Author: "Tim C.D. Lucas" - Searchworks@Jio Institute Digital Library Search Results

24 results on '"Tim C.D. Lucas"'

1. Mapping malaria seasonality in Madagascar using health facility data

Author: Michele Nguyen, Rosalind E. Howes, Tim C.D. Lucas, Katherine E. Battle, Ewan Cameron, Harry S. Gibson, Jennifer Rozier, Suzanne Keddie, Emma Collins, Rohan Arambepola, Su Yun Kang, Chantal Hendriks, Anita Nandi, Susan F. Rumisha, Samir Bhatt, Sedera A. Mioramalala, Mauricette Andriamananjara Nambinisoa, Fanjasoa Rakotomanana, Peter W. Gething, and Daniel J. Weiss
Subjects: Seasonality, Malaria, Geostatistical model, Health facility data, Madagascar, Medicine
Abstract: Abstract Background Many malaria-endemic areas experience seasonal fluctuations in case incidence as Anopheles mosquito and Plasmodium parasite life cycles respond to changing environmental conditions. Identifying location-specific seasonality characteristics is useful for planning interventions. While most existing maps of malaria seasonality use fixed thresholds of rainfall, temperature, and/or vegetation indices to identify suitable transmission months, we construct a statistical modelling framework for characterising the seasonal patterns derived directly from monthly health facility data. Methods With data from 2669 of the 3247 health facilities in Madagascar, a spatiotemporal regression model was used to estimate seasonal patterns across the island. In the absence of catchment population estimates or the ability to aggregate to the district level, this focused on the monthly proportions of total annual cases by health facility level. The model was informed by dynamic environmental covariates known to directly influence seasonal malaria trends. To identify operationally relevant characteristics such as the transmission start months and associated uncertainty measures, an algorithm was developed and applied to model realisations. A seasonality index was used to incorporate burden information from household prevalence surveys and summarise ‘how seasonal’ locations are relative to their surroundings. Results Positive associations were detected between monthly case proportions and temporally lagged covariates of rainfall and temperature suitability. Consistent with the existing literature, model estimates indicate that while most parts of Madagascar experience peaks in malaria transmission near March–April, the eastern coast experiences an earlier peak around February. Transmission was estimated to start in southeast districts before southwest districts, suggesting that indoor residual spraying should be completed in the same order. In regions where the data suggested conflicting seasonal signals or two transmission seasons, estimates of seasonal features had larger deviations and therefore less certainty. Conclusions Monthly health facility data can be used to establish seasonal patterns in malaria burden and augment the information provided by household prevalence surveys. The proposed modelling framework allows for evidence-based and cohesive inferences on location-specific seasonal characteristics. As health surveillance systems continue to improve, it is hoped that more of such data will be available to improve our understanding and planning of intervention strategies.
Published: 2020
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2. Responsible modelling: Unit testing for infectious disease epidemiology

Author: Tim C.D. Lucas, Timothy M Pollington, Emma L Davis, and T Déirdre Hollingsworth
Subjects: Unit testing, Software development, Reproducible science, Computational models, Infectious and parasitic diseases, RC109-216
Abstract: Infectious disease epidemiology is increasingly reliant on large-scale computation and inference. Models have guided health policy for epidemics including COVID-19 and Ebola and endemic diseases including malaria and tuberculosis. Yet a coding bug may bias results, yielding incorrect conclusions and actions causing avoidable harm. We are ethically obliged to make our code as free of error as possible. Unit testing is a coding method to avoid such bugs, but it is rarely used in epidemiology. We demonstrate how unit testing can handle the particular quirks of infectious disease models and aim to increase the uptake of this methodology in our field.
Published: 2020
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3. Diving into the vertical dimension of elasmobranch movement ecology

