Mohebi, F., Bandpei, M. A. M., Molokhia, M., Monasta, L., Moodley, Y., Moradi, M., Moradi, G., Moradi-Lakeh, M., Moradzadeh, R., Morawska, L., Velasquez, I. M., Morrison, S. D., Mossie, T. B., Muluneh, A. G., Musa, K. I., Mustafa, G., Naderi, M., Nagarajan, A. J., Naik, G., Naimzada, M. D., Najaf, F., Nangia, V., Nascimento, B. R., Naserbakht, M., Nayak, V., Nazari, J., Ndwandwe, D. E., Negoi, I., Ngunjiri, J. W., Nguyen, T. H., Nguyen, C. T., Nguyen, D. N., Nguyen, H. L. T., Nikbakhsh, R., Ningrum, D. N. A., Nnaji, C. A., Ofori-Asenso, R., Ogbo, F. A., Oghenetega, O. B., Oh, I. -H., Olagunju, A. T., Olagunju, T. O., Bali, A. O., Onwujekwe, O. E., Orpana, H. M., Ota, E., Otstavnov, N., Otstavnov, S. S., Mahesh, A. P., Padubidri, J. R., Pakhale, S., Pakshir, K., Panda-Jonas, S., Park, E. -K., Patel, S. K., Pathak, A., Pati, S., Paulos, K., Peden, A. E., Pepito, V. C. F., Pereira, J., Phillips, M. R., Polibin, R. V., Polinder, S., Pourmalek, F., Pourshams, A., Poustchi, H., Prakash, S., Pribadi, D. R. A., Puri, P., Syed, Z. Q., Rabiee, N., Rabiee, M., Radfar, A., Rafay, A., Rafee, A., Rafei, A., Rahim, F., Rahimi, S., Rahman, M. A., Rajabpour-Sanati, A., Rajati, F., Rakovac, I., Rao, S. J., Rashedi, V., Rastogi, P., Rathi, P., Rawaf, S., Rawal, L., Rawassizadeh, R., Renjith, V., Resnikoff, S., Rezapour, A., Ribeiro, A. I., Rickard, J., Gonzalez, C. M. R., Roever, L., Ronfani, L., Roshandel, G., Saddik, B., Safarpour, H., Safdarian, M., Sajadi, S. M., Salamati, P., Salem, M. R. R., Salem, H., Salz, I., Samy, A. M., Sanabria, J., Riera, L. S., Milicevic, M. M. S., Sarker, A. R., Sarveazad, A., Sathian, B., Sawhney, M., Sayyah, M., Schwebel, D. C., Seedat, S., Senthilkumaran, S., Seyedmousavi, S., Sha, F., Shaahmadi, F., Shahabi, S., Shaikh, M. A., Shams-Beyranvand, M., Sheikh, A., Shigematsu, M., Shin, J. I., Shiri, R., Siabani, S., Sigfusdottir, I. D., Singh, J. A., Singh, P. K., Sinha, D. N., Soheili, A., Soriano, J. B., Sorrie, M. B., Soyiri, I. N., Stokes, M. A., Sufiyan, M. B., Sykes, B. L., Tabares-Seisdedos, R., Tabb, K. M., Taddele, B. W., Tefera, Y. M., Tehrani-Banihashemi, A., Tekulu, G. H., Tesema, A. K. T., Tesfay, B. E., Thapar, R., Titova, M. V., Tlaye, K. G., Tohidinik, H. R., Topor-Madry, R., Tran, K. B., Tran, B. X., Tripathy, J. P., Tsai, A. C., Tsatsakis, A., Car, L. T., Ullah, I., Ullah, S., Unnikrishnan, B., Upadhyay, E., Uthman, O. A., Valdez, P. R., Vasankari, T. J., Veisani, Y., Venketasubramanian, N., Violante, F. S., Vlassov, V., Waheed, Y., Wang, Y. -P., Wiangkham, T., Wolde, H. F., Woldeyes, D. H., Wondmeneh, T. G., Wondmieneh, A. B., Wu, A. -M., Wyper, G. M. A., Yadav, R., Yadollahpour, A., Yano, Y., Yaya, S., Yazdi-Feyzabadi, V., Ye, P., Yip, P., Yisma, E., Yonemoto, N., Yoon, S. -J., Youm, Y., Younis, M. Z., Yousef, Z., Yu, C., Yu, Y., Moghadam, T. Z., Zaidi, Z., Zaman, S. B., Zamani, M., Zandian, H., Zarei, F., Zhang, Z. -J., Zhang, Y., Ziapour, A., Zodpey, S., Dandona, R., Dharmaratne, S. D., Hay, S. I., Mokdad, A. H., Pigott, D. M., Reiner, R. C., Vos, T., James, S, Castle, C, Dingels, Z, Fox, J, Hamilton, E, Liu, Z, Roberts, N, Sylte, D, Bertolacci, G, Cunningham, M, Henry, N, Legrand, K, Abdelalim, A, Abdollahpour, I, Abdulkader, R, Abedi, A, Abegaz, K, Abosetugn, A, Abushouk, A, Adebayo, O, Adsuar, J, Advani, S, Agudelo-Botero, M, Ahmad, T, Ahmed, M, Ahmed, R, Eddine Aichour, M, Alahdab, F, Alanezi, F, Alema, N, Alemu, B, Alghnam, S, Ali, B, Ali, S, Alinia, C, Alipour, V, Aljunid, S, Almasi-Hashiani, A, Almasri, N, Altirkawi, K, Abdeldayem Amer, Y, Andrei, C, Ansari-Moghaddam, A, T Antonio, C, Anvari, D, Yaw Appiah, S, Arabloo, J, Arab-Zozani, M, Arefi, Z, Aremu, O, Ariani, F, Arora, A, Asaad, M, Ayala Quintanilla, B, Ayano, G, Ayanore, M, Azarian, G, Badawi, A, Badiye, A, Baig, A, Bairwa, M, Bakhtiari, A, Balachandran, A, Banach, M, Banerjee, S, Banik, P, Banstola, A, Barker-Collo, S, Bärnighausen, T, Barzegar, A, Bayati, M, Bazargan-Hejazi, S, Bedi, N, Behzadifar, M, Belete, H, Bennett, D, Bensenor, I, Berhe, K, Bhagavathula, A, Bhardwaj, P, Bhat, A, Bhattacharyya, K, Bhutta, Z, Bibi, S, Bijani, A, Boloor, A, Borges, G, Borschmann, R, Borzì, A, Boufous, S, Braithwaite, D, Briko, N, Brugha, T, Budhathoki, S, Car, J, Cárdenas, R, Carvalho, F, Castaldelli-Maia, J, Castañeda-Orjuela, C, Castelpietra, G, Catalá-López, F, Cerin, E, Chandan, J, Chapman, J, Chattu, V, Chattu, S, Chatziralli, I, Chaudhary, N, Cho, D, Choi, J, Kabir Chowdhury, M, Christopher, D, Chu, D, Cicuttini, F, Coelho, J, Costa, V, Dahlawi, S, Daryani, A, Dávila-Cervantes, C, Leo, D, Demeke, F, Demoz, G, Demsie, D, Deribe, K, Desai, R, Nasab, M, Silva, D, Dibaji Forooshani, Z, Do, H, Doyle, K, Driscoll, T, Dubljanin, E, Adema, B, Eagan, A, Elemineh, D, El-Jaafary, S, El-Khatib, Z, Ellingsen, C, Zaki, M, Eskandarieh, S, Eyawo, O, Faris, P, Faro, A, Farzadfar, F, Fereshtehnejad, S, Fernandes, E, Ferrara, P, Fischer, F, Folayan, M, Fomenkov, A, Foroutan, M, Francis, J, Franklin, R, Fukumoto, T, Geberemariyam, B, Gebremariam, H, 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Z.A., Memiah, P.T.N., Mendoza, W., Menezes, R.G., Mengesha, M.M., Meretoja, T.J., Meretoja, A., Merie, H.E., Mestrovic, T., Miazgowski, B., Miazgowski, T., Miller, T.R., Mini, G.K., Mirica, A., Mirrakhimov, E.M., Mirzaei-Alavijeh, M., Mithra, P., Moazen, B., Moghadaszadeh, M., Mohamadi, E., Mohammad, Y., Darwesh, A.M., Mohammadian-Hafshejani, A., Mohammadpourhodki, R., Mohammed, S., Mohammed, J.A., Mohebi, F., Bandpei, M.A.M., Molokhia, M., Monasta, L., Moodley, Y., Moradi, M., Moradi, G., Moradi-Lakeh, M., Moradzadeh, R., Morawska, L., Velásquez, I.M., Morrison, S.D., Mossie, T.B., Muluneh, A.G., Musa, K.I., Mustafa, G., Naderi, M., Nagarajan, A.J., Naik, G., Naimzada, M.D., Najaf, F., Nangia, V., Nascimento, B.R., Naserbakht, M., Nayak, V., Nazari, J., Ndwandwe, D.E., Negoi, I., Ngunjiri, J.W., Nguyen, T.H., Nguyen, C.T., Nguyen, D.N., Nguyen, H.L.T., Nikbakhsh, R., Ningrum, D.N.A., Nnaji, C.A., Ofori-Asenso, R., Ogbo, F.A., Oghenetega, O.B., Oh, I.-H., Olagunju, A.T., Olagunju, 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Sayyah, M., Schwebel, D.C., Seedat, S., Senthilkumaran, S., Seyedmousavi, S., Sha, F., Shaahmadi, F., Shahabi, S., Shaikh, M.A., Shams-Beyranvand, M., Sheikh, A., Shigematsu, M., Shin, J.I., Shiri, R., Siabani, S., Sigfusdottir, I.D., Singh, J.A., Singh, P.K., Sinha, D.N., Soheili, A., Soriano, J.B., Sorrie, M.B., Soyiri, I.N., Stokes, M.A., Sufiyan, M.B., Sykes, B.L., Tabarés-Seisdedos, R., Tabb, K.M., Taddele, B.W., Tefera, Y.M., Tehrani-Banihashemi, A., Tekulu, G.H., Tesema, A.K.T., Tesfay, B.E., Thapar, R., Titova, M.V., Tlaye, K.G., Tohidinik, H.R., Topor-Madry, R., Tran, K.B., Tran, B.X., Tripathy, J.P., Tsai, A.C., Tsatsakis, A., Car, L.T., Ullah, I., Ullah, S., Unnikrishnan, B., Upadhyay, E., Uthman, O.A., Valdez, P.R., Vasankari, T.J., Veisani, Y., Venketasubramanian, N., Violante, F.S., Vlassov, V., Waheed, Y., Wang, Y.-P., Wiangkham, T., Wolde, H.F., Woldeyes, D.H., Wondmeneh, T.G., Wondmieneh, A.B., Wu, A.-M., Wyper, G.M.A., Yadav, R., Yadollahpour, A., Yano, Y., Yaya, S., Yazdi-Feyzabadi, V., Ye, P., Yip, P., Yisma, E., Yonemoto, N., Yoon, S.-J., Youm, Y., Younis, M.Z., Yousef, Z., Yu, C., Yu, Y., Moghadam, T.Z., Zaidi, Z., Zaman, S.B., Zamani, M., Zandian, H., Zarei, F., Zhang, Z.-J., Zhang, Y., Ziapour, A., Zodpey, S., Dandona, R., Dharmaratne, S.D., Hay, S.I., Mokdad, A.H., Pigott, D.M., Reiner, R.C., Vos, T., Instituto de Saúde Pública da Universidade do Porto, James, Spencer L, Castle, Chris D, Dingels, Zachary V, Fox, Jack T, Adema, Bereket Duko, Yisma, Engida, Vos, Theo, and Public Health