Author: Samantha Andrzejaczek, Tim C.D. Lucas, Maurice C. Goodman, Nigel E. Hussey, Amelia J. Armstrong, Aaron Carlisle, Daniel M. Coffey, Adrian C. Gleiss, Charlie Huveneers, David M. P. Jacoby, Mark G. Meekan, Johann Mourier, Lauren R. Peel, Kátya Abrantes, André S. Afonso, Matthew J. Ajemian, Brooke N. Anderson, Scot D. Anderson, Gonzalo Araujo, Asia O. Armstrong, Pascal Bach, Adam Barnett, Mike B. Bennett, Natalia A. Bezerra, Ramon Bonfil, Andre M. Boustany, Heather D. Bowlby, Ilka Branco, Camrin D. Braun, Edward J. Brooks, Judith Brown, Patrick J. Burke, Paul Butcher, Michael Castleton, Taylor K. Chapple, Olivier Chateau, Maurice Clarke, Rui Coelho, Enric Cortes, Lydie I. E. Couturier, Paul D. Cowley, Donald A. Croll, Juan M. Cuevas, Tobey H. Curtis, Laurent Dagorn, Jonathan J. Dale, Ryan Daly, Heidi Dewar, Philip D. Doherty, Andrés Domingo, Alistair D. M. Dove, Michael Drew, Christine L. Dudgeon, Clinton A. J. Duffy, Riley G. Elliott, Jim R. Ellis, Mark V. Erdmann, Thomas J. Farrugia, Luciana C. Ferreira, Francesco Ferretti, John D. Filmalter, Brittany Finucci, Chris Fischer, Richard Fitzpatrick, Fabien Forget, Kerstin Forsberg, Malcolm P. Francis, Bryan R. Franks, Austin J. Gallagher, Felipe Galvan-Magana, Mirta L. García, Troy F. Gaston, Bronwyn M. Gillanders, Matthew J. Gollock, Jonathan R. Green, Sofia Green, Christopher A. Griffiths, Neil Hammerschlag, Abdi Hasan, Lucy A. Hawkes, Fabio Hazin, Matthew Heard, Alex Hearn, Kevin J. Hedges, Suzanne M. Henderson, John Holdsworth, Kim N. Holland, Lucy A. Howey, Robert E. Hueter, Nicholas E. Humphries, Melanie Hutchinson, Fabrice R. A. Jaine, Salvador J. Jorgensen, Paul E. Kanive, Jessica Labaja, Fernanda O. Lana, Hugo Lassauce, Rebecca S. Lipscombe, Fiona Llewellyn, Bruno C. L. Macena, Ronald Mambrasar, Jaime D. McAllister, Sophy R. McCully Phillips, Frazer McGregor, Matthew N. McMillan, Lianne M. McNaughton, Sibele A. Mendonça, Carl G. Meyer, Megan Meyers, John A. Mohan, John C. Montgomery, Gonzalo Mucientes, Michael K. Musyl, Nicole Nasby-Lucas, Lisa J. Natanson, John B. O’Sullivan, Paulo Oliveira, Yannis P. Papastamtiou, Toby A. Patterson, Simon J. Pierce, Nuno Queiroz, Craig A. Radford, Andy J. Richardson, Anthony J. Richardson, David Righton, Christoph A. Rohner, Mark A. Royer, Ryan A. Saunders, Matthias Schaber, Robert J. Schallert, Michael C. Scholl, Andrew C. Seitz, Jayson M. Semmens, Edy Setyawan, Brendan D. Shea, Rafid A. Shidqi, George L. Shillinger, Oliver N. Shipley, Mahmood S. Shivji, Abraham B. Sianipar, Joana F. Silva, David W. Sims, Gregory B. Skomal, Lara L. Sousa, Emily J. Southall, Julia L. Y. Spaet, Kilian M. Stehfest, Guy Stevens, Joshua D. Stewart, James A. Sulikowski, Ismail Syakurachman, Simon R. Thorrold, Michele Thums, David Tickler, Mariana T. Tolloti, Kathy A. Townsend, Paulo Travassos, John P. Tyminski, Jeremy J. Vaudo, Drausio Veras, Laurent Wantiez, Sam B. Weber, R.J. David Wells, Kevin C. Weng, Bradley M. Wetherbee, Jane E. Williamson, Matthew J. Witt, Serena Wright, Kelly Zilliacus, Barbara A. Block, David J. Curnick, Andrzejaczek, Samantha [0000-0002-9929-7312], Lucas, Tim CD [0000-0003-4694-8107], Goodman, Maurice C [0000-0002-6874-2313], Hussey, Nigel E [0000-0002-9050-6077], Armstrong, Amelia J [0000-0001-8103-4314], Carlisle, Aaron [0000-0003-0796-6564], Coffey, Daniel M [0000-0001-5983-0146], Huveneers, Charlie [0000-0001-8937-1358], Jacoby, David MP [0000-0003-2729-3811], Meekan, Mark G [0000-0002-3067-9427], Mourier, Johann [0000-0001-9019-1717], Peel, Lauren R [0000-0001-6960-5663], Abrantes, Kátya [0000-0001-7430-8428], Afonso, André S [0000-0001-9129-278X], Ajemian, Matthew J [0000-0002-2725-4030], Anderson, Brooke N [0000-0003-4299-3496], Araujo, Gonzalo [0000-0002-4708-3638], Armstrong, Asia O [0000-0002-9307-0598], Barnett, Adam [0000-0001-7430-8428], Bennett, Mike B [0000-0001-8051-0040], Bezerra, Natalia A [0000-0002-4203-8408], Bonfil, Ramon [0000-0002-5753-464X], Boustany, Andre M [0000-0001-5501-7190], Bowlby, Heather D [0000-0002-2532-3725], Branco, Ilka [0000-0001-8136-2596], Braun, Camrin D [0000-0002-9317-9489], Brooks, Edward J [0000-0001-5206-7133], Burke, Patrick J [0000-0002-7217-0215], Butcher, Paul [0000-0001-7338-6037], Castleton, Michael [0000-0001-9639-6967], Chapple, Taylor K [0000-0002-0357-0223], Chateau, Olivier [0000-0003-1153-6284], Coelho, Rui [0000-0003-3813-5157], Cortes, Enric [0000-0001-6001-2482], Couturier, Lydie IE [0000-0002-3885-3397], Cuevas, Juan M [0000-0003-0086-5963], Curtis, Tobey H [0000-0003-0164-7335], Dale, Jonathan J [0000-0001-8565-3841], Daly, Ryan [0000-0002-4409-6951], Dewar, Heidi [0000-0002-8202-1387], Doherty, Philip D [0000-0001-7561-3731], Domingo, Andrés [0000-0002-1793-7663], Dove, Alistair DM [0000-0003-3239-4772], Drew, Michael [0000-0002-5109-7792], Dudgeon, Christine L [0000-0001-5059-7886], Duffy, Clinton AJ [0000-0002-3352-1609], Elliott, Riley G [0000-0003-0234-5953], Erdmann, Mark V [0000-0002-3644-8347], Farrugia, Thomas J [0000-0001-9052-8826], Ferreira, Luciana C [0000-0001-6755-2799], Ferretti, Francesco [0000-0001-9510-3552], Finucci, Brittany [0000-0003-1315-2946], Forget, Fabien [0000-0002-4845-4277], Forsberg, Kerstin [0000-0002-1233-9381], Franks, Bryan R [0000-0003-4016-9225], Gallagher, Austin J [0000-0003-1515-3440], García, Mirta L [0000-0003-0143-7397], Gaston, Troy F [0000-0003-0049-0831], Gillanders, Bronwyn M [0000-0002-7680-2240], Green, Jonathan R [0000-0001-7671-6716], Green, Sofia [0000-0002-2878-5984], Griffiths, Christopher A [0000-0001-7203-0426], Hammerschlag, Neil [0000-0001-9002-9082], Hawkes, Lucy A [0000-0002-6696-1862], Hearn, Alex [0000-0002-4986-098X], Hedges, Kevin J [0000-0002-2219-2360], Holland, Kim N [0000-0003-4663-7026], Howey, Lucy A [0000-0001-7381-4871], Humphries, Nicholas E [0000-0003-3741-1594], Hutchinson, Melanie [0000-0001-7042-0658], Jaine, Fabrice RA [0000-0002-9304-5034], Jorgensen, Salvador J [0000-0002-4331-1648], Kanive, Paul E [0000-0003-2430-6920], Labaja, Jessica [0000-0001-6916-7050], Lana, Fernanda O [0000-0001-7235-069X], Lassauce, Hugo [0000-0001-9636-6522], Lipscombe, Rebecca S [0000-0001-9602-643X], Llewellyn, Fiona [0000-0003-4309-8311], Macena, Bruno CL [0000-0001-5010-8560], McCully Phillips, Sophy R [0000-0003-3110-5916], McGregor, Frazer [0000-0002-7441-4404], McMillan, Matthew N [0000-0001-6348-184X], Mendonça, Sibele A [0000-0002-1981-5950], Mohan, John A [0000-0002-2758-163X], Mucientes, Gonzalo [0000-0001-6650-3020], Musyl, Michael K [0000-0003-4719-9259], Nasby-Lucas, Nicole [0000-0001-8355-9392], Natanson, Lisa J [0000-0002-2903-6037], O'Sullivan, John B [0000-0002-1689-2141], Oliveira, Paulo [0000-0001-7697-2111], Papastamtiou, Yannis P [0000-0002-6091-6841], Patterson, Toby A [0000-0002-7150-9205], Pierce, Simon J [0000-0002-9375-5175], Queiroz, Nuno [0000-0002-3860-7356], Radford, Craig A [0000-0001-7949-9497], Richardson, Andy J [0000-0003-2598-5080], Richardson, Anthony J [0000-0002-9289-7366], Righton, David [0000-0001-8643-3672], Rohner, Christoph A [0000-0001-8760-8972], Royer, Mark A [0000-0002-6938-7536], Schaber, Matthias [0000-0003-1032-4626], Schallert, Robert J [0000-0002-3584-2668], Scholl, Michael C [0000-0002-6014-1759], Semmens, Jayson M [0000-0003-1742-6692], Setyawan, Edy [0000-0001-6629-5997], Shea, Brendan D [0000-0001-7771-0586], Shillinger, George L [0000-0001-5168-4551], Shipley, Oliver N [0000-0001-5163-3471], Sianipar, Abraham B [0000-0003-4049-3893], Silva, Joana F [0000-0002-2897-1410], Sims, David W [0000-0002-0916-7363], Sousa, Lara L [0000-0002-4392-3572], Southall, Emily J [0000-0001-7246-278X], Spaet, Julia LY [0000-0001-8703-1472], Stevens, Guy [0000-0002-2056-9830], Sulikowski, James A [0000-0002-3646-5200], Thums, Michele [0000-0002-8669-8440], Tickler, David [0000-0001-7722-0771], Tolloti, Mariana T [0000-0001-6895-2479], Townsend, Kathy A [0000-0002-2581-2158], Travassos, Paulo [0000-0001-8667-5292], Tyminski, John P [0000-0001-8251-7385], Vaudo, Jeremy J [0000-0002-6826-3822], Veras, Drausio [0000-0001-5627-6848], Wantiez, Laurent [0000-0001-5024-2057], Weber, Sam B [0000-0003-1447-4082], Wells, RJ David [0000-0002-1306-0614], Weng, Kevin C [0000-0002-7069-7152], Wetherbee, Bradley M [0000-0002-3753-8950], Williamson, Jane E [0000-0003-3627-4508], Witt, Matthew J [0000-0002-9498-5378], Zilliacus, Kelly [0000-0001-9166-5611], Block, Barbara A [0000-0001-5181-3616], Curnick, David J [0000-0002-3093-1282], Apollo - University of Cambridge Repository, Zoological Society of London - ZSL (UNITED KINGDOM), Centre de recherches insulaires et observatoire de l'environnement (CRIOBE), Université de Perpignan Via Domitia (UPVD)-École Pratique des Hautes Études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS), MARine Biodiversity Exploitation and Conservation (UMR MARBEC), and Institut de Recherche pour le Développement (IRD)-Institut Français de Recherche pour l'Exploitation de la Mer (IFREMER)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)
Subjects: Multidisciplinary, [SDV]Life Sciences [q-bio], Violacea Bonaparte, 3103 Ecology, Pelagic stingray, Scalloped hammerhead shark, 41 Environmental Sciences, Western North Pacific, Reproductive-biology, Habitat Use, Carcharhinus-falciformis, Galeocerdo-cuvier, Sexual segregation, Sphyna-lewini, 31 Biological Sciences
Abstract: 20 pages, 3 tables, 5 figures.-- Samantha Andrzejaczek ... et al.-- Distributed under a Creative Commons Attribution NonCommercial License 4.0 (CC BY-NC), Knowledge of the three-dimensional movement patterns of elasmobranchs is vital to understand their ecological roles and exposure to anthropogenic pressures. To date, comparative studies among species at global scales have mostly focused on horizontal movements. Our study addresses the knowledge gap of vertical movements by compiling the first global synthesis of vertical habitat use by elasmobranchs from data obtained by deployment of 989 biotelemetry tags on 38 elasmobranch species. Elasmobranchs displayed high intra- and interspecific variability in vertical movement patterns. Substantial vertical overlap was observed for many epipelagic elasmobranchs, indicating an increased likelihood to display spatial overlap, biologically interact, and share similar risk to anthropogenic threats that vary on a vertical gradient. We highlight the critical next steps toward incorporating vertical movement into global management and monitoring strategies for elasmobranchs, emphasizing the need to address geographic and taxonomic biases in deployments and to concurrently consider both horizontal and vertical movements, Data analysis was funded by the Bertarelli Foundation through the Marine Science program through grants to D.J.C., B.A.B., and S.A. D.J.C. is also funded through Research England, UK. S.A. and B.A.B. thank the Moore Foundation and the Packard Foundation. F.G.-M. thanks the Instituto Politecnico Nacional for fellowships (COFAA, EDI). S.B.W. thanks funding from the Darwin Initiative (DPLUS046). A.D.M.D. acknowledges funding from the Research and Conservation Budget at Georgia Aquarium, including philanthropic gifts from several anonymous donors. K.F. acknowledges funding from the Rolex Awards for Enterprise and the Whitley Fund for Nature
Published: 2022
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4. Mapping Malaria by Sharing Spatial Information Between Incidence and Prevalence Data Sets

Author: Anita Nandi, Peter W. Gething, Rohan Arambepola, Susan F. Rumisha, Emma L. Collins, Daniel J. Weiss, Punam Amratia, Rosalind E. Howes, Tim C.D. Lucas, Katherine E. Battle, Penelope A. Hancock, Suzanne H. Keddie, Ewan Cameron, Andre Python, Katherine A. Twohig, Michele Nguyen, Amelia Bertozzi-Villa, and Elisabeth G. Chestnutt
Subjects: Statistics and Probability, Computer science, Approximation error, Incidence (epidemiology), Polygon, Covariate, Statistics, Regression analysis, Statistics, Probability and Uncertainty, Baseline (configuration management), Spatial analysis, Gaussian random field
Abstract: As malaria incidence decreases and more countries move towards elimination, maps of malaria risk in low-prevalence areas are increasingly needed. For low-burden areas, disaggregation regression models have been developed to estimate risk at high spatial resolution from routine surveillance reports aggregated by administrative unit polygons. However, in areas with both routine surveillance data and prevalence surveys, models that make use of the spatial information from prevalence point-surveys might make more accurate predictions. Using case studies in Indonesia, Senegal and Madagascar, we compare the out-of-sample mean absolute error for two methods for incorporating point-level, spatial information into disaggregation regression models. The first simply fits a binomial-likelihood, logit-link, Gaussian random field to prevalence point-surveys to create a new covariate. The second is a multi-likelihood model that is fitted jointly to prevalence point-surveys and polygon incidence data. We find that in most cases there is no difference in mean absolute error between models. In only one case, did the new models perform the best. More generally, our results demonstrate that combining these types of data has the potential to reduce absolute error in estimates of malaria incidence but that simpler baseline models should always be fitted as a benchmark.
Published: 2021
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5. Engagement and adherence trade-offs for SARS-CoV-2 contact tracing

Author: Lucy Yardley, Emma L Davis, Thomas Crellen, Li Pi, Diepreye Ayabina, Tim C.D. Lucas, Julia R. Gog, Graham F. Medley, T. Déirdre Hollingsworth, Anna Borlase, Petra Klepac, Gog, Julia [0000-0003-1240-7214], Apollo - University of Cambridge Repository, Lucas, Tim C. D. [0000-0003-4694-8107], Crellen, Thomas [0000-0003-2934-1063], and Déirdre Hollingsworth, T. [0000-0001-5962-4238]
Subjects: 2019-20 coronavirus outbreak, Average duration, Coronavirus disease 2019 (COVID-19), Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), branching processes, Basic Reproduction Number, General Biochemistry, Genetics and Molecular Biology, Disease Outbreaks, 03 medical and health sciences, ARTICLES, 0302 clinical medicine, Contact tracing, Research articles, Humans, adherence, 030212 general & internal medicine, Enforcement, Pandemics, 030304 developmental biology, 0303 health sciences, Public economics, SARS-CoV-2, Trade offs, quarantine, COVID-19, Covid19, Models, Theoretical, contact tracing, case isolation, Adherence, Quarantine, Communicable Disease Control, Case Isolation, Physical and Mental Health, Business, General Agricultural and Biological Sciences, engagement
Abstract: Contact tracing is an important tool for allowing countries to ease lockdown policies introduced to combat SARS-CoV-2. For contact tracing to be effective, those with symptoms must self-report themselves while their contacts must self-isolate when asked. However, policies such as legal enforcement of self-isolation can create trade-offs by dissuading individuals from self-reporting. We use an existing branching process model to examine which aspects of contact tracing adherence should be prioritised. We consider an inverse relationship between self-isolation adherence and self-reporting engagement, assuming that increasingly strict self-isolation policies will result in fewer individuals self-reporting to the programme. We find that policies that increase the verage duration of self-isolation, or that increase the probability that people self-isolate at all, at the expense of reduced self-reporting rate, will not decrease the risk of a large outbreak and may increase the risk, depending on the strength of the trade-off. These results suggest that policies to increase self-isolation adherence should be implemented carefully. Policies that increase self-isolation adherence at the cost of self-reporting rates should be avoided.Competing Interest StatementThe authors have declared no competing interest.Funding StatementContact tracing is an important tool for allowing countries to ease lockdown policies introduced to combat SARS-CoV-2. For contact tracing to be effective, those with symptoms must self-report themselves while their contacts must self-isolate when asked. However, policies such as legal enforcement of self-isolation can create trade-offs by dissuading individuals from self-reporting. We use an existing branching process model to examine which aspects of contact tracing adherence should be prioritised. We consider an inverse relationship between self-isolation adherence and self-reporting engagement, assuming that increasingly strict self-isolation policies will result in fewer individuals self-reporting to the programme. We find that policies that increase the average duration of self-isolation, or that increase the probability that people self-isolate at all, at the expense of reduced self-reporting rate, will not decrease the risk of a large outbreak and may increase the risk, depending on the strength of the trade-off. These results suggest that policies to increase self-isolation adherence should be implemented carefully. Policies that increase self-isolation adherence at the cost of self-reporting rates should be avoided. Author DeclarationsI confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.YesThe details of the IRB/oversight body that provided approval or exemption for the research described are given below:noneAll necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived.YesI understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).Yes I have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable.YesAll simulation code is available on github. No data other than simulated data was used.https://github.com/timcdlucas/ringbp/tree/adherence_tradeoff_runs
Published: 2021
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6. Dynamics of SARS-CoV-2 with waning immunity in the UK population

Author: Diepreye Ayabina, Li Pi, Emma L Davis, Kiesha Prem, Jaspreet Toor, Graham F. Medley, Petra Klepac, Thomas Crellen, Anna Borlase, T. Déirdre Hollingsworth, Tim C.D. Lucas, and Timothy M Pollington
Subjects: QA75, Coronavirus disease 2019 (COVID-19), Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), Population, Basic Reproduction Number, Worst-case scenario, Biology, General Biochemistry, Genetics and Molecular Biology, Herd immunity, Immunity, RA0421, Pandemic, Humans, mathematical modelling, UK, Waning immunity, education, Pandemics, Research Articles, education.field_of_study, SARS-CoV-2, infectious disease epidemiology, COVID-19, Articles, biochemical phenomena, metabolism, and nutrition, immunity, United Kingdom, QR, Specific antibody, Communicable Disease Control, General Agricultural and Biological Sciences, Basic reproduction number, Demography
Abstract: The dynamics of immunity are crucial to understanding the long-term patterns of the SARS-CoV-2 pandemic. Several cases of reinfection with SARS-CoV-2 have been documented 48–142 days after the initial infection and immunity to seasonal circulating coronaviruses is estimated to be shorter than 1 year. Using an age-structured, deterministic model, we explore potential immunity dynamics using contact data from the UK population. In the scenario where immunity to SARS-CoV-2 lasts an average of three months for non-hospitalized individuals, a year for hospitalized individuals, and the effective reproduction number after lockdown ends is 1.2 (our worst-case scenario), we find that the secondary peak occurs in winter 2020 with a daily maximum of 387 000 infectious individuals and 125 000 daily new cases; threefold greater than in a scenario with permanent immunity. Our models suggest that longitudinal serological surveys to determine if immunity in the population is waning will be most informative when sampling takes place from the end of the lockdown in June until autumn 2020. After this period, the proportion of the population with antibodies to SARS-CoV-2 is expected to increase due to the secondary wave. Overall, our analysis presents considerations for policy makers on the longer-term dynamics of SARS-CoV-2 in the UK and suggests that strategies designed to achieve herd immunity may lead to repeated waves of infection as immunity to reinfection is not permanent. This article is part of the theme issue ‘Modelling that shaped the early COVID-19 pandemic response in the UK’.
Published: 2021

7. Predicted Impact of COVID-19 on Neglected Tropical Disease Programs and the Opportunity for Innovation

Author: María Soledad Castaño, Robin L. Bailey, Carolin Vegvari, Emanuele Giorgi, Claudio Fronterre, Anna Borlase, Aatreyee M. Das, Veronica Malizia, Matthew R. Graham, Tim C.D. Lucas, Jonathan I D Hamley, Peter J. Diggle, Jaspreet Toor, Panayiota Touloupou, Simon E. F. Spencer, Andreia Vasconcelos, Christopher N. Davis, Klodeta Kura, T. Déirdre Hollingsworth, Luc E. Coffeng, Epke A. Le Rutte, Kat S. Rock, Sake J. de Vlas, David J. Blok, Martin Walker, Edwin Michael, Joaquin M. Prada, Morgan E. Smith, Emma L Davis, Emily R. Adams, Benjamin Amoah, Nakul Chitnis, Ronald E. Crump, Seth Blumberg, Maryam Aliee, Travis C. Porco, Federica Giardina, Thomas M. Lietman, Michael S. Deiner, Graham F. Medley, Wilma A. Stolk, Diepreye Ayabina, Rocio Caja Rivera, Ching-I Huang, Roy M. Anderson, María-Gloria Basáñez, Aronrag Meeyai, Public Health, Health Economics (HE), Medical Research Council (MRC), and The Task Force for Global Health
Subjects: Microbiology (medical), wc_680, coronavirus, Schistosomiasis, wa_395, Medical and Health Sciences, Microbiology, Rare Diseases, SDG 3 - Good Health and Well-being, Environmental health, Tropical Medicine, Pandemic, medicine, wc_505, Humans, neglected tropical diseases, Pandemics, Lymphatic filariasis, 11 Medical and Health Sciences, wa_105, business.industry, SARS-CoV-2, Tropical disease, Neglected Diseases, COVID-19, modeling, 06 Biological Sciences, Biological Sciences, medicine.disease, Viewpoints, Vector-Borne Diseases, AcademicSubjects/MED00290, Visceral leishmaniasis, Orphan Drug, Infectious Diseases, Good Health and Well Being, Trachoma, Neglected tropical diseases, business, Onchocerciasis, Infection, RA, RC
Abstract: Due to the COVID-19 pandemic, many key neglected tropical disease (NTD) activities have been postponed. This hindrance comes at a time when the NTDs are progressing towards their ambitious goals for 2030. Mathematical modelling on several NTDs, namely gambiense sleeping sickness, lymphatic filariasis, onchocerciasis, schistosomiasis, soil-transmitted helminthiases (STH), trachoma, and visceral leishmaniasis, shows that the impact of this disruption will vary across the diseases. Programs face a risk of resurgence, which will be fastest in high-transmission areas. Furthermore, of the mass drug administration diseases, schistosomiasis, STH, and trachoma are likely to encounter faster resurgence. The case-finding diseases (gambiense sleeping sickness and visceral leishmaniasis) are likely to have fewer cases being detected but may face an increasing underlying rate of new infections. However, once programs are able to resume, there are ways to mitigate the impact and accelerate progress towards the 2030 goals., The COVID-19 pandemic has disrupted neglected tropical disease programs. Modelling shows the impact of this will vary across diseases. Once interventions are reintroduced, there are opportunities for mitigating the delay and accelerating progress towards the 2030 goals.
Published: 2021

8. Global estimation of anti-malarial drug effectiveness for the treatment of uncomplicated Plasmodium falciparum malaria 1991-2019