Subjects: Male, Populations/contexts, 1106 Human Movement and Sports Sciences, Global injuries, population, 030204 cardiovascular system & hematology, Global Health, contexts, context, Global Burden of Disease, 0302 clinical medicine, Quality-Adjusted Life Year, Global health, 030212 general & internal medicine, Original Research, Data, populations/contexts, Incidence (epidemiology), Incidence, methodology, 3142 Public health care science, environmental and occupational health, Female, Public Health, TERRITORIES, Quality-Adjusted Life Years, descriptive epidemiology, Human, Disabilities, 195 COUNTRIES, statistical issue, 1117 Public Health and Health Services, 03 medical and health sciences, AGE, Life Expectancy, Environmental health, Injury prevention, SYSTEMATIC ANALYSIS, statistical issues, Humans, Mortality, Estimation, SEX-SPECIFIC MORTALITY, DISABILITY, Public Health, Environmental and Occupational Health, populations, Morbidity, Wounds and Injuries, 1106 Human Movement and Sports Sciences, 1117 Public Health and Health Services, 1701 Psychology, Quality-adjusted life year, Years of potential life lost, 1701 Psychology, Life expectancy, Estimates, Human medicine, populations/context
Abstract: Background. While there is a long history of measuring death and disability from injuries, modern research methods must account for the wide spectrum of disability that can occur in an injury, and must provide estimates with sufficient demographic, geographical and temporal detail to be useful for policy makers. The Global Burden of Disease (GBD) 2017 study used methods to provide highly detailed estimates of global injury burden that meet these criteria. Methods. In this study, we report and discuss the methods used in GBD 2017 for injury morbidity and mortality burden estimation. In summary, these methods included estimating cause-specific mortality for every cause of injury, and then estimating incidence for every cause of injury. Non-fatal disability for each cause is then calculated based on the probabilities of suffering from different types of bodily injury experienced. Results. GBD 2017 produced morbidity and mortality estimates for 38 causes of injury. Estimates were produced in terms of incidence, prevalence, years lived with disability, cause-specific mortality, years of life lost and disability-adjusted life-years for a 28-year period for 22 age groups, 195 countries and both sexes. Conclusions. GBD 2017 demonstrated a complex and sophisticated series of analytical steps using the largest known database of morbidity and mortality data on injuries. GBD 2017 results should be used to help inform injury prevention policy making and resource allocation. We also identify important avenues for improving injury burden estimation in the future. Seyed Aljunid acknowledges the Department of Health Policy and Management, Faculty of Public Health, Kuwait University and International Centre for Casemix and Clinical Coding, Faculty of Medicine, National University of Malaysia for the approval and support to participate in this research project. Alaa Badawi acknowledges support by the Public Health Agency of Canada. Till Bärnighausen acknowledges support by the Alexander von Humboldt Foundation through the Alexander von Humboldt Professor award, funded by the German Federal Ministry of Education and Research. Traolach Brugha received support from NatCen Social Research (http://natcen.ac.uk/) via NHS Digital and Department of Health and Social Care London, for the Adult Psychiatric Morbidity Survey (APMS) programme. Felix Carvalho received support from UID/MULTI/04378/2019 with funding from FCT/MCTES through national funds. Vera M Costa acknowledges support from grant (SFRH/BHD/110001/2015), received by Portuguese national funds through Fundação para a Ciência e Tecnologia (FCT), IP, under the Norma Transitória DL57/2016/CP1334/CT0006. Kebede Deribe is supported by a grant from the Wellcome Trust [grant number 201900] as part of his International Intermediate Fellowship. Tim Driscoll acknowledges that work on occupational risk factors was partially supported by funds from the World Health Organization. Eduarda Fernandes acknowledges support from UID/QUI/50006/2019 with funding from FCT/MCTES through national funds. Yuming Guo acknowledges support from Career Development Fellowships of the Australian National Health and Medical Research Council (numbers APP1107107 and APP1163693). Sheikh Mohammed Shariful Islam is funded by a Fellowship from National Heart Foundation of Australia and Institute for Physical Activity and Nutrition, Deakin University. Mihajlo Jakovljevic acknowledges support by the Ministry of Education Science and Technological Development of the Republic of Serbia through the Grant number OI175014; publication of results was not contingent upon Ministry's censorship or approval. Sudha Jayaraman acknowledges support from: NIH R21: 1R21TW010439-01A1 (PI); Rotary Foundation Global Grant #GG1749568 (PI); NIH P20: 1P20CA210284-01A1 (Co-PI) and DOD grant W81XWH-16-2-0040 (Co-I), during the period of this study. Yun Jin Kim acknowledges support from a grant from the Research Management Centre, Xiamen University Malaysia [grant number: XMUMRF/2018-C2/ITCM/0001]. Kewal Krishan is supported by UGC Centre of Advanced Study (CAS II) awarded to the Department of Anthropology, Panjab University, Chandigarh, India. Mansai Kumar acknowledges support from FIC/ NIH K43 1K43MH114320-01. Amanda Mason-Jones acknowledges support by the University of York. Mariam Molokhia is supported by the National Institute for Health Research Biomedical Research Center at Guy’s and St Thomas’s National Health Service Foundation Trust and King’s College London. Ilais Moreno Velasquez is supported by the Sistema Nacional de Investigación (SNI, Senacyt, Panama). Mukhammad David Naimzada acknowledges support from Government of the Russian Federation (Agreement No – 075-02-2019-967). Duduzile Ndwandwe acknowledges support from Cochrane South Africa, South African Medical Research Council. Stanislav S. Otstavnov acknowledges the support from the Government of the Russian Federation (Agreement No – 075-02-2019-967). Ashish Pathak acknowledges support from Indian Council of Medical Research (ICMR), New Delhi, India (Grant number 2013-1253). Michael R Phillips acknowledges support in part by a grant from the National Natural Science Foundation of China (No.81761128031). Abdallah M. Samy received a fellowship from the Egyptian Fulbright Mission Program. Milena Santric Milicevic acknowledges the support from the Ministry of Education, Science and Technological Development, Republic of Serbia (Contract No. 175087). Seyedmojtaba Seyedmousavi was supported by the Intramural Research Program of the National Institutes of Health, Clinical Center, Department of Laboratory Medicine, Bethesda, MD, USA. Rafael Tabarés-Seisdedos was supported in part by the national grant PI17/00719 from ISCIII-FEDER. Sojib Bin Zaman acknowledges support from an "Australian Government Research Training Program (RTP) Scholarship.
Published: 2020