Author: Emma L. Collins, Tim C.D. Lucas, Penelope A. Hancock, Amelia Bertozzi-Villa, Michele Nguyen, Harry S. Gibson, Daniel J. Weiss, Peter W. Gething, Rohan Arambepola, Tasmin L. Symons, Katherine E. Battle, Anita Nandi, Elisabeth G. Chestnutt, Philippe J Guerin, Suzanne H. Keddie, Jennifer Rozier, Justin Millar, Punam Amratia, Ewan Cameron, Katherine A. Twohig, Giulia Rathmes, Andre Python, Susan F. Rumisha, Georgina S Humphreys, and Samir Bhatt
Subjects: Drug, medicine.medical_specialty, Drug quality, lcsh:Arctic medicine. Tropical medicine, Combination therapy, lcsh:RC955-962, media_common.quotation_subject, Plasmodium falciparum, Drug Resistance, Drug resistance, lcsh:Infectious and parasitic diseases, Antimalarials, parasitic diseases, medicine, Humans, lcsh:RC109-216, Artemisinin, Malaria, Falciparum, Falciparum malaria, Intensive care medicine, media_common, biology, business.industry, Incidence (epidemiology), Public health, Research, Anti-malarial drug effectiveness, Global, medicine.disease, biology.organism_classification, Artemisinins, Infectious Diseases, Parasitology, business, Malaria, medicine.drug
Abstract: Background Anti-malarial drugs play a critical role in reducing malaria morbidity and mortality, but their role is mediated by their effectiveness. Effectiveness is defined as the probability that an anti-malarial drug will successfully treat an individual infected with malaria parasites under routine health care delivery system. Anti-malarial drug effectiveness (AmE) is influenced by drug resistance, drug quality, health system quality, and patient adherence to drug use; its influence on malaria burden varies through space and time. Methods This study uses data from 232 efficacy trials comprised of 86,776 infected individuals to estimate the artemisinin-based and non-artemisinin-based AmE for treating falciparum malaria between 1991 and 2019. Bayesian spatiotemporal models were fitted and used to predict effectiveness at the pixel-level (5 km × 5 km). The median and interquartile ranges (IQR) of AmE are presented for all malaria-endemic countries. Results The global effectiveness of artemisinin-based drugs was 67.4% (IQR: 33.3–75.8), 70.1% (43.6–76.0) and 71.8% (46.9–76.4) for the 1991–2000, 2006–2010, and 2016–2019 periods, respectively. Countries in central Africa, a few in South America, and in the Asian region faced the challenge of lower effectiveness of artemisinin-based anti-malarials. However, improvements were seen after 2016, leaving only a few hotspots in Southeast Asia where resistance to artemisinin and partner drugs is currently problematic and in the central Africa where socio-demographic challenges limit effectiveness. The use of artemisinin-based combination therapy (ACT) with a competent partner drug and having multiple ACT as first-line treatment choice sustained high levels of effectiveness. High levels of access to healthcare, human resource capacity, education, and proximity to cities were associated with increased effectiveness. Effectiveness of non-artemisinin-based drugs was much lower than that of artemisinin-based with no improvement over time: 52.3% (17.9–74.9) for 1991–2000 and 55.5% (27.1–73.4) for 2011–2015. Overall, AmE for artemisinin-based and non-artemisinin-based drugs were, respectively, 29.6 and 36% below clinical efficacy as measured in anti-malarial drug trials. Conclusions This study provides evidence that health system performance, drug quality and patient adherence influence the effectiveness of anti-malarials used in treating uncomplicated falciparum malaria. These results provide guidance to countries’ treatment practises and are critical inputs for malaria prevalence and incidence models used to estimate national level malaria burden.
Published: 2020

9. A translucent box: interpretable machine learning in ecology

Author: Tim C.D. Lucas
Subjects: Computer science, business.industry, Ecology, Ecology (disciplines), Artificial intelligence, Machine learning, computer.software_genre, Random effects model, business, Model interpretation, computer, Ecology, Evolution, Behavior and Systematics, Random forest
Published: 2020
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10. Engagement and adherence trade-offs for SARS-CoV-2 contact tracing

Author: Anna Borlase, Tim C.D. Lucas, Lucy Yardley, T. Déirdre Hollingsworth, Julia R. Gog, Li Pi, Emma L Davis, Diepreye Ayabina, Petra Klepac, Graham F. Medley, and Thomas Crellen
Subjects: 0303 health sciences, 03 medical and health sciences, Average duration, 0302 clinical medicine, Public economics, Coronavirus disease 2019 (COVID-19), Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), Trade offs, 030212 general & internal medicine, Business, Enforcement, Contact tracing, 030304 developmental biology
Abstract: Contact tracing is an important tool for allowing countries to ease lock-down policies introduced to combat SARS-CoV-2. For contact tracing to be effective, those with symptoms must self-report themselves while their contacts must self-isolate when asked. However, policies such as legal enforcement of self-isolation can create trade-offs by dissuading individuals from self-reporting. We use an existing branching process model to examine which aspects of contact tracing adherence should be prioritised. We consider an inverse relationship between self-isolation adherence and self-reporting engagement, assuming that increasingly strict self-isolation policies will result in fewer individuals self-reporting to the programme. We find that policies that increase the average duration of self-isolation, or that increase the probability that people self-isolate at all, at the expense of reduced self-reporting rate, will not decrease the risk of a large outbreak and may increase the risk, depending on the strength of the trade-off. These results suggest that policies to increase self-isolation adherence should be implemented carefully. Policies that increase self-isolation adherence at the cost of self-reporting rates should be avoided.
Published: 2020
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11. Responsible modelling: Unit testing for infectious disease epidemiology

Author: Emma L Davis, Timothy M Pollington, Tim C.D. Lucas, and T. Déirdre Hollingsworth
Subjects: medicine.medical_specialty, Epidemiology, Computer science, 030231 tropical medicine, Inference, Review, Unit testing, Microbiology, Communicable Diseases, Models, Biological, lcsh:Infectious and parasitic diseases, 1117 Public Health and Health Services, QH301, 03 medical and health sciences, 0302 clinical medicine, Virology, parasitic diseases, Pandemic, medicine, Computational models, Humans, lcsh:RC109-216, Computer Simulation, 030212 general & internal medicine, Intensive care medicine, Pandemics, Health policy, Science & Technology, fungi, Public Health, Environmental and Occupational Health, food and beverages, COVID-19, Software development, Reproducible science, 1103 Clinical Sciences, Infectious Disease Epidemiology, medicine.disease, Infectious Diseases, Harm, Risk analysis (engineering), Infectious disease (medical specialty), Reinfection, Parasitology, RA, Life Sciences & Biomedicine, Malaria, Software
Abstract: Highlights • Unit testing can reduce the number of bugs in code but is rarely used in our field. • We present a worked example of adding unit tests to a computational model. • Specific issues such as stochastic code are common in infectious disease modelling. • Unit testing can handle particular quirks of infectious disease models. • We hope to increase the use of unit testing in infectious disease epidemiology., Infectious disease epidemiology is increasingly reliant on large-scale computation and inference. Models have guided health policy for epidemics including COVID-19 and Ebola and endemic diseases including malaria and tuberculosis. Yet a coding bug may bias results, yielding incorrect conclusions and actions causing avoidable harm. We are ethically obliged to make our code as free of error as possible. Unit testing is a coding method to avoid such bugs, but it is rarely used in epidemiology. We demonstrate how unit testing can handle the particular quirks of infectious disease models and aim to increase uptake of this methodology in our field.
Published: 2020

12. Improving disaggregation models of malaria incidence by ensembling non-linear models of prevalence

Author: Susan F. Rumisha, Daniel J. Weiss, Suzanne H. Keddie, Tasmin L. Symons, Anita Nandi, Katherine E. Battle, Elisabeth G. Chestnutt, Tim C.D. Lucas, Ewan Cameron, Andre Python, Punam Amratia, Penelope A. Hancock, Rosalind E. Howes, Justin Millar, Amelia Bertozzi-Villa, Peter W. Gething, and Rohan Arambepola
Subjects: Epidemiology, Computer science, Health, Toxicology and Mutagenesis, 030231 tropical medicine, Geography, Planning and Development, High resolution, 1117 Public Health and Health Services, 03 medical and health sciences, 0302 clinical medicine, parasitic diseases, Statistics, medicine, Prevalence, Humans, 030212 general & internal medicine, Malaria, Falciparum, Disease burden, 0707 Veterinary Sciences, Incidence, Contrast (statistics), Regression analysis, Non linear model, medicine.disease, Regression, Malaria, Infectious Diseases, Malaria incidence, Nonlinear Dynamics
Abstract: Maps of disease burden are a core tool needed for the control and elimination of malaria. Reliable routine surveillance data of malaria incidence, typically aggregated to administrative units, is becoming more widely available. Disaggregation regression is an important model framework for estimating high resolution risk maps from aggregated data. However, the aggregation of incidence over large, heterogeneous areas means that these data are underpowered for estimating complex, non-linear models. In contrast, prevalence point-surveys are directly linked to local environmental conditions but are not common in many areas of the world. Here, we train multiple non-linear, machine learning models on Plasmodium falciparum prevalence point-surveys. We then ensemble the predictions from these machine learning models with a disaggregation regression model that uses aggregated malaria incidences as response data. We find that using a disaggregation regression model to combine predictions from machine learning models improves model accuracy relative to a baseline model.
Published: 2020

13. A downscaling approach to compare COVID-19 count data from databases aggregated at different spatial scales

Author: Janine B. Illian, Andreas Bender, Jianwei Yin, Yingying Wen, Marta Blangiardo, Andre Python, Ying Lin, Siwei Tan, Tim C.D. Lucas, Davit Svanidze, and Baoli Liu
Subjects: Statistics and Probability, Economics and Econometrics, Coronavirus disease 2019 (COVID-19), Bayesian probability, Declaration, 01 natural sciences, 010104 statistics & probability, 03 medical and health sciences, COVID‐19, 0502 economics and business, Regional science, 050207 economics, 0101 mathematics, China, Spatial analysis, 030304 developmental biology, 0303 health sciences, 050208 finance, 05 social sciences, downscaling, Regression analysis, Original Articles, 3. Good health, Bayesian statistics, Data set, spatially disaggregated data, Geography, Scale (social sciences), Original Article, Statistics, Probability and Uncertainty, Scale (map), Cartography, Social Sciences (miscellaneous), Count data, Downscaling, District level
Abstract: As the COVID-19 pandemic continues to threaten various regions around the world, obtaining accurate and reliable COVID-19 data is crucial for governments and local communities aiming at rigorously assessing the extent and magnitude of the virus spread and deploying efficient interventions. Using data reported between January and February 2020 in China, we compared counts of COVID-19 from near-real time spatially disaggregated data (city-level) with fine-spatial scale predictions from a Bayesian downscaling regression model applied to a reference province-level dataset. The results highlight discrepancies in the counts of coronavirus-infected cases at district level and identify districts that may require further investigation. Funding Statement: This work was supported by Zhejiang University Educational Funding (2020XGZX054), the National Natural Science Foundation of China under Grants (61825205 and 41601001), and The Royal Society, United Kingdom (NF171120). Declaration of Interests: The authors declare no competing interests.
Published: 2020

14. An imperfect tool: contact tracing could provide valuable reductions in COVID-19 transmission if good adherence can be achieved and maintained