35. Rapid epidemic expansion of the SARS-CoV-2 Omicron variant in southern Africa

Author: Raquel Viana, Sikhulile Moyo, Daniel G. Amoako, Houriiyah Tegally, Cathrine Scheepers, Christian L. Althaus, Ugochukwu J. Anyaneji, Phillip A. Bester, Maciej F. Boni, Mohammed Chand, Wonderful T. Choga, Rachel Colquhoun, Michaela Davids, Koen Deforche, Deelan Doolabh, Louis du Plessis, Susan Engelbrecht, Josie Everatt, Jennifer Giandhari, Marta Giovanetti, Diana Hardie, Verity Hill, Nei-Yuan Hsiao, Arash Iranzadeh, Arshad Ismail, Charity Joseph, Rageema Joseph, Legodile Koopile, Sergei L. Kosakovsky Pond, Moritz U. G. Kraemer, Lesego Kuate-Lere, Oluwakemi Laguda-Akingba, Onalethatha Lesetedi-Mafoko, Richard J. Lessells, Shahin Lockman, Alexander G. Lucaci, Arisha Maharaj, Boitshoko Mahlangu, Tongai Maponga, Kamela Mahlakwane, Zinhle Makatini, Gert Marais, Dorcas Maruapula, Kereng Masupu, Mogomotsi Matshaba, Simnikiwe Mayaphi, Nokuzola Mbhele, Mpaphi B. Mbulawa, Adriano Mendes, Koleka Mlisana, Anele Mnguni, Thabo Mohale, Monika Moir, Kgomotso Moruisi, Mosepele Mosepele, Gerald Motsatsi, Modisa S. Motswaledi, Thongbotho Mphoyakgosi, Nokukhanya Msomi, Peter N. Mwangi, Yeshnee Naidoo, Noxolo Ntuli, Martin Nyaga, Lucier Olubayo, Sureshnee Pillay, Botshelo Radibe, Yajna Ramphal, Upasana Ramphal, James E. San, Lesley Scott, Roger Shapiro, Lavanya Singh, Pamela Smith-Lawrence, Wendy Stevens, Amy Strydom, Kathleen Subramoney, Naume Tebeila, Derek Tshiabuila, Joseph Tsui, Stephanie van Wyk, Steven Weaver, Constantinos K. Wibmer, Eduan Wilkinson, Nicole Wolter, Alexander E. Zarebski, Boitumelo Zuze, Dominique Goedhals, Wolfgang Preiser, Florette Treurnicht, Marietje Venter, Carolyn Williamson, Oliver G. Pybus, Jinal Bhiman, Allison Glass, Darren P. Martin, Andrew Rambaut, Simani Gaseitsiwe, Anne von Gottberg, and Tulio de Oliveira
Subjects: Models, Molecular, Recombination, Genetic, Botswana, Multidisciplinary, SARS-CoV-2, COVID-19, 610 Medicine & health, Antibodies, Neutralizing, South Africa, 360 Social problems & social services, Mutation, Spike Glycoprotein, Coronavirus, Humans, Phylogeny, Immune Evasion
Abstract: The SARS-CoV-2 epidemic in southern Africa has been characterized by three distinct waves. The first was associated with a mix of SARS-CoV-2 lineages, while the second and third waves were driven by the Beta (B.1.351) and Delta (B.1.617.2) variants, respectively1–3. In November 2021, genomic surveillance teams in South Africa and Botswana detected a new SARS-CoV-2 variant associated with a rapid resurgence of infections in Gauteng province, South Africa. Within three days of the first genome being uploaded, it was designated a variant of concern (Omicron, B.1.1.529) by the World Health Organization and, within three weeks, had been identified in 87 countries. The Omicron variant is exceptional for carrying over 30 mutations in the spike glycoprotein, which are predicted to influence antibody neutralization and spike function4. Here we describe the genomic profile and early transmission dynamics of Omicron, highlighting the rapid spread in regions with high levels of population immunity.
Published: 2022

36. Urgent need for a non-discriminatory and non-stigmatizing nomenclature for monkeypox virus

Author: Christian Happi, Ifedayo Adetifa, Placide Mbala, Richard Njouom, Emmanuel Nakoune, Anise Happi, Nnaemeka Ndodo, Oyeronke Ayansola, Gerald Mboowa, Trevor Bedford, Richard A. Neher, Cornelius Roemer, Emma Hodcroft, Houriiyah Tegally, Áine O’Toole, Andrew Rambaut, Oliver Pybus, Moritz U. G. Kraemer, Eduan Wilkinson, Joana Isidro, Vítor Borges, Miguel Pinto, João Paulo Gomes, Lucas Freitas, Paola C. Resende, Raphael T. C. Lee, Sebastian Maurer-Stroh, Cheryl Baxter, Richard Lessells, Ahmed E. Ogwell, Yenew Kebede, Sofonias K. Tessema, and Tulio de Oliveira
Subjects: General Immunology and Microbiology, Monkeypox Virus, Epidemiology, General Neuroscience, 360 Soziale Probleme, Sozialdienste, Genomics, Monkeypox, General Biochemistry, Genetics and Molecular Biology, Disease Outbreaks, Diagnosis, Humans, Monkeypox virus, General Agricultural and Biological Sciences, 610 Medizin und Gesundheit
Abstract: Free PMC article: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9451062/ We propose a novel, non-discriminatory classification of monkeypox virus diversity. Together with the World Health Organization, we named three clades (I, IIa and IIb) in order of detection. Within IIb, the cause of the current global outbreak, we identified multiple lineages (A.1, A.2, A.1.1 and B.1) to support real-time genomic surveillance. info:eu-repo/semantics/publishedVersion
Published: 2022
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37. Hospital admission and emergency care attendance risk for SARS-CoV-2 delta (B.1.617.2) compared with alpha (B.1.1.7) variants of concern: a cohort study

Author: Harry D Wilson, Elaine O'Toole, Andrew Bassett, Moritz U. G. Kraemer, Beth Blane, Scott Goodwin, Giri Shankar, Joseph Hughes, Lucy R. Frost, Alicia Thornton, Scott Elliott, Tammy V Merrill, Sheila Waugh, Alexander Adams, Peter Muir, Graciela Sluga, Rebecca Williams, Hannah Dent, Christophe Fraser, Shavanthi Rajatileka, John C. Hartley, Luke B Snell, Benjamin J Cogger, Lance Turtle, Alex Makunin, John A. Todd, Victoria Wright, Daniela De Angelis, James McKenna, Dinesh Aggarwal, Jonathan K. Ball, Jillian Durham, Garren Scott, Thushan I de Silva, Veena Raviprakash, Hannah M Pymont, Jason Coombes, Anita Lucaci, Luke R. Green, Leigh M Jackson, Hermione J. Webster, Louis du Plessis, David A. Jackson, Minal Patel, Áine O'Toole, Ravi Gupta, Marc Niebel, Garry Scarlett, Rajiv Shah, Guy Mollett, Kathy Li, Rory Gunson, Matthew Bashton, Carl Jones, Sara Kumziene-Summerhayes, Zoltan Molnar, Siona Silveira, Malte L Pinckert, Catherine Ludden, Angeliki Karamani, Leanne Kane, Brendan A I Payne, Alan McNally, Clare M. McCann, Holli Carden, Mohammad Raza, Alison E. Mather, Kate B. Cook, Amy Gaskin, David J. Williams, Shaun R. Seaman, Christopher I. Jones, Gilberto Betancor, Matthew T. G. Holden, Jennifier Liddle, Meera Unnikrishnan, Angie Green, Ben Taylor, Kelly Bicknell, Alexander J. Trotter, Emma Meader, Leanne M Kermack, Nathaniel Storey, Michelle Cronin, Sally Forrest, Sarah Jeremiah, Asad Zaidi, M Morgan, Alasdair MacLean, Thomas R. Connor, Johnathan M Evans, Rachael Stanley, Ryan P George, Nadine Holmes, Richard H. Myers, Christine Sambles, Bernardo Gutierrez, Jeffrey K. J. Cheng, Tim Wyatt, Natasha Jesudason, Lindsay Coupland, Monika Pusok, Manon Ragonnet-Cronin, Jenifer Mason, Joshua Maksimovic, Russell Hope, Alison Holmes, David Simpson, Radoslaw Poplawski, Amelia Joseph, Erwan Acheson, James Bonfield, Mara K. N. Lawniczak, Sascha Ott, Lesley-Anne Williams, Jessica Lynch, Graham P. Taylor, Anita Kenyon, Elizabeth Wastenge, Megan Mayhew, Adhyana I K Mahanama, Stavroula F Louka, Chloe Bishop, Esther Robinson, Darren Smith, Anne M. Presanis, Matthew Carlile, Thomas D Stanton, Dennis Wang, Katerina Galai, Adam P Westhorpe, Flavia Flaviani, Michelle Wantoch, Max Whiteley, Yann Bourgeois, Matthew Gemmell, Mary Ramsay, A Lloyd, Simon Thelwall, Hannah C. Howson-Wells, Joseph G. Chappell, Steve Paterson, Gary Eltringham, Robert Impey, Siddharth Mookerjee, Steven Platt, Emma Swindells, Laura Letchford, Alex Alderton, Lee Graham, Safiah Afifi, David C. Lee, Cassie Breen, Melisa Louise Fenton, Benita Percival, Adrian W Signell, Tanya Golubchik, Ian B Vipond, Eleri Wilson-Davies, Angie Lackenby, Laura Atkinson, Sarojini Pandey, Nazreen F. Hadjirin, Michael A Chapman, Huw Gulliver, Joana Dias, Grant Hall, Antony D Hale, Hassan Hartman, Alp Aydin, Louise Smith, Ashok Dadrah, Johnny Debebe, Sarah Walsh, Stephanie W. Lo, Andrew Bosworth, Bridget Knight, Hannah E Bridgewater, Nadua Bayzid, Gemma L. Kay, Richard Gregory, Sally Kay, Ellena Brooks, Andre Charlett, Georgina M McManus, Riaz Jannoo, Victoria Blakey, Carol Scott, Rachel Nelson, Liz Ratcliffe, Gerry Tonkin-Hill, Verity Hill, Joanne D. Stockton, Danielle Leek, Steven Leonard, Stephanie Hutchings, Jonathan D. Moore, Kathryn Ann Harris, Sophie Jones, Venkat Sivaprakasam, Amy Plimmer, Tanzina Haque, Katherine L. Bellis, Khalil Abudahab, Dianne Irish-Tavares, Gaia Nebbia, Kathryn A Jackson, Stephen W Attwood, Daniel Mair, Sreenu Vattipally, Susanne Stonehouse, Ian Merrick, Lucille Rainbow, Mathew A. Beale, Angela Helen Beckett, Ember Hilvers, Thomas Helmer, Jenna Nichols, Giselda Bucca, Salman Goudarzi, Christopher Ruis, Surendra Parmar, Angela Cowell, Alberto C Cerda, Divya K. Shah, Judith Heaney, E. Thomson, Kyriaki Nomikou, Nicole Pacchiarini, Katherine L Harper, Fatima Downing, M. Estée Török, Michelle L Michelsen, Aaron R. Jeffries, Jennifer Collins, Christopher Williams, Katie F. Loveson, Steven Rudder, Theocharis Tsoleridis, Robert Davies, David Robertson, Katherine Smollett, Kathryn McCluggage, Liam Crawford, Inigo Martincorena, Charlotte Beaver, Oliver Megram, Karla Spellman, Sam Haldenby, Emma Betteridge, William D. Fuller, Will P. M. Rowe, Cherian Koshy, Tim E. A. Peto, Alison Cox, Natasha Johnson, Tanya Curran, Sharif Shaaban, Tamyo Mbisa, Cordelia Langford, Eric Witele, Andrew J. Page, Christoph Puethe, Nicola Reynolds, Paul W Bird, Louise Aigrain, Ronan Lyons, Amy Trebes, Sally Corden, Steven Rushton, Jack Cd Lee, Jane Greenaway, Hibo Asad, Amanda Bradley, Mohammed O Hassan-Ibrahim, Shane McCarthy, Fei Sang, Matthew Loose, Hannah Jones, Keith D. James, Chloe L Fisher, Chrystala Constantinidou, Alex G. Richter, Jane A. H. Masoli, Michael Gallagher, Vicki M. Fleming, Anna Price, Amy Ash, Michaela John, Alex Zarebski, Fenella D. Halstead, John Danesh, Christine Kitchen, Aminu S Jahun, Mark Whitehead, Julianne R Brown, Catherine Bresner, Marius Cotic, Stefanie V Lensing, Nick Levene, Louissa R Macfarlane-Smith, Wendy Hogsden, Cressida Auckland, Eleanor Drury, Richard Eccles, Jennifer Hart, Seema Nickbakhsh, Alisha Davies, David M. Aanensen, Shirelle Burton-Fanning, Ben Farr, Buddhini Samaraweera, Sarah Wyllie, Hannah Lowe, Richard J. Orton, Martin D. Curran, Carol Churcher, Karen Oliver, Elihu Aranday-Cortes, Wen Yew, Thanh Le-Viet, Matthew Parker, Katherine A Twohig, Shahjahan Miah, Samuel M. Nicholls, G MacIntyre-Cockett, Tranprit Saluja, Charlotte Nelson, Vicki Chalker, Roberto Amato, Ellen Higginson, Timothy M. Freeman, Christopher W Holmes, Yasmin Chaudhry, Elias Allara, Alec Birchley, Iraad Bronner, Emma Moles-Garcia, Angus I. Best, Anna L. Casey, Audrey Farbos, Nicholas W Machin, David W Eyre, Tim Boswell, Charlotte A Williams, Elen De Lacy, Matthew J. Bull, Matilde Mori, Carmen F. Manso, Peijun Zhang, Sahar Eldirdiri, Dimitris Grammatopoulos, Corin Yeats, Claudia Wierzbicki, David G Partridge, Kordo Saeed, Nichola Duckworth, David J. Studholme, Harmeet K Gill, Juan Ledesma, Thomas R. A. Davis, Sushmita Sridhar, Clive Graham, Husam Osman, Julian A. Hiscox, Helen Adams, Christopher Fearn, Fabrícia F. Nascimento, Ulf Schaefer, James W. Harrison, Andrew J. Nelson, Joshua Quick, Mohammad Tauqeer Alam, Liam Prestwood, Nikos Manesis, Julian Tang, Justin O'Grady, Sophia T Girgis, Louise Berry, Gemma Clark, Marina Escalera Zamudio, Karlie Fallon, Tim J Sloan, Joanne Watkins, Clare Pearson, Andrew D Beggs, Rachel Williams, Luke Bedford, Trevor Robinson, Nicholas M Redshaw, Richard Hopes, Mirko Menegazzo, Katherine Twohig, Gabrielle Vernet, Steven Liggett, Mariateresa de Cesare, Derrick W. Crook, Dominic P. Kwiatkowski, Mark Kristiansen, Miren Iturriza-Gomara, Christopher I. Moore, Claire Cormie, Olivia Boyd, Nikki Smith, Noel Craine, Kathleen A. Williamson, John Boyes, Sian Ellard, Cristina V. Ariani, Wendy Chatterton, David Bonsall, Kevin Lewis, David Jorgensen, Ian Harrison, Christopher Jackson, Martin P McHugh, Danni Weldon, Michael A. Quail, Amita Patel, Lily Geidelberg, Myra Hosmillo, Judith Breuer, Cariad Evans, Edward Barton, Trudy Workman, Derek Fairley, Vineet Patel, Daniel Bradshaw, Robin Manley, Scott Aj Thurston, John Sillitoe, Monique Andersson, Sharon J. Peacock, Jamie Lopez-Bernal, Thomas Thompson, Nabil-Fareed Alikhan, Ben Temperton, Paul Baker, Robin J Moll, Laura Gifford, Nicholas J. Loman, Jayna Raghwani, Jacqui Prieto, Andrew Hesketh, Oliver G. Pybus, Adela Alcolea-Medina, David Buck, Gregory R Young, Alistair C. Darby, Sónia Gonçalves, Aileen G. Rowan, Tabitha Mahungu, Nicholas Ellaby, Jon-Paul Keatley, Lily Tong, Robert Beer, Martyn Guest, Lisa J Levett, Ali R Awan, Iliana Georgana, Paul E Brown, Li Xu-McCrae, Stephen P. Kidd, Sara Rey, Shazaad Ahmad, Danielle C. Groves, Tetyana I. Vasylyeva, David F. Bibby, Nathan Moore, Fiona Ashcroft, Igor Starinskij, Hannah Paul, Claire McMurray, Michael Spencer Chapman, Carlos Balcazar, Joanna Warwick-Dugdale, Pinglawathee Madona, Edith Vamos, Lesley Shirley, Kate Templeton, Luke Foulser, Igor Siveroni, Ewan M. Harrison, Sian Morgan, Diana Rajan, S Taylor, Laia Fina, Naomi Park, Sarah J. O'Brien, Alessandro M Carabelli, Angela Marchbank, Sunando Roy, Leonardo de Oliveira Martins, Steve Palmer, Jonathan Hubb, Alexander J Keeley, Francesc Coll, Malorie Perry, Paul J. Parsons, Anthony Underwood, Patawee Asamaphan, William L Hamilton, Tommy Nyberg, Sophie Palmer, Amanda Symmonds, Anoop Chauhan, Robert Johnson, Christopher J. R. Illingworth, James Shepherd, Wendy Smith, Rich Livett, Rachel Blacow, Margaret Hughes, Jeremy Mirza, Joanne Watts, Jonathan D. Edgeworth, Sarah François, Sue Edwards, Adrienn Angyal, Thomas N. Williams, Marta Gallis, Lauren Gilbert, Paul Randell, Kate Johnson, Eileen Gallagher, Nick Cortes, Yusri Taha, Leah Ensell, Emanuela Pelosi, Stefan Rooke, Michelle Lister, Ana da Silva Filipe, Cassandra S Malone, Themoula Charalampous, Benjamin B Lindsey, Natalie Groves, Colin Smith, Ross J Harris, Rebekah E Wilson, Stephen Bonner, Richard Stark, Sharon Campbell, Nicola Sheriff, Helen L Lowe, Rachel Jones, Ben Warne, Rose K Davidson, Declan Bradley, Ian Johnston, Jeffrey C. Barrett, Joshua B Singer, Shirin Aliabadi, Andrew Whitwham, Patrick McClure, Samuel Robson, Sharon Glaysher, Robert J. Munn, Emma L. Wise, Laura Baxter, Kim S Smith, Catherine Moore, Bree Gatica-Wilcox, Alice Broos, Sarah Essex, David Baker, Manjinder Khakh, Dorota Jamrozy, Rachel Tucker, Ian Goodfellow, S.E. Moses, Nicola Cumley, Robin Howe, Meera Chand, James I. Price, Marina Gourtovaia, Debra Padgett, Jaime Tovar-Corona, Stephen L. Michell, Matthew J. Dorman, Lizzie Meadows, David Heyburn, Iona Willingham, Rocio Martinez Nunez, Grace Taylor-Joyce, Claire M Bewshea, Anita Justice, Simon Cottrell, Rebecca C H Brown, Jamie Young, Gavin Dabrera, Matthew Wyles, Stephen Carmichael, Lisa Berry, Frances Bolt, Andrew Rambaut, Samir Dervisevic, Erik M. Volz, Rahul Batra, Caoimhe McKerr, Samantha McGuigan, Katie Jones, Mailis Maes, Rebecca Dewar, Mary Sinnathamby, Joel Southgate, and Lynn Monaghan
Subjects: medicine.medical_specialty, business.industry, Proportional hazards model, Public health, Hazard ratio, Attendance, C500, Vaccination, Infectious Diseases, Relative risk, Internal medicine, Cohort, medicine, business, Cohort study
Abstract: Background: \ud The SARS-CoV-2 delta (B.1.617.2) variant was first detected in England in March, 2021. It has since rapidly become the predominant lineage, owing to high transmissibility. It is suspected that the delta variant is associated with more severe disease than the previously dominant alpha (B.1.1.7) variant. We aimed to characterise the severity of the delta variant compared with the alpha variant by determining the relative risk of hospital attendance outcomes.\ud \ud Methods: \ud This cohort study was done among all patients with COVID-19 in England between March 29 and May 23, 2021, who were identified as being infected with either the alpha or delta SARS-CoV-2 variant through whole-genome sequencing. Individual-level data on these patients were linked to routine health-care datasets on vaccination, emergency care attendance, hospital admission, and mortality (data from Public Health England's Second Generation Surveillance System and COVID-19-associated deaths dataset; the National Immunisation Management System; and NHS Digital Secondary Uses Services and Emergency Care Data Set). The risk for hospital admission and emergency care attendance were compared between patients with sequencing-confirmed delta and alpha variants for the whole cohort and by vaccination status subgroups. Stratified Cox regression was used to adjust for age, sex, ethnicity, deprivation, recent international travel, area of residence, calendar week, and vaccination status.\ud \ud Findings: \ud Individual-level data on 43 338 COVID-19-positive patients (8682 with the delta variant, 34 656 with the alpha variant; median age 31 years [IQR 17–43]) were included in our analysis. 196 (2·3%) patients with the delta variant versus 764 (2·2%) patients with the alpha variant were admitted to hospital within 14 days after the specimen was taken (adjusted hazard ratio [HR] 2·26 [95% CI 1·32–3·89]). 498 (5·7%) patients with the delta variant versus 1448 (4·2%) patients with the alpha variant were admitted to hospital or attended emergency care within 14 days (adjusted HR 1·45 [1·08–1·95]). Most patients were unvaccinated (32 078 [74·0%] across both groups). The HRs for vaccinated patients with the delta variant versus the alpha variant (adjusted HR for hospital admission 1·94 [95% CI 0·47–8·05] and for hospital admission or emergency care attendance 1·58 [0·69–3·61]) were similar to the HRs for unvaccinated patients (2·32 [1·29–4·16] and 1·43 [1·04–1·97]; p=0·82 for both) but the precision for the vaccinated subgroup was low.\ud \ud Interpretation: \ud This large national study found a higher hospital admission or emergency care attendance risk for patients with COVID-19 infected with the delta variant compared with the alpha variant. Results suggest that outbreaks of the delta variant in unvaccinated populations might lead to a greater burden on health-care services than the alpha variant.\ud \ud Funding: \ud Medical Research Council; UK Research and Innovation; Department of Health and Social Care; and National Institute for Health Research.
Published: 2022