Author: Petra Klepac, Emma L Davis, Li Pi, Thomas Crellen, Diepreye Ayabina, Anna Borlase, Timothy M Pollington, T. Déirdre Hollingsworth, Graham F. Medley, Tim C.D. Lucas, Sam Abbott, and Joel Hellewell
Subjects: Continuation, Risk analysis (engineering), Coronavirus disease 2019 (COVID-19), Computer science, Distancing, Imperfect, Tracing, Contact tracing, Test (assessment), TRACE (psycholinguistics)
Abstract: Emerging evidence suggests that contact tracing has had limited success in the UK in reducing the R number across the COVID-19 pandemic. We investigate potential pitfalls and areas for improvement by extending an existing branching process contact tracing model, adding diagnostic testing and refining parameter estimates. Our results demonstrate that reporting and adherence are the most important predictors of programme impact but tracing coverage and speed plus diagnostic sensitivity also play an important role. We conclude that well-implemented contact tracing could bring small but potentially important benefits to controlling and preventing outbreaks, providing up to a 15% reduction in R, and reaffirm that contact tracing is not currently appropriate as the sole control measure.
Published: 2020
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15. Mapping the global endemicity and clinical burden of Plasmodium vivax, 2000–17: a spatial and temporal modelling study

Author: Mohsen Naghavi, Ali H. Mokdad, Michael P Thorn, Peter W. Gething, Ric N. Price, Haidong Wang, Michele Nguyen, Michael Kutz, Grant Nguyen, Katya Anne Shackelford, Xie Rachel Kulikoff, Tim C.D. Lucas, Ursula Dalrymple, Anita Nandi, Daniel A. Pfeffer, Puja C Rao, Rosalind E. Howes, Alan D. Lopez, Ewan Cameron, Jennifer Rozier, Nancy Fullman, Daniel C Casey, Emma L. Collins, Harry S. Gibson, Chantal Huynh, Theo Vos, Samir Bhatt, Christopher J L Murray, Suzanne H. Keddie, Donal Bisanzio, J. Kevin Baird, Stephen S Lim, David L. Smith, Simon I. Hay, Daniel J. Weiss, Katherine E. Battle, Carlos A. Guerra, Katherine A. Twohig, and Joseph R Harris
Subjects: medicine.medical_specialty, Endemic Diseases, Oceania, Plasmodium vivax, Disease, 030204 cardiovascular system & hematology, Global Health, Article, 03 medical and health sciences, Medicine, General & Internal, MALARIA, Spatio-Temporal Analysis, 0302 clinical medicine, General & Internal Medicine, Epidemiology, parasitic diseases, Malaria, Vivax, medicine, Global health, Humans, 030212 general & internal medicine, Socioeconomic status, Asia, Southeastern, 11 Medical and Health Sciences, Science & Technology, biology, Incidence (epidemiology), 1. No poverty, Bayes Theorem, Plasmodium falciparum, General Medicine, biology.organism_classification, medicine.disease, 3. Good health, Geography, Population Surveillance, Africa, Americas, Life Sciences & Biomedicine, Malaria, Demography
Abstract: Summary Background Plasmodium vivax exacts a significant toll on health worldwide, yet few efforts to date have quantified the extent and temporal trends of its global distribution. Given the challenges associated with the proper diagnosis and treatment of P vivax , national malaria programmes—particularly those pursuing malaria elimination strategies—require up to date assessments of P vivax endemicity and disease impact. This study presents the first global maps of P vivax clinical burden from 2000 to 2017. Methods In this spatial and temporal modelling study, we adjusted routine malariometric surveillance data for known biases and used socioeconomic indicators to generate time series of the clinical burden of P vivax . These data informed Bayesian geospatial models, which produced fine-scale predictions of P vivax clinical incidence and infection prevalence over time. Within sub-Saharan Africa, where routine surveillance for P vivax is not standard practice, we combined predicted surfaces of Plasmodium falciparum with country-specific ratios of P vivax to P falciparum . These results were combined with surveillance-based outputs outside of Africa to generate global maps. Findings We present the first high-resolution maps of P vivax burden. These results are combined with those for P falciparum (published separately) to form the malaria estimates for the Global Burden of Disease 2017 study. The burden of P vivax malaria decreased by 41·6%, from 24·5 million cases (95% uncertainty interval 22·5–27·0) in 2000 to 14·3 million cases (13·7–15·0) in 2017. The Americas had a reduction of 56·8% (47·6–67·0) in total cases since 2000, while South-East Asia recorded declines of 50·5% (50·3–50·6) and the Western Pacific regions recorded declines of 51·3% (48·0–55·4). Europe achieved zero P vivax cases during the study period. Nonetheless, rates of decline have stalled in the past five years for many countries, with particular increases noted in regions affected by political and economic instability. Interpretation Our study highlights important spatial and temporal patterns in the clinical burden and prevalence of P vivax . Amid substantial progress worldwide, plateauing gains and areas of increased burden signal the potential for challenges that are greater than expected on the road to malaria elimination. These results support global monitoring systems and can inform the optimisation of diagnosis and treatment where P vivax has most impact. Funding Bill & Melinda Gates Foundation and the Wellcome Trust.
Published: 2019

16. Mapping malaria by sharing spatial information between incidence and prevalence datasets

Author: Amelia Bertozzi-Villa, Peter W. Gething, Rosalind E. Howes, Rohan Arambepola, Michele Nguyen, Elisabeth G. Chestnutt, Emma L. Collins, Punam Amratia, Andre Python, Daniel J. Weiss, Susan F. Rumisha, Tim C.D. Lucas, Penelope A. Hancock, Ewan Cameron, Katherine E. Battle, Anita Nandi, Suzanne H. Keddie, and Katherine A. Twohig
Subjects: Geography, Incidence (epidemiology), Polygon, Statistics, Covariate, medicine, Regression analysis, medicine.disease, Baseline (configuration management), Spatial analysis, Malaria, Unit (housing)
Abstract: SummaryAs malaria incidence decreases and more countries move towards elimination, maps of malaria risk in low prevalence areas are increasingly needed. For low burden areas, disaggregation regression models have been developed to estimate risk at high spatial resolution from routine surveillance reports aggregated by administrative unit polygons. However, in areas with both routine surveillance data and prevalence surveys, models that make use of the spatial information from prevalence point-surveys have great potential. Using case studies in Indonesia, Senegal and Madagascar, we compare two methods for incorporating point-level, spatial information into disaggregation regression models. The first simply fits a Gaussian random field to prevalence point-surveys to create a new covariate. The second is a multi-likelihood model that is fitted jointly to prevalence point-surveys and polygon incidence data. We find that the simple model generally performs better than a baseline disaggregation model while the joint model performance was mixed. More generally, our results demonstrate that combining these types of data improves estimates of malaria incidence.
Published: 2020
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17. A global map of travel time to cities to assess inequalities in accessibility in 2015

Author: Nancy Fullman, Donal Bisanzio, Harry S. Gibson, E. Poyart, Allison Lieber, Katherine E. Battle, Hancher, William H. Temperley, Daniel J. Weiss, Tim C.D. Lucas, Jennifer Rozier, Su Yun Kang, Rosalind E. Howes, Samir Bhatt, Ursula Dalrymple, S. Belchior, Lucy S. Tusting, Bonnie Mappin, S. Peedell, Peter W. Gething, Ewan Cameron, Andrew Nelson, Department of Natural Resources, Faculty of Geo-Information Science and Earth Observation, and UT-I-ITC-FORAGES
Subjects: Internationality, Time Factors, Urban Population, 010504 meteorology & atmospheric sciences, Inequality, Health Status, media_common.quotation_subject, Developing country, 010501 environmental sciences, 01 natural sciences, Spatio-Temporal Analysis, Regional science, Humans, Cities, Healthcare Disparities, Socioeconomic status, 0105 earth and related environmental sciences, media_common, Developing world, Sustainable development, Travel, Multidisciplinary, Equity (economics), Geography, Global Map, Livelihood, 22/4 OA procedure, Socioeconomic Factors, Sustainability, ITC-ISI-JOURNAL-ARTICLE, Educational Status, Maps as Topic
Abstract: The economic and man-made resources that sustain human wellbeing are not distributed evenly across the world, but are instead heavily concentrated in cities. Poor access to opportunities and services offered by urban centres (a function of distance, transport infrastructure, and the spatial distribution of cities) is a major barrier to improved livelihoods and overall development. Advancing accessibility worldwide underpins the equity agenda of ‘leaving no one behind’ established by the Sustainable Development Goals of the United Nations1. This has renewed international efforts to accurately measure accessibility and generate a metric that can inform the design and implementation of development policies. The only previous attempt to reliably map accessibility worldwide, which was published nearly a decade ago2, predated the baseline for the Sustainable Development Goals and excluded the recent expansion in infrastructure networks, particularly in lower-resource settings. In parallel, new data sources provided by Open Street Map and Google now capture transportation networks with unprecedented detail and precision. Here we develop and validate a map that quantifies travel time to cities for 2015 at a spatial resolution of approximately one by one kilometre by integrating ten global-scale surfaces that characterize factors affecting human movement rates and 13,840 high-density urban centres within an established geospatial-modelling framework. Our results highlight disparities in accessibility relative to wealth as 50.9% of individuals living in low-income settings (concentrated in sub-Saharan Africa) reside within an hour of a city compared to 90.7% of individuals in high-income settings. By further triangulating this map against socioeconomic datasets, we demonstrate how access to urban centres stratifies the economic, educational, and health status of humanity.
Published: 2018
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18. Global burden of 369 diseases and injuries in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019