38. Mapping environmental suitability of Haemagogus and Sabethes spp. mosquitoes to understand sylvatic transmission risk of yellow fever virus in Brazil

Author: Sabrina L. Li, André L. Acosta, Sarah C. Hill, Oliver J. Brady, Marco A. B. de Almeida, Jader da C. Cardoso, Arran Hamlet, Luis F. Mucci, Juliana Telles de Deus, Felipe C. M. Iani, Neil S. Alexander, G. R. William Wint, Oliver G. Pybus, Moritz U. G. Kraemer, Nuno R. Faria, Jane P. Messina, Medical Research Council-São Paulo Research Foundation (FAPESP), and Wellcome Trust
Subjects: Environmental Impacts, SELECTION, Epidemiology, RC955-962, Forests, Disease Vectors, Mosquitoes, Geographical locations, Trees, Medical Conditions, Arctic medicine. Tropical medicine, INFECTION, Medicine and Health Sciences, 11 Medical and Health Sciences, Ecology, Eukaryota, Plants, Terrestrial Environments, ATLANTIC FOREST, ABSENCE, Insects, Infectious Diseases, OVIPOSITION, Host-Pathogen Interactions, Yellow fever virus, Public aspects of medicine, RA1-1270, Life Sciences & Biomedicine, Brazil, Research Article, Arthropoda, VECTOR, Mosquito Vectors, Disease Surveillance, Ecosystems, DIPTERA-CULICIDAE, Population Metrics, Species Specificity, Tropical Medicine, Yellow Fever, Animals, Population Density, Science & Technology, Population Biology, Ecology and Environmental Sciences, Public Health, Environmental and Occupational Health, Organisms, Biology and Life Sciences, South America, 06 Biological Sciences, Invertebrates, Insect Vectors, Species Interactions, Culicidae, Medical Risk Factors, PATTERNS, Parasitology, DEFORESTATION, People and places, Zoology, Entomology
Abstract: Background Yellow fever (YF) is an arboviral disease which is endemic to Brazil due to a sylvatic transmission cycle maintained by infected mosquito vectors, non-human primate (NHP) hosts, and humans. Despite the existence of an effective vaccine, recent sporadic YF epidemics have underscored concerns about sylvatic vector surveillance, as very little is known about their spatial distribution. Here, we model and map the environmental suitability of YF’s main vectors in Brazil, Haemagogus spp. and Sabethes spp., and use human population and NHP data to identify locations prone to transmission and spillover risk. Methodology/Principal findings We compiled a comprehensive set of occurrence records on Hg. janthinomys, Hg. leucocelaenus, and Sabethes spp. from 1991–2019 using primary and secondary data sources. Linking these data with selected environmental and land-cover variables, we adopted a stacked regression ensemble modelling approach (elastic-net regularized GLM, extreme gradient boosted regression trees, and random forest) to predict the environmental suitability of these species across Brazil at a 1 km x 1 km resolution. We show that while suitability for each species varies spatially, high suitability for all species was predicted in the Southeastern region where recent outbreaks have occurred. By integrating data on NHP host reservoirs and human populations, our risk maps further highlight municipalities within the region that are prone to transmission and spillover. Conclusions/Significance Our maps of sylvatic vector suitability can help elucidate potential locations of sylvatic reservoirs and be used as a tool to help mitigate risk of future YF outbreaks and assist in vector surveillance. Furthermore, at-risk regions identified from our work could help disease control and elucidate gaps in vaccination coverage and NHP host surveillance., Author summary Yellow fever virus (YFV) is an arbovirus transmitted to humans from mosquitoes and can lead to severe disease and death. Recent sporadic outbreaks coupled with low vaccination coverage have highlighted the importance of mosquito surveillance for preventing future outbreaks and potential virus spillover into dense urban areas. Yet, very little is known about the spatial distribution of mosquitoes known to transmit YFV and the factors that contribute to their environmental suitability in Brazil. We compiled an occurrence database of primary and secondary mosquito vectors belonging to Haemagogus and Sabethes species’ collected between 1991–2019 and integrated this data with environmental and land-use data to predict their spatial suitability at 1 km x 1 km resolution. Using this information, we identified suitable regions for their co-existence. We overlaid this information with human population density and locations of non-human primate host reservoirs to identify areas at risk of transmission and spillover. Our study provides high-resolution mapping tools to assist with mosquito and arbovirus surveillance which is especially useful in low-resource settings.
Published: 2022

39. Travel time to health facilities in areas of outbreak potential: maps for guiding local preparedness and response

Author: Nancy Fullman, Brent Bell, Robert C. Reiner, Judith N. Wasserheit, E. N. Hulland, Peter M. Rabinowitz, Daniel J. Weiss, Shreya Shirude, David M. Pigott, Tamer H. Farag, Kirsten E. Wiens, Vandana Gupta, Simon I. Hay, Molly K. Miller-Petrie, Julia D. Morgan, Moritz U. G. Kraemer, and Amelia Bertozzi-Villa
Subjects: medicine.medical_specialty, Time Factors, Geospatial analysis, Travel time, Preparedness, 030231 tropical medicine, lcsh:Medicine, computer.software_genre, Disease Outbreaks, 03 medical and health sciences, Marburg, 0302 clinical medicine, Spillover effect, Health facility, CCHF, Humans, Medicine, 030212 general & internal medicine, Environmental planning, Travel, business.industry, Public health, lcsh:R, Civil Defense, Outbreak, Health facility access, General Medicine, Hemorrhagic Fevers, Ebola, Resource allocation, Health Facilities, business, computer, Viral hemorrhagic fevers, Research Article, Lassa fever
Abstract: Background Repeated outbreaks of emerging pathogens underscore the need for preparedness plans to prevent, detect, and respond. As countries develop and improve National Action Plans for Health Security, addressing subnational variation in preparedness is increasingly important. One facet of preparedness and mitigating disease transmission is health facility accessibility, linking infected persons with health systems and vice versa. Where potential patients can access care, local facilities must ensure they can appropriately diagnose, treat, and contain disease spread to prevent secondary transmission; where patients cannot readily access facilities, alternate plans must be developed. Here, we use travel time to link facilities and populations at risk of viral hemorrhagic fevers (VHFs) and identify spatial variation in these respective preparedness demands. Methods and findings We used geospatial resources of travel friction, pathogen environmental suitability, and health facilities to determine facility accessibility of any at-risk location within a country. We considered in-country and cross-border movements of exposed populations and highlighted vulnerable populations where current facilities are inaccessible and new infrastructure would reduce travel times. We developed profiles for 43 African countries. Resulting maps demonstrate gaps in health facility accessibility and highlight facilities closest to areas at risk for VHF spillover. For instance, in the Central African Republic, we identified travel times of over 24 h to access a health facility. Some countries had more uniformly short travel times, such as Nigeria, although regional disparities exist. For some populations, including many in Botswana, access to areas at risk for VHF nationally was low but proximity to suitable spillover areas in bordering countries was high. Additional analyses provide insights for considering future resource allocation. We provide a contemporary use case for these analyses for the ongoing Ebola outbreak. Conclusions These maps demonstrate the use of geospatial analytics for subnational preparedness, identifying facilities close to at-risk populations for prioritizing readiness to detect, treat, and respond to cases and highlighting where gaps in health facility accessibility exist. We identified cross-border threats for VHF exposure and demonstrate an opportunity to improve preparedness activities through the use of precision public health methods and data-driven insights for resource allocation as part of a country’s preparedness plans.
Published: 2019

40. Recommended reporting items for epidemic forecasting and prediction research: the EPIFORGE 2020 guidelines

Author: Caitlin Rivers, Rachel Lowe, Oliver J. Brady, Michael A. Johansson, Srinivasan Venkatramanan, Travis C. Porco, Moritz U. G. Kraemer, Benjamin M. Althouse, Rachel Sippy, Suzanne E Mate, Steven Riley, Nicholas G. Reich, Alina Deshpande, Matthew Biggerstaff, Willem G. van Panhuis, Irina Maljkovic Berry, Wirichada Pan-Ngum, Talia M. Quandelacy, Cécile Viboud, Sasikiran Kandula, David L. Blazes, Jacob D Ball, David M. Brett-Major, Julie A. Pavlin, Sara Y. Del Valle, Lindsay Morton, Jeff Morgan, Sheetal Silal, Alessandro Vespigiani, Simon Pollett, and University of St Andrews. Statistics
Subjects: Reproducibility of results, Research design, Biomedical Research, Medical Journals, Epidemics/statistics & numerical data, Epidemiology, Delphi method, Population Modeling, Checklist/methods, Guidelines and Guidance, Mathematical and Statistical Techniques, Medical Conditions, Electronics Engineering, RA0421, RA0421 Public health. Hygiene. Preventive Medicine, Medicine and Health Sciences, Public and Occupational Health, health care economics and organizations, COVID-19/epidemiology, Communicable disease, Statistics, General Medicine, Research Assessment, Medical research, Checklist, Infectious Diseases, Systematic review, Research Design, Physical Sciences, Comparators, Research Reporting Guidelines, Engineering and Technology, Medicine, Psychology, Guidelines as topic/standards, education, NDAS, Guidelines as Topic, Research and Analysis Methods, Communicable Diseases, Infectious Disease Epidemiology, SDG 3 - Good Health and Well-being, Biomedical research/methods, Humans, Statistical Methods, Epidemics, Actuarial science, Population Biology, End user, COVID-19, Reproducibility of Results, Biology and Life Sciences, Computational Biology, Communicable diseases/epidemiology, Guideline, Forecasting/methods, Electronics, Infectious Disease Modeling, Medical Humanities, Mathematics, Forecasting
Abstract: Background The importance of infectious disease epidemic forecasting and prediction research is underscored by decades of communicable disease outbreaks, including COVID-19. Unlike other fields of medical research, such as clinical trials and systematic reviews, no reporting guidelines exist for reporting epidemic forecasting and prediction research despite their utility. We therefore developed the EPIFORGE checklist, a guideline for standardized reporting of epidemic forecasting research. Methods and findings We developed this checklist using a best-practice process for development of reporting guidelines, involving a Delphi process and broad consultation with an international panel of infectious disease modelers and model end users. The objectives of these guidelines are to improve the consistency, reproducibility, comparability, and quality of epidemic forecasting reporting. The guidelines are not designed to advise scientists on how to perform epidemic forecasting and prediction research, but rather to serve as a standard for reporting critical methodological details of such studies. Conclusions These guidelines have been submitted to the EQUATOR network, in addition to hosting by other dedicated webpages to facilitate feedback and journal endorsement., Simon Pollett and co-workers describe EPIFORGE, a guideline for reporting research on epidemic forecasting.
Published: 2021

41. Track Omicron's spread with molecular data

Author: Lesley Scott, Nei-yuan Hsiao, Sikhuline Moyo, Lavanya Singh, Houriiyah Tegally, Graeme Dor, Piet Maes, Oliver G. Pybus, Moritz U. G. Kraemer, Elizaveta Semenova, Samir Bhatt, Seth Flaxman, Nuno R. Faria, Tulio de Oliveira, Medical Research Council (MRC), Medical Research Council-São Paulo Research Foundation (FAPESP), Wellcome Trust, and Bill & Melinda Gates Foundation
Subjects: Multidisciplinary, Science & Technology, Whole Genome Sequencing, General Science & Technology, SARS-CoV-2, COVID-19, Genome, Viral, Multidisciplinary Sciences, COVID-19 Nucleic Acid Testing, Databases, Genetic, Spike Glycoprotein, Coronavirus, Prevalence, Science & Technology - Other Topics, Humans, Sequence Deletion
Abstract: On 26 November, the newly emerged variant Omicron was designated a severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variant of concern (VOC) (1). Rapid polymerase chain reaction (PCR) test results could improve estimates of the prevalence of Omicron around the world. The widely used Thermo Fisher TaqPath COVID-19 PCR assay was valuable in tracking the spread of the Alpha (B.1.1.7) VOC (2) because a deletion of amino acids 69 and 70 in Alpha’s spike gene (Δ69–70) yields a distinct absent S-gene (S−) despite positive test results. The Delta VOC lacks this deletion and is therefore S-gene positive (S+) on TaqPath PCR tests (3). The Omicron VOC shares the spike Δ69–70 deletion with Alpha, which has dropped to negligible levels worldwide. Therefore, the frequency of S− results can be used as a rapid proxy for the frequency of Omicron cases, provided initial detection of local circulation had been confirmed by sequencing.
Published: 2021

42. Monitoring key epidemiological parameters of SARS-CoV-2 transmission

Author: Moritz U G, Kraemer, Oliver G, Pybus, Christophe, Fraser, Simon, Cauchemez, Andrew, Rambaut, and Benjamin J, Cowling
Subjects: Infection Control, Time Factors, Virulence, SARS-CoV-2, COVID-19, Virus Replication, Publisher Correction, Databases, Databases as Topic, Risk Factors, Viral infection, Population Surveillance, Humans, Contact Tracing, Pandemics
Published: 2021

43. Tracking the 2022 monkeypox outbreak with epidemiological data in real-time

Author: Moritz U G Kraemer, Houriiyah Tegally, David M Pigott, Abhishek Dasgupta, James Sheldon, Eduan Wilkinson, Marinanicole Schultheiss, Aimee Han, Mark Oglia, Spencer Marks, Joshua Kanner, Katelynn O'Brien, Sudheer Dandamudi, Benjamin Rader, Kara Sewalk, Ana I Bento, Samuel V Scarpino, Tulio de Oliveira, Isaac I Bogoch, Rebecca Katz, and John S Brownstein
Subjects: Infectious Diseases, Humans, Monkeypox, Monkeypox virus, Disease Outbreaks
Published: 2022

44. Sharing, synthesis and sustainability of data analysis for epidemic preparedness in Europe

Author: Moritz U. G. Kraemer, Emma B. Hodcroft, and Adam J. Kucharski
Subjects: Geography, Oncology, 360 Social problems & social services, Health Policy, Preparedness, Sustainability, Internal Medicine, 610 Medicine & health, Series Viewpoint, Environmental planning
Published: 2021

45. Arboviral diseases and poverty in Alabama, 2007–2017

Author: Moritz U. G. Kraemer, Donal Bisanzio, Richard Reithinger, Elisa Martello, Katherine Izenour, Kelly Stevens, Benjamin A. McKenzie, Ramandeep Kaur, and Sarah Zohdy
Subjects: RNA viruses, Viral Diseases, Eastern Equine Encephalitis Virus, Eastern equine encephalitis virus, viruses, RC955-962, Dengue virus, medicine.disease_cause, Geographical locations, Zika virus, 0302 clinical medicine, Medical Conditions, Arctic medicine. Tropical medicine, Public and Occupational Health, 030212 general & internal medicine, Chikungunya, Pathology and laboratory medicine, education.field_of_study, Chikungunya Virus, biology, virus diseases, Medical microbiology, Socioeconomic Aspects of Health, Geography, Infectious Diseases, Arboviral Infections, Viruses, Saint Louis encephalitis, Alabama, Public Health, Public aspects of medicine, RA1-1270, Pathogens, West Nile virus, Research Article, medicine.medical_specialty, Alphaviruses, 030231 tropical medicine, Population, Arbovirus Infections, Environment, Microbiology, Virus, Togaviruses, 03 medical and health sciences, Environmental health, medicine, Humans, education, Poverty, Medicine and health sciences, Biology and life sciences, Flaviviruses, Public health, Public Health, Environmental and Occupational Health, Organisms, Viral pathogens, Zika Virus, Dengue Virus, medicine.disease, biology.organism_classification, United States, Microbial pathogens, Health Care, Socioeconomic Factors, North America, People and places, Arboviruses
Abstract: Mosquito-borne viruses cause diseases of great public health concern. Arboviral disease case distributions have complex relationships with socioeconomic and environmental factors. We combined information about socio-economic (population, and poverty rate) and environmental (precipitation, and land use) characteristics with reported human cases of arboviral disease in the counties of Alabama, USA, from 2007–2017. We used county level data on West Nile virus (WNV), dengue virus (DENV), chikungunya virus (CHIKV), Zika virus (ZIKV), California serogroup virus, Eastern equine encephalitis virus, and Saint Louis encephalitis virus to provide a detailed description of their spatio-temporal pattern. We found a significant spatial convergence between incidence of WNV and poverty rate clustered in the southern part of Alabama. DENV, CHIKV and ZIKV cases showed a different spatial pattern, being mostly located in the northern part, in areas of high socioeconomic status. The results of our study establish that poverty-driven inequities in arboviral risk exist in the southern USA, and should be taken into account when planning prevention and intervention strategies., Author summary Mosquito-borne arboviruses like West Nile virus (WNV), dengue virus (DENV), chikungunya virus (CHIKV), Zika virus (ZIKV), California serogroup virus (CSV), Eastern equine encephalitis virus (EEE), and Saint Louis encephalitis virus (SLE) are on the rise globally. Socioeconomic and environmental conditions have played a role in directing in this expansion by creating conditions ideal for mosquito vectors and transmission. In this study, we used 10 years (2007–2017) of county level human arboviral case data from the US state of Alabama to better understand the roles socioeconomics (poverty rate) and environmental (land use, precipitation, land cover) conditions may play in driving patterns of arboviral disease in the southern US. We found a significant association between poverty rate and incidence of WNV, an arbovirus primarily transmitted by Culex spp. mosquitoes, which are known for thriving in contaminated water sources and sewage overflow. Conversely, cases of DENV, CHIKV, and ZIKV, arboviruses primarily transmitted by Aedes spp. mosquitoes, were reported in areas of high socioeconomic status. These findings suggest differential distribution of arboviruses relevant to human health in Alabama, and that poverty in the southern US is a significant factor that should be considered when planning WNV prevention and intervention strategies.
Published: 2021

46. Progress and challenges in virus genomic epidemiology

Author: Moritz U. G. Kraemer, Verity Hill, Sumali Bajaj, Christopher Ruis, and Oliver G. Pybus
Subjects: Disease surveillance, Computer science, Genomic data, Genome, Viral, Genomics, Data science, Genome, DNA sequencing, law.invention, Disease Outbreaks, Metadata, ComputingMethodologies_PATTERNRECOGNITION, Infectious Diseases, Transmission (mechanics), law, Key (cryptography), Parasitology, Disease transmission
Abstract: Genomic epidemiology, which links pathogen genomes with associated metadata to understand disease transmission, has become a key component of outbreak response. Decreasing costs of genome sequencing and increasing computational power provide opportunities to generate and analyse large viral genomic datasets that aim to uncover the spatial scales of transmission, the demographics contributing to transmission patterns, and to forecast epidemic trends. Emerging sources of genomic data and associated metadata provide new opportunities to further unravel transmission patterns. Key challenges include how to integrate genomic data with metadata from multiple sources, how to generate efficient computational algorithms to cope with large datasets, and how to establish sampling frameworks to enable robust conclusions.
Published: 2021