Author: Sorin Hostiuc, Shaun Wen Huey Lee, Jorge R. Ledesma, Carsten Flohr, Masoumeh Sadeghi, João Mauricio Castaldelli-Maia, Behzad Karami Matin, Cyrus Alinia, Mehdi Bohluli, Félix Carvalho, Yun Jin Kim, Catalina Liliana Andrei, Seyyed Meysam Mousavi, Bernhard T. Baune, Ehsan Ahmadpour, Dinh-Toi Chu, Beatrix Haddock, Gianfranco Alicandro, Vasily Vlassov, Mohammad Taghi Khodayari, Gianna Gayle Herrera Amul, Arash Tehrani-Banihashemi, Govinda Prasad Dhungana, Fereshteh Ansari, Michael K. Hole, Azeem Majeed, Iman Halvaei, Saqib Ali, Arianna Maever L. Amit, Tomas Y. Yeheyis, John S. Ji, Martin McKee, Jamileh Shadid, Leonardo Roever, Peng Jia, Ettore Beghi, Pablo M. Lavados, Young Eun Kim, Vahid Alipour, Sowmya J. Rao, Ahmad Daryani, Cathleen Keller, Ibrahim Abdollahpour, Nicole K. DeCleene, Ebrahim Babaee, Saman Esmaeilnejad, Boris Bikbov, William M. Gardner, Lydia M. Haile, Luca Ronfani, Azalea M. Thomson, Irena Ilic, Ruth W. Kimokoti, Yingxi Zhao, Guoqing Hu, Mehran Shams-Beyranvand, Ilais Moreno Velásquez, Nathaniel J. Henry, Brijesh Sathian, Daniel Kim, Peter Memiah, Mohammad Hadi Abbasi, Andrea Farioli, Zahra Kamiab, Bolajoko O. Olusanya, Matthew C. Doxey, Tommi Vasankari, Hamideh Salimzadeh, Luisa Sorio Flor, Priya Rathi, Shanshan Li, Tanvir M. Huda, Dillon O Sylte, Rosario Cárdenas, Agegnehu Bante, Helen Ippolito, Alyssa Acebedo, Jeffrey D. Stanaway, Anwar Faraj, João Pedro Silva, Amin Mousavi Khaneghah, Pushpendra Kumar, Sangram Kishor Patel, Josephine W. Ngunjiri, Holly E. Erskine, Eugene Sobngwi, Filippo Ariani, Shane D. Morrison, Mohammad Aghaali, Meghan D. Mooney, Vera Marisa Costa, Palash Chandra Banik, Rupak Desai, Ken Takahashi, Maigeng Zhou, Morteza Oladnabi, Bogdan Oancea, Daniela Ribeiro, Mohammad Farahmand, Irmina Maria Michalek, Yetunde O. John-Akinola, Khem Narayan Pokhrel, Emilie R Maddison, Syed Mohamed Aljunid, Damian G. Hoy, Hosni Salem, V. Prakash, Shuhei Nomura, Inga Dora Sigfusdottir, Anders Larsson, Sharareh Eskandarieh, Abdollah Mohammadian-Hafshejani, Somayeh Bohlouli, Joana Morgado-da-Costa, Siamak Sabour, Theo Vos, Han Yong Wunrow, Khaled Khatab, Alireza Zangeneh, Ann Kristin Knudsen, Marissa B Reitsma, Hannah J. Henrikson, Randah R. Hamadeh, Tuomo J. Meretoja, Ireneous N. Soyiri, Giuseppe Grosso, Ziyad Al-Aly, Taraneh Yousefinezhadi, Joseph L Ward, Roba Khundkar, Ricardo Santiago Gomez, Reza Malekzadeh, John J. McGrath, Sandra B. Munro, Shahin Soltani, Amy E. Peden, Rufus Akinyemi, Marcel Ausloos, Naohiro Yonemoto, Bogdan Wojtyniak, Ahmad Ghashghaee, Guilherme Borges, Sadia Bibi, Farhad Islami, Hamed Mirzaei, Mohammad Ali Sahraian, M. Ashworth Dirac, Hosna Janjani, Kairat Davletov, Hermann Brenner, Yuichiro Yano, Elissa M. Abrams, Ana Vukovic, Bartosz Miazgowski, Jobert Richie Nansseu, Jennifer O Lam, Mona Pathak, Leeberk Raja Inbaraj, Thirunavukkarasu Sathish, Asadollah Gholamian, Carlos A Castañeda-Orjuela, Babak Eshrati, Edgar Denova-Gutiérrez, Atte Meretoja, Lorenzo Monasta, Ronan A. Lyons, Neda Kianipour, Desalegn Getnet Demsie, Yasir Waheed, Desta Debalkie Atnafu, Davide Sattin, Kevin S Ikuta, Ghobad Moradi, Srinivas Goli, Krittika Bhattacharyya, Mika Kivimäki, Christopher Troeger, Jordi Alonso, Alireza Ahmadi, Navid Manafi, Caroline Stein, Songhomitra Panda-Jonas, Jason Nguyen, Moses K. Muriithi, Aziz Rezapour, Ismael R. Campos-Nonato, Adrian Pana, H. Dean Hosgood, Noore Alam, James L. Fisher, Mariam Molokhia, Susan F. Rumisha, Ernoiz Antriyandarti, Ayman Grada, Emma Nichols, Babak Asghari, André Luiz Sena Guimarães, Ferrán Catalá-López, Aletta E. Schutte, Fiona B. Bennitt, Maciej Banach, Antonio Biondi, Donal Bisanzio, Josip Car, Ronny Westerman, Shafiu Mohammed, Biniyam Sahiledengle Geberemariyam, Kenji Shibuya, Meghdad Pirsaheb, Milena Santric-Milicevic, Karen M. Tabb, Paula Moraga, Soheil Hassanipour, Hasan Yusefzadeh, Avina Vongpradith, Dara K. Mohammad, Ralph Maddison, Babak Moazen, Getachew Mullu Kassa, Rahman Shiri, Fernando Neves Hugo, Hmwe H Kyu, Zachary V Dingels, Florian Fischer, Valentin Yurievich Skryabin, Rafael Tabarés-Seisdedos, Massimo Cirillo, Nikita Otstavnov, Robert C. Reiner, Van C. Lansingh, Rodrigo Sarmiento-Suarez, Ashkan Afshin, Benjamin A Stark, Mohsen Abbasi-Kangevari, Natalie C. Galles, Behnam Heidari, Eun-Kee Park, Mohammad Ali Jahani, Suzanne Polinder, Mahalaqua Nazli Khatib, Farhad Jadidi-Niaragh, Amir Radfar, Mowafa Househ, Derrick A Bennett, Gaorui Guo, Hesam Ghiasvand, Taweewat Wiangkham, Tamás Joó, Cristiana Abbafati, Kathryn Mei Ming Lau, Anita K. Nandi, Miklós Szócska, Manasi Kumar, Eduardo A. Undurraga, Oladimeji M. Adebayo, Simon Yadgir, Victor Aboyans, Justin J. Lang, Catherine O. Johnson, Soewarta Kosen, Carla Sofia e.Sá Farinha, Marcos Roberto Tovani-Palone, Kamarul Imran Musa, Farshad Pourmalek, Kiomars Sharafi, Heather Orpana, Samuel B. Albertson, Mahdi Afshari, Nicholas J K Breitborde, Nelson J. Alvis-Zakzuk, Adrian Oţoiu, Iván Landires, Robert G. Weintraub, Kidanemaryam Berhe, André Faro, Sophia Emmons-Bell, Lauren E. Schaeffer, Alexandre C. Pereira, Mehdi Naderi, Yordanos Gizachew Yeshitila, Mehdi Hosseinzadeh, Arash Etemadi, Oleguer Plana-Ripoll, Theodore Patrick Younker, Joemer C. Maravilla, Alireza Ansari-Moghaddam, Borhan Mansouri, Narayanaswamy Venketasubramanian, Seyed Mohammad Kazem Aghamir, Linda Morales, Amanda Deen, Noushin Mohammadifard, Obinna Onwujekwe, Ai Koyanagi, Michele Nguyen, Chieh Han, Kiana Ramezanzadeh, Mika Shigematsu, Mohammed Shannawaz, Khurshid Alam, Javad Nazari, Bryan L. Sykes, Rajat Das Gupta, Stephen S Lim, Lingkan Barua, Zubair Kabir, Michael Brauer, Afarin Rahimi-Movaghar, Deepa Jahagirdar, Kaja Abbas, Gholamreza Bazmandegan, Mark A. Stokes, Rajaa Al-Raddadi, Kanyin L. Ong, Kate Causey, Ahmed Omar Bali, Matilde Leonardi, Jeffrey V. Lazarus, Wondimeneh Shibabaw Shiferaw, André Karch, Blair R. Bumgarner, Nelson Alvis-Guzman, Jennifer H MacLachlan, Saeed Amini, Parvaiz A Koul, Blessing J. Akombi, Ro Ting Lin, Dabere Nigatu, Alaa Badawi, Flavia M. Cicuttini, Deanna Anderlini, Claudio Alberto Dávila-Cervantes, Rupert R A Bourne, Tanuj Kanchan, Catherine P. Benziger, Tahiya Alam, M. Mofizul Islam, Muktar Omer Omer, Leila Zaki, Mehdi Mirzaei-Alavijeh, Inbal Salz, Katharine J Looker, Shiwei Liu, Fatemeh Amiri, Christopher R. Cederroth, Mitra Abbasifard, Hamidreza Pazoki Toroudi, Gbenga A. Kayode, Antonio Luiz Pinho Ribeiro, Krishna Kumar Aryal, Mu'awiyyah Babale Sufiyan, Mohamed M. Gad, Assefa Desalew, Lidia Morawska, Davood Anvari, Mohammad Reza Salahshoor, Hadi Pourjafar, Abdu A. Adamu, Maryam Adabi, Zulfiqar A. Bhutta, Jessica A. Cruz, Foad Abd-Allah, Amir Hasanzadeh, Jordan Weiss, Maryam Ghadimi, Seyed-Mohammad Fereshtehnejad, Serge Resnikoff, Joanna L Whisnant, Kelly Compton, Priya Parmar, Sanjay Basu, Leila R Kalankesh, Nickolas Reinig, Ana Maria Mantilla Herrera, Fatemeh Rajati, Damian Santomauro, Mojisola Oluwasanu, Sheikh Mohammed Shariful Islam, David M. Pereira, Joht Singh Chandan, Deepak Kumar Pasupula, Aristidis Tsatsakis, Hoa Thi Do, Whitney L. Teagle, Hans W. Hoek, James Leigh, Morteza Arab-Zozani, Yasser Vasseghian, Stephanie R M Zimsen, Charlie Ashbaugh, Fariba Dorostkar, Abdelrahman Ibrahim Abushouk, Mikk Jürisson, Golnaz Heidari, Kala M. Mehta, Saeed Shahabi, Sarah Wulf Hanson, Nizal Sarrafzadegan, Dharmesh Kumar Lal, Hai Quang Pham, Aleksandr Y. Aravkin, Joshua A. Salomon, David C. Schwebel, Milena Ilic, Kareha M Agesa, Jost B. 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L., Department of Public Health, Clinicum, Department of Neurosciences, HUS Comprehensive Cancer Center, Environmental Sciences, Public Health, Real World Studies in PharmacoEpidemiology, -Genetics, -Economics and -Therapy (PEGET), Value, Affordability and Sustainability (VALUE), Microbes in Health and Disease (MHD), Sálfræðideild (HR), Department of Psychology (RU), Samfélagssvið (HR), School of Social Sciences (RU), Háskólinn í Reykjavík, Reykjavik University, GBD 2019 Diseases and Injuries Collaborator, Violante FS, Department of Earth Observation Science, Faculty of Geo-Information Science and Earth Observation, and UT-I-ITC-ACQUAL
Subjects: Male, Life expectancy, Disability-Adjusted Life Year, Diseases, Disease, communicable disease, systematic analysis, Global Burden of Disease, 0302 clinical medicine, 80 and over, Medicine, 10. No inequality, Child, 11 Medical and Health Sciences, injuries, Aged, 80 and over, education.field_of_study, Sjúkdómar, DEMENTIA, FALLS, General Medicine, Forvarnir, 3. Good health, Child, Preschool, Human, GBD, Population health, 03 medical and health sciences, SDG 3 - Good Health and Well-being, Humans, Global Burden of Disease Study, education, Aged, Spatial Analysis, Global burden, Disability, Prevention, DISABILITY, Infant, Spatial Analysi, Mortality rate, Global Burden of Disease, Diseases, Injuries, Systematic analysis, PREVENTION, Years of potential life lost, Risk factors, Disease study, ITC-ISI-JOURNAL-ARTICLE, RISK-FACTORS, Clinical Medicine, RA, Demography, Fötlun, Dánartíðni, Áhættuþættir, 030204 cardiovascular system & hematology, Risk Factors, Cause of Death, Global health, 030212 general & internal medicine, 1. No poverty, Disability-Adjusted Life Years, Public Health, Global Health, Social Medicine and Epidemiology, Middle Aged, 3142 Public health care science, environmental and occupational health, Adolescent, Adult, Age Distribution, Female, Infant, Newborn, Young Adult, Lýðheilsa, CLINICAL-TRIALS, Population, Settore MED/01 - Statistica Medica, diseases, ITC-HYBRID, Heilbrigðisvísindi, General & Internal Medicine, Mortality, Preschool, Disease burden, business.industry, Risk Factor, Klinisk medicin, Newborn, purl.org/pe-repo/ocde/ford#3.02.00 [https], Folkhälsovetenskap, global hälsa, socialmedicin och epidemiologi, Áverkar, Systematic analysis, NA, business