47. Tracking the international spread of SARS-CoV-2 lineages B.1.1.7 and B.1.351/501Y-V2 with grinch

Author: Darío García de Viedma, Angelica Bianco, Torsten Seemann, Nikita Sahadeo, Orna Mor, Richard R. Lessells, Christian Drosten, Nikki E. Freed, Aekyung Park, Chuan Kok Lim, Michal Mandelboim, Sanmarié Schlebusch, Georgi Merhi, Tanja Stadler, Kamran Khan, Ira Deveson, Joep de Ligt, Kirsten St. George, Nuno R. Faria, Mailie Gall, Matthew Storey, Chitra Pattabiraman, Teemu Smura, Andrew Rambaut, Tamara Salloum, Antonio Parisi, Sureshnee Pillay, Erica Lasek-Nesselquist, Ravi Vasanthapuram, Issac I. Bogoch, Xiaoyun Ren, Dirk Eggink, Jamie McMahon, Reina S. Sikkema, Satu Kurkela, William D. Rawlinson, Jennifer Giandhari, Maitshwarelo Matsheka, Tulio de Oliveira, Daniela Loconsole, Laurence Josset, Malebogo Kebabonye, Gape Nyepetsi, V. Sudha rani, Joana Isidro, Madisa Mine, Lars Steinbrück, Albert Heim, Áine O'Toole, Pramada Prasad, Martina Rueca, Alexander Watts, Chantal Reusken, Lex E. X. Leong, SeqCOVID-Spain, Marion Koopmans, Hamad Hassan, Antonin Bal, Alicia Arnott, Search Alliance San Diego, Jenny Draper, Vítor Borges, Houriiyah Tegally, Jemma L. Geoghegan, Jane P. Messina, Massab Umair, Neta S. Zuckerman, Sílvia Duarte, Olin K. Silander, Jad Koweyes, Mia Brytting, Maria Chironna, Anita Desai, S. Pavani, Harry Vennema, Verity Hill, Michael J. Carr, Kefentse Arnold Tumedi, Sergio Buenestado-Serrano, Thomas F. Schulz, Barbara Bartolini, Gabo Gonzalez, Bas B. Oude Munnink, Thorsten Wolff, Oliver G. Pybus, Edward C. Holmes, Christine V.F. Carrington, João Paulo Gomes, Moritz U. G. Kraemer, Muhammad Salman, Sima Tokajian, and Virology
Subjects: Infecções Respiratórias, genomic surveillance, air travel, SARS-CoV-2, Genomics, genome sequencing, Virus, Surveillance, Pandemic, B.1.1.7, B.1.351, N501Y, Coronavirus, Sequencing, genomic epidemiology, viruses, Lineage (evolution), Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), Medicine (miscellaneous), medicine.disease_cause, General Biochemistry, Genetics and Molecular Biology, DNA sequencing, 03 medical and health sciences, 0302 clinical medicine, medicine, Genome Sequencing, Genomic Epidemiology, 030304 developmental biology, 0303 health sciences, Genomic Surveillance, Haplotype, virus diseases, COVID-19, 3. Good health, Geography, Air Travel, Evolutionary biology, Biological dispersal, 030217 neurology & neurosurgery
Abstract: Late in 2020, two genetically-distinct clusters of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) with mutations of biological concern were reported, one in the United Kingdom and one in South Africa. Using a combination of data from routine surveillance, genomic sequencing and international travel we track the international dispersal of lineages B.1.1.7 and B.1.351 (variant 501YV2). We account for potential biases in genomic surveillance efforts by including passenger volumes from location of where the lineage was first reported, London and South Africa respectively. Using the software tool grinch (global report investigating novel coronavirus haplotypes), we track the international spread of lineages of concern with automated daily reports, Further, we have built a custom tracking website (cov-lineages.org/global_report.html) which hosts this daily report and will continue to include novel SARS-CoV-2 lineages of concern as they are detected., Wellcome Open Research, 6
Published: 2021

48. Global patterns of aegyptism without arbovirus

Author: Moritz U. G. Kraemer, Jose G. Juarez, Mark F. Olson, Gabriel L. Hamer, and Jane P. Messina
Subjects: RNA viruses, Viral Diseases, Atmospheric Science, Epidemiology, Economics, viruses, Rain, Climate, RC955-962, Social Sciences, Dengue virus, Disease Vectors, medicine.disease_cause, Pathology and Laboratory Medicine, Population density, Mosquitoes, Dengue fever, Dengue Fever, Dengue, Medical Conditions, Aedes, Arctic medicine. Tropical medicine, Medicine and Health Sciences, Chikungunya, biology, Ecology, virus diseases, Eukaryota, Insects, Geography, Infectious Diseases, Medical Microbiology, Viral Pathogens, Viruses, Public aspects of medicine, RA1-1270, Pathogens, Research Article, Neglected Tropical Diseases, Arthropoda, Aedes aegypti, Mosquito Vectors, Aedes Aegypti, Arbovirus, Microbiology, Meteorology, Population Metrics, medicine, Animals, Humans, Microbial Pathogens, Ecosystem, Population Density, Population Biology, Flaviviruses, Public Health, Environmental and Occupational Health, Organisms, Outbreak, Biology and Life Sciences, Dengue Virus, biology.organism_classification, medicine.disease, Tropical Diseases, Invertebrates, Economic Analysis, Insect Vectors, Species Interactions, Socioeconomic Factors, Vector (epidemiology), Medical Risk Factors, Earth Sciences, Zoology, Entomology, Arboviruses
Abstract: The world’s most important mosquito vector of viruses, Aedes aegypti, is found around the world in tropical, subtropical and even some temperate locations. While climate change may limit populations of Ae. aegypti in some regions, increasing temperatures will likely expand its territory thus increasing risk of human exposure to arboviruses in places like Europe, Northern Australia and North America, among many others. Most studies of Ae. aegypti biology and virus transmission focus on locations with high endemicity or severe outbreaks of human amplified urban arboviruses, such as dengue, Zika, and chikungunya viruses, but rarely on areas at the margins of endemicity. The objective in this study is to explore previously published global patterns in the environmental suitability for Ae. aegypti and dengue virus to reveal deviations in the probability of the vector and human disease occurring. We developed a map showing one end of the gradient being higher suitability of Ae. aegypti with low suitability of dengue and the other end of the spectrum being equal and higher environmental suitability for both Ae. aegypti and dengue. The regions of the world with Ae. aegypti environmental suitability and no endemic dengue transmission exhibits a phenomenon we term ‘aegyptism without arbovirus’. We then tested what environmental and socioeconomic variables influence this deviation map revealing a significant association with human population density, suggesting that locations with lower human population density were more likely to have a higher probability of aegyptism without arbovirus. Characterizing regions of the world with established populations of Ae. aegypti but little to no autochthonous transmission of human-amplified arboviruses is an important step in understanding and achieving aegyptism without arbovirus., Author summary The preeminent vector of arboviruses, Aedes aegypti, is distributed globally and capable of transmitting deadly pathogens to over half the world’s population. While most studies focus on areas where Ae. aegypti and human-amplified urban arboviruses such as dengue and Zika viruses are locally established, our study explores the margins of endemicity where Aedes aegypti can be found, but arboviral illness is rare. These areas where we find environmental suitability for the vector but an absence of established arboviral transmission we term ‘aegyptism without arbovirus’. This builds on the long-held observation of ‘anophelism without malaria’ in which some regions having Plasmodium-competent Anopheles spp. mosquitoes but not the associated human malaria. This study uses previously published maps to reveal locations with higher suitability of Ae. aegypti and low suitability of dengue. Additionally, we analyzed the resulting map of deviations between Ae. aegypti and dengue and found significant associations with human population density, infant mortality rate, temperature, and precipitation. To the best of our knowledge, this is the first study to characterize places around the world that exhibit ‘aegyptism without arbovirus’ which is an important first step in our ongoing battle with human-amplified urban arboviruses.
Published: 2021

49. Past and future spread of the arbovirus vectors Aedes aegypti and Aedes albopictus

Author: Donal Bisanzio, Thomas W. Scott, G. R. William Wint, T. Alex Perkins, Nicole Davis Weaver, Lucas Earl, Laurie B. Marczak, Erik Wetter, Hongjie Yu, Shengjie Lai, Dingdong Yi, Simon I. Hay, Wim Van Bortel, Cedric Marsboom, Francis Schaffner, Giovanini E. Coelho, David M. Pigott, Jane P. Messina, Xin Lu, Robert C. Reiner, Linus Bengtsson, Andrew J. Tatem, David L. Smith, Chester G. Moore, Shreya Shirude, Qiyong Liu, Peter A. Jones, Kimberly J. Johnson, Roberta G. Carvalho, John S. Brownstein, Moritz U. G. Kraemer, Oliver J. Brady, Nuno R. Faria, Louis Lambrechts, Nick Golding, Marius Gilbert, Heinrich H. Nax, Guy Hendrickx, Oliver G. Pybus, Simon Cauchemez, Catherine Linard, University of Oxford [Oxford], Boston Children's Hospital, Harvard Medical School [Boston] (HMS), University of Washington [Seattle], London School of Hygiene and Tropical Medicine (LSHTM), Université libre de Bruxelles (ULB), Fonds National de la Recherche Scientifique [Bruxelles] (FNRS), Harvard University [Cambridge], University of Nottingham, UK (UON), Eck Institute for Global Health, University of Notre Dame [Indiana] (UND), Fudan University [Shanghai], University of Southampton, Flowminder Foundation, Central South University [Changsha], National University of Defense Technology [China], Southwestern University of Finance and Economics [Chengdu, China], Waen Associates Ltd, Pan American Health Organization [Washington] (PAHO), Ministry of Health [Brasília, Brazil], European Centre for Disease Prevention and Control (ECDC), Institute of Tropical Medicine [Antwerp] (ITM), Egis Avia (FRANCE), Francis Schaffner Consultancy, Colorado State University [Fort Collins] (CSU), Eidgenössische Technische Hochschule - Swiss Federal Institute of Technology [Zürich] (ETH Zürich), Karolinska Institutet [Stockholm], Stockholm School of Economics (SSE), Interactions Virus-Insectes - Insect-Virus Interactions (IVI), Centre National de la Recherche Scientifique (CNRS)-Institut Pasteur [Paris], Modélisation mathématique des maladies infectieuses - Mathematical modelling of Infectious Diseases, Institut Pasteur [Paris]-Centre National de la Recherche Scientifique (CNRS), Université de Namur [Namur] (UNamur), University of California [Davis] (UC Davis), University of California, Chinese Center for Disease Control and Prevention, Shandong University, University of Melbourne, The authors thank S. Ray for providing comments during the revision process. M.U.G.K. acknowledges funding from the Society in Science, The Branco Weiss Fellowship, administered by the ETH Zurich. M.U.G.K. also acknowledges funding from the Training Grant from the National Institute of Child Health and Human Development (T32HD040128). M.U.G.K., S.I.H., J.P.M., N.G., O.J.B. and G.R.W.W. acknowledge funding from the International Research Consortium on Dengue Risk Assessment Management and Surveillance (IDAMS, European Commission 7th Framework Programme no. 21893). O.B.J. was funded by a Sir Henry Wellcome Fellowship funded by the Wellcome Trust (grant number 206471/Z/17/Z) and a grant from the Bill and Melinda Gates Foundation (OP1183567). S.I.H. received a grant from the Research for Health in Humanitarian Crises (R2HC) Programme, managed by Enhancing Learning and Research for Humanitarian Assistance (ELRHA, no. 13468), which also supported M.U.G.K. and N.G. The R2HC programme aims to improve health outcomes by strengthening the evidence base for public health interventions in humanitarian crises. The £8 million R2HC programme is funded equally by the Wellcome Trust and Department of International Development (DFiD), with ELRHA overseeing the programme’s execution and management. S.I.H. was also funded by a Senior Research Fellowship from the Wellcome Trust (no. 95066) and grants from the Bill & Melinda Gates Foundation (OPP1106023, OPP1093011, OPP1132415 and OPP1159934). This study was made possible by the support of the American people through the US Agency for International Development Emerging Pandemic Threats Program-2 PREDICT-2 (Cooperative Agreement number AID-OAA-A-14-00102), which also supported M.U.G.K. J.S.B. is supported by the National Library of Medicine of the National Institutes of Health (R01LM010812 and R01LM011965), which also supports M.U.G.K. D.L.S. is funded by the National Institutes of Health and National Institute of Allergy and Infectious Diseases (no. U10AI089674). H.H.N. was funded by the European Commission through the European Research Council Advanced Investigator Grant ‘Momentum’ 324247. L.L. received funding from the French Government’s Investissement d’Avenir program, Laboratoire d’Excellence Integrative Biology of Emerging Infectious Diseases (grant ANR-10-LABX-62-IBEID), the French Agence Nationale de la Recherche (grant ANR-16-CE35-0004), the City of Paris Emergence(s) programme in Biomedical Research, and the European Union’s Horizon 2020 research and innovation programme under ZikaPLAN grant agreement No. 734584. S.C. received funding from the AXA Research Fund, the Investissement d’Avenir program, the Laboratoire d’Excellence Integrative Biology of Emerging Infectious Diseases program (Grant ANR-10-LABX-62-IBEID), the Models of Infectious Disease Agent Study of the National Institute of General Medical Sciences, the INCEPTION project (PIA/ANR-16-CONV-0005), and the European Union’s Horizon 2020 research and innovation programme under ZIKAlliance grant agreement No 734548. N.G. is supported by a University of Melbourne McKenzie fellowship. W.V.B., G.H. and F.S. acknowledge funding from VBORNET and VectorNet, an ECDC and EFSA-funded project (no. ECDC/09/018 and OC/EFSA/AHAW/2013/02), and thank all contributing VBORNET and VectorNet experts for data sharing. T.W.S., R.C.R. and L.L. received funding from the National Institutes of Health Program Project grant (no. P01 AI098670). X.L. is supported by the Natural Science Foundation of China (71771213, 71522014, 71725001, 91846301 and 71790615). This work was also partially supported by the European Union’s Horizon 2020 Research and Innovation Programme under ZIKAlliance Grant Agreement no. 734548., ANR-10-LABX-0062,IBEID,Integrative Biology of Emerging Infectious Diseases(2010), ANR-16-CE35-0004,MOSQUIBIOTA,Contribution de la diversité bactérienne intestinale à la capacité vectorielle d'Aedes aegypti(2016), European Project: 734548,ZIKAlliance(2016), European Project: 324247,EC:FP7:ERC,ERC-2012-ADG_20120411,MOMENTUM(2013), European Project: 281803,EC:FP7:HEALTH,FP7-HEALTH-2011-single-stage,IDAMS(2011), University of Oxford, Harvard University, European Centre for Disease Prevention and Control [Stockholm, Sweden] (ECDC), Institut Pasteur [Paris] (IP)-Centre National de la Recherche Scientifique (CNRS), and University of California (UC)
Subjects: Microbiology (medical), Microbiologie et protistologie [parasitologie hum. et anim.], Aedes albopictus, Arbovirus Infections, Immunology, Aedes aegypti, medicine.disease_cause, Applied Microbiology and Biotechnology, Arbovirus, Microbiology, Article, Dengue fever, Zika virus, 03 medical and health sciences, Genetics, medicine, Chikungunya, 030304 developmental biology, Aedes, 0303 health sciences, biology, 030306 microbiology, Ecology, virus diseases, Cell Biology, biology.organism_classification, medicine.disease, 3. Good health, Microbiologie et protistologie [entomologie,phytoparasitolog.], Geography, [SDV.MP.VIR]Life Sciences [q-bio]/Microbiology and Parasitology/Virology, Infectious diseases
Abstract: The global population at risk from mosquito-borne diseases—including dengue, yellow fever, chikungunya and Zika—is expanding in concert with changes in the distribution of two key vectors: Aedes aegypti and Aedes albopictus. The distribution of these species is largely driven by both human movement and the presence of suitable climate. Using statistical mapping techniques, we show that human movement patterns explain the spread of both species in Europe and the United States following their introduction. We find that the spread of Ae. aegypti is characterized by long distance importations, while Ae. albopictus has expanded more along the fringes of its distribution. We describe these processes and predict the future distributions of both species in response to accelerating urbanization, connectivity and climate change. Global surveillance and control efforts that aim to mitigate the spread of chikungunya, dengue, yellow fever and Zika viruses must consider the so far unabated spread of these mosquitos. Our maps and predictions offer an opportunity to strategically target surveillance and control programmes and thereby augment efforts to reduce arbovirus burden in human populations globally., 0, SCOPUS: ar.j, SCOPUS: er.j, info:eu-repo/semantics/published
Published: 2019

50. Genomic epidemiology of SARS-CoV-2 transmission lineages in Ecuador

Author: Denisse Portugal, Mónica Becerra-Wong, Marina Escalera-Zamudio, Andrés Carrazco-Montalvo, Paul Cardenas, Leandro Patiño, Manuel Gonzalez, Josefina Coloma, Sully Márquez, Orson Mestanza, Gabriel Trueba, Oliver G. Pybus, Arne Kühne, Alberto Orlando, John T. McCrone, Domenica de Mora, Gabriela Sevillano, Nuno R. Faria, Rubén Armas-Gonzalez, Maritza Olmedo, Juan Carlos Fernandez-Cadena, Bernardo Gutierrez, Veronica Barragan, Moritz U. G. Kraemer, Alfredo Bruno, Louis du Plessis, Darlan da Silva Candido, Belén Prado-Vivar, Gabriel Morey-León, Jan Felix Drexler, Derly Andrade-Molina, Juan José Guadalupe, Anna-Lena Sander, Andres Moreira-Soto, Patricio Rojas-Silva, Michelle Grunauer, Sebastian Brünink, and Jeannete Zurita
Subjects: medicine.medical_specialty, media_common.quotation_subject, Context (language use), phylogeography, molecular epidemiology, Microbiology, Genome, Article, law.invention, 03 medical and health sciences, law, Virology, Epidemiology, Pandemic, medicine, AcademicSubjects/MED00860, Molecular clock, 030304 developmental biology, media_common, 0303 health sciences, Genetic diversity, Molecular epidemiology, SARS-CoV-2, 030306 microbiology, AcademicSubjects/SCI01130, AcademicSubjects/SCI02285, phylogenetics, Phylogeography, Geography, Transmission (mechanics), Evolutionary biology, transmission lineages, Research Article, Diversity (politics)
Abstract: Characterisation of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) genetic diversity through space and time can reveal trends in virus importation and domestic circulation and permit the exploration of questions regarding the early transmission dynamics. Here, we present a detailed description of SARS-CoV-2 genomic epidemiology in Ecuador, one of the hardest hit countries during the early stages of the coronavirus-19 pandemic. We generated and analysed 160 whole genome sequences sampled from all provinces of Ecuador in 2020. Molecular clock and phylogeographic analysis of these sequences in the context of global SARS-CoV-2 diversity enable us to identify and characterise individual transmission lineages within Ecuador, explore their spatiotemporal distributions, and consider their introduction and domestic circulation. Our results reveal a pattern of multiple international importations across the country, with apparent differences between key provinces. Transmission lineages were mostly introduced before the implementation of non-pharmaceutical interventions, with differential degrees of persistence and national dissemination., Virus Evolution, 7 (2), ISSN:2057-1577
Published: 2021

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