Abstract: Publisher's version (útgefin grein), Background In an era of shifting global agendas and expanded emphasis on non-communicable diseases and injuries along with communicable diseases, sound evidence on trends by cause at the national level is essential. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) provides a systematic scientific assessment of published, publicly available, and contributed data on incidence, prevalence, and mortality for a mutually exclusive and collectively exhaustive list of diseases and injuries. Methods GBD estimates incidence, prevalence, mortality, years of life lost (YLLs), years lived with disability (YLDs), and disability-adjusted life-years (DALYs) due to 369 diseases and injuries, for two sexes, and for 204 countries and territories. Input data were extracted from censuses, household surveys, civil registration and vital statistics, disease registries, health service use, air pollution monitors, satellite imaging, disease notifications, and other sources. Cause-specific death rates and cause fractions were calculated using the Cause of Death Ensemble model and spatiotemporal Gaussian process regression. Cause-specific deaths were adjusted to match the total all-cause deaths calculated as part of the GBD population, fertility, and mortality estimates. Deaths were multiplied by standard life expectancy at each age to calculate YLLs. A Bayesian meta-regression modelling tool, DisMod-MR 2.1, was used to ensure consistency between incidence, prevalence, remission, excess mortality, and cause-specific mortality for most causes. Prevalence estimates were multiplied by disability weights for mutually exclusive sequelae of diseases and injuries to calculate YLDs. We considered results in the context of the Socio-demographic Index (SDI), a composite indicator of income per capita, years of schooling, and fertility rate in females younger than 25 years. Uncertainty intervals (UIs) were generated for every metric using the 25th and 975th ordered 1000 draw values of the posterior distribution. Findings Global health has steadily improved over the past 30 years as measured by age-standardised DALY rates. After taking into account population growth and ageing, the absolute number of DALYs has remained stable. Since 2010, the pace of decline in global age-standardised DALY rates has accelerated in age groups younger than 50 years compared with the 1990-2010 time period, with the greatest annualised rate of decline occurring in the 0-9-year age group. Six infectious diseases were among the top ten causes of DALYs in children younger than 10 years in 2019: lower respiratory infections (ranked second), diarrhoeal diseases (third), malaria (fifth), meningitis (sixth), whooping cough (ninth), and sexually transmitted infections (which, in this age group, is fully accounted for by congenital syphilis; ranked tenth). In adolescents aged 10-24 years, three injury causes were among the top causes of DALYs: road injuries (ranked first), self-harm (third), and interpersonal violence (fifth). Five of the causes that were in the top ten for ages 10-24 years were also in the top ten in the 25-49-year age group: road injuries (ranked first), HIV/AIDS (second), low back pain (fourth), headache disorders (fifth), and depressive disorders (sixth). In 2019, ischaemic heart disease and stroke were the top-ranked causes of DALYs in both the 50-74-year and 75-years-and-older age groups. Since 1990, there has been a marked shift towards a greater proportion of burden due to YLDs from non-communicable diseases and injuries. In 2019, there were 11 countries where non-communicable disease and injury YLDs constituted more than half of all disease burden. Decreases in age-standardised DALY rates have accelerated over the past decade in countries at the lower end of the SDI range, while improvements have started to stagnate or even reverse in countries with higher SDI. Interpretation As disability becomes an increasingly large component of disease burden and a larger component of health expenditure, greater research and development investment is needed to identify new, more effective intervention strategies. With a rapidly ageing global population, the demands on health services to deal with disabling outcomes, which increase with age, will require policy makers to anticipate these changes. The mix of universal and more geographically specific influences on health reinforces the need for regular reporting on population health in detail and by underlying cause to help decision makers to identify success stories of disease control to emulate, as well as opportunities to improve. Copyright (C) 2020 The Author(s). Published by Elsevier Ltd., Research reported in this publication was supported by the Bill & Melinda Gates Foundation; the University of Melbourne; Queensland Department of Health, Australia; the National Health and Medical Research Council, Australia; Public Health England; the Norwegian Institute of Public Health; St Jude Children's Research Hospital; the Cardiovascular Medical Research and Education Fund; the National Institute on Ageing of the National Institutes of Health (award P30AG047845); and the National Institute of Mental Health of the National Institutes of Health (award R01MH110163). The content is solely the responsibility of the authors and does not necessarily represent the official views of the funders. The authors alone are responsible for the views expressed in this Article and they do not necessarily represent the views, decisions, or policies of the institutions with which they are affiliated, the National Health Service (NHS), the National Institute for Health Research (NIHR), the UK Department of Health and Social Care, or Public Health England; the United States Agency for International Development (USAID), the US Government, or MEASURE Evaluation; or the European Centre for Disease Prevention and Control (ECDC). This research used data from the Chile National Health Survey 2003, 2009-10, and 2016-17. The authors are grateful to the Ministry of Health, the survey copyright owner, for allowing them to have the database. All results of the study are those of the authors and in no way committed to the Ministry. The Costa Rican Longevity and Healthy Aging Study project is a longitudinal study by the University of Costa Rica's Centro Centroamericano de Poblacion and Instituto de Investigaciones en Salud, in collaboration with the University of California at Berkeley. The original pre-1945 cohort was funded by the Wellcome Trust (grant 072406), and the 1945-55 Retirement Cohort was funded by the US National Institute on Aging (grant R01AG031716). The principal investigators are Luis Rosero-Bixby and William H Dow and co-principal investigators are Xinia Fernandez and Gilbert Brenes. The accuracy of the authors' statistical analysis and the findings they report are not the responsibility of ECDC. ECDC is not responsible for conclusions or opinions drawn from the data provided. ECDC is not responsible for the correctness of the data and for data management, data merging and data collation after provision of the data. ECDC shall not be held liable for improper or incorrect use of the data. The Health Behaviour in School-Aged Children (HBSC) study is an international study carried out in collaboration with WHO/EURO. The international coordinator of the 1997-98, 2001-02, 2005-06, and 2009-10 surveys was Candace Currie and the databank manager for the 1997-98 survey was Bente Wold, whereas for the following surveys Oddrun Samdal was the databank manager. A list of principal investigators in each country can be found on the HBSC website. Data used in this paper come from the 2009-10 Ghana Socioeconomic Panel Study Survey, which is a nationally representative survey of more than 5000 households in Ghana. The survey is a joint effort undertaken by the Institute of Statistical, Social and Economic Research (ISSER) at the University of Ghana and the Economic Growth Centre (EGC) at Yale University. It was funded by EGC. ISSER and the EGC are not responsible for the estimations reported by the analysts. The Palestinian Central Bureau of Statistics granted the researchers access to relevant data in accordance with license number SLN2014-3-170, after subjecting data to processing aiming to preserve the confidentiality of individual data in accordance with the General Statistics Law, 2000. The researchers are solely responsible for the conclusions and inferences drawn upon available data. Data for this research was provided by MEASURE Evaluation, funded by USAID. The authors thank the Russia Longitudinal Monitoring Survey, conducted by the National Research University Higher School of Economics and ZAO Demoscope together with Carolina Population Center, University of North Carolina at Chapel Hill and the Institute of Sociology, Russia Academy of Sciences for making data available. This paper uses data from the Bhutan 2014 STEPS survey, implemented by the Ministry of Health with the support of WHO; the Kuwait 2006 and 2014 STEPS surveys, implemented by the Ministry of Health with the support of WHO; the Libya 2009 STEPS survey, implemented by the Secretariat of Health and Environment with the support of WHO; the Malawi 2009 STEPS survey, implemented by Ministry of Health with the support of WHO; and the Moldova 2013 STEPS survey, implemented by the Ministry of Health, the National Bureau of Statistics, and the National Center of Public Health with the support of WHO. This paper uses data from Survey of Health, Ageing and Retirement in Europe (SHARE) Waves 1 (DOI:10.6103/SHARE. w1.700), 2 (10.6103/SHARE.w2.700), 3 (10.6103/SHARE.w3.700), 4 (10.6103/SHARE.w4.700), 5 (10.6103/SHARE.w5.700), 6 (10.6103/SHARE.w6.700), and 7 (10.6103/SHARE.w7.700); see Borsch-Supan and colleagues (2013) for methodological details. The SHARE data collection has been funded by the European Commission through FP5 (QLK6-CT-2001-00360), FP6 (SHARE-I3: RII-CT-2006-062193, COMPARE: CIT5-CT-2005-028857, SHARELIFE: CIT4-CT-2006-028812), FP7 (SHARE-PREP: GA N degrees 211909, SHARE-LEAP: GA N degrees 227822, SHARE M4: GA N degrees 261982) and Horizon 2020 (SHARE-DEV3: GA N degrees 676536, SERISS: GA N degrees 654221) and by DG Employment, Social Affairs & Inclusion. Additional funding from the German Ministry of Education and Research, the Max Planck Society for the Advancement of Science, the US National Institute on Aging (U01_AG09740-13S2, P01_AG005842, P01_AG08291, P30_AG12815, R21_AG025169, Y1-AG-4553-01, IAG_BSR06-11, OGHA_04-064, HHSN271201300071C), and from various national funding sources is gratefully acknowledged. This study has been realised using the data collected by the Swiss Household Panel, which is based at the Swiss Centre of Expertise in the Social Sciences. The project is financed by the Swiss National Science Foundation. The United States Aging, Demographics, and Memory Study is a supplement to the Health and Retirement Study (HRS), which is sponsored by the National Institute of Aging (grant number NIA U01AG009740). It was conducted jointly by Duke University and the University of Michigan. The HRS is sponsored by the National Institute on Aging (grant number NIA U01AG009740) and is conducted by the University of Michigan. This paper uses data from Add Health, a program project designed by J Richard Udry, Peter S Bearman, and Kathleen Mullan Harris, and funded by a grant P01-HD31921 from the Eunice Kennedy Shriver National Institute of Child Health and Human Development, with cooperative funding from 17 other agencies. Special acknowledgment is due to Ronald R Rindfuss and Barbara Entwisle for assistance in the original design. Information on how to obtain the Add Health data files is available on the Add Health website. No direct support was received from grant P01-HD31921 for this analysis. The data reported here have been supplied by the United States Renal Data System. The interpretation and reporting of these data are the responsibility of the authors and in no way should be seen as an official policy or interpretation of the US Government. Collection of data for the Mozambique National Survey on the Causes of Death 2007-08 was made possible by USAID under the terms of cooperative agreement GPO-A-00-08-000_D3-00. This manuscript is based on data collected and shared by the International Vaccine Institute (IVI) from an original study IVI conducted. L G Abreu acknowledges support from Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior (Brazil; finance code 001) and Conselho Nacional de Desenvolvimento Cientifico e Tecnologico (CNPq, a Brazilian funding agency). I N Ackerman was supported by a Victorian Health and Medical Research Fellowship awarded by the Victorian Government. O O Adetokunboh acknowledges the South African Department of Science and Innovation and the National Research Foundation. A Agrawal acknowledges the Wellcome Trust DBT India Alliance Senior Fellowship. S M 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. M Ausloos, C Herteliu, and A Pana acknowledge partial support by a grant of the Romanian National Authority for Scientific Research and Innovation, CNDS-UEFISCDI, project number PN-III-P4-ID-PCCF-2016-0084. A Badawi is supported by the Public Health Agency of Canada. D A Bennett was supported by the NIHR Oxford Biomedical Research Centre. R Bourne acknowledges the Brien Holden Vision Institute, University of Heidelberg, Sightsavers, Fred Hollows Foundation, and Thea Foundation. G B Britton and I Moreno Velasquez were supported by the Sistema Nacional de Investigacion, SNI-SENACYT, Panama. R Buchbinder was supported by an Australian National Health and Medical Research Council (NHMRC) Senior Principal Research Fellowship. J J Carrero was supported by the Swedish Research Council (2019-01059). F Carvalho acknowledges UID/MULTI/04378/2019 and UID/QUI/50006/2019 support with funding from FCT/MCTES through national funds. A R Chang was supported by National Institutes of Health/National Institute of Diabetes and Digestive and Kidney Diseases grant K23 DK106515. V M Costa acknowledges the grant SFRH/BHD/110001/2015, received by Portuguese national funds through Fundacao para a Ciencia e Tecnologia, IP, under the Norma Transitaria DL57/2016/CP1334/CT0006. A Douiri acknowledges support and funding from the National Institute for Health Research Collaboration for Leadership in Applied Health Research and Care South London at King's College Hospital NHS Foundation Trust and the Royal College of Physicians, and support from the NIHR Biomedical Research Centre based at Guy's and St Thomas' NHS Foundation Trust and King's College London. B B Duncan acknowledges grants from the Foundation for the Support of Research of the State of Rio Grande do Sul (IATS and PrInt) and the Brazilian Ministry of Health. H E Erskine is the recipient of an Australian NHMRC Early Career Fellowship grant (APP1137969). A J Ferrari was supported by a NHMRC Early Career Fellowship grant (APP1121516). H E Erskine and A J Ferrari are employed by and A M Mantilla-Herrera and D F Santomauro affiliated with the Queensland Centre for Mental Health Research, which receives core funding from the Queensland Department of Health. M L Ferreira holds an NHMRC Research Fellowship. C Flohr was supported by the NIHR Biomedical Research Centre based at Guy's and St Thomas' NHS Foundation Trust. M Freitas acknowledges financial support from the EU (European Regional Development Fund [FEDER] funds through COMPETE POCI-01-0145-FEDER-029248) and National Funds (Fundacao para a Ciencia e Tecnologia) through project PTDC/NAN-MAT/29248/2017. A L S Guimaraes acknowledges support from CNPq. C Herteliu was partially supported by a grant co-funded by FEDER through Operational Competitiveness Program (project ID P_40_382). P Hoogar acknowledges Centre for Bio Cultural Studies, Directorate of Research, Manipal Academy of Higher Education and Centre for Holistic Development and Research, Kalaghatagi. F N Hugo acknowledges the Visiting Professorship, PRINT Program, CAPES Foundation, Brazil. B-F Hwang was supported by China Medical University (CMU107-Z-04), Taichung, Taiwan. S M S Islam was funded by a National Heart Foundation Senior Research Fellowship and supported by Deakin University. R Q Ivers was supported by a research fellowship from the National Health and Medical Research Council of Australia. M Jakovljevic acknowledges the Serbian part of this GBD-related contribution was co-funded through Grant OI175014 of the Ministry of Education Science and Technological Development of the Republic of Serbia. P Jeemon was supported by a Clinical and Public Health intermediate fellowship (grant number IA/CPHI/14/1/501497) from the Wellcome Trust-Department of Biotechnology, India Alliance (2015-20). O John is a recipient of UIPA scholarship from University of New South Wales, Sydney. S V Katikireddi acknowledges funding from a NRS Senior Clinical Fellowship (SCAF/15/02), the Medical Research Council (MC_UU_12017/13, MC_UU_12017/15), and the Scottish Government Chief Scientist Office (SPHSU13, SPHSU15). C Kieling is a CNPq researcher and a UK Academy of Medical Sciences Newton Advanced Fellow. Y J Kim was supported by Research Management Office, Xiamen University Malaysia (XMUMRF/2018-C2/ITCM/00010). K Krishan is supported by UGC Centre of Advanced Study awarded to the Department of Anthropology, Panjab University, Chandigarh, India. M Kumar was supported by K43 TW 010716 FIC/NIMH. B Lacey acknowledges support from the NIHR Oxford Biomedical Research Centre and the BHF Centre of Research Excellence, Oxford. J V Lazarus was supported by a Spanish Ministry of Science, Innovation and Universities Miguel Servet grant (Instituto de Salud Carlos III [ISCIII]/ESF, the EU [CP18/00074]). K J Looker thanks the NIHR Health Protection Research Unit in Evaluation of Interventions at the University of Bristol, in partnership with Public Health England, for research support. S Lorkowski was funded by the German Federal Ministry of Education and Research (nutriCARD, grant agreement number 01EA1808A). R A Lyons is supported by Health Data Research UK (HDR-9006), which is funded by the UK Medical Research Council, Engineering and Physical Sciences Research Council, Economic and Social Research Council, NIHR (England), Chief Scientist Office of the Scottish Government Health and Social Care Directorates, Health and Social Care Research and Development Division (Welsh Government), Public Health Agency (Northern Ireland), British Heart Foundation, and Wellcome Trust. J J McGrath is supported by the Danish National Research Foundation (Niels Bohr Professorship), and the Queensland Health Department (via West Moreton HHS). P T N Memiah acknowledges support from CODESRIA. U O Mueller gratefully acknowledges funding by the German National Cohort Study BMBF grant number 01ER1801D. S Nomura acknowledges the Ministry of Education, Culture, Sports, Science, and Technology of Japan (18K10082). A Ortiz was supported by ISCIII PI19/00815, DTS18/00032, ISCIII-RETIC REDinREN RD016/0009 Fondos FEDER, FRIAT, Comunidad de Madrid B2017/BMD-3686 CIFRA2-CM. These funding sources had no role in the writing of the manuscript or the decision to submit it for publication. S B Patten was supported by the Cuthbertson & Fischer Chair in Pediatric Mental Health at the University of Calgary. G C Patton was supported by an aNHMRC Senior Principal Research Fellowship. M R Phillips was supported in part by the National Natural Science Foundation of China (NSFC, number 81371502 and 81761128031). A Raggi, D Sattin, and S Schiavolin were supported by grants from the Italian Ministry of Health (Ricerca Corrente, Fondazione Istituto Neurologico C Besta, Linea 4-Outcome Research: dagli Indicatori alle Raccomandazioni Cliniche). P Rathi and B Unnikrishnan acknowledge Kasturba Medical College, Mangalore, Manipal Academy of Higher Education, Manipal. A L P Ribeiro was supported by Brazilian National Research Council, CNPq, and the Minas Gerais State Research Agency, FAPEMIG. D C Ribeiro was supported by The Sir Charles Hercus Health Research Fellowship (#18/111) Health Research Council of New Zealand. D Ribeiro acknowledges financial support from the EU (FEDER funds through the Operational Competitiveness Program; POCI-01-0145-FEDER-029253). P S Sachdev acknowledges funding from the NHMRC of Australia Program Grant. A M Samy was supported by a fellowship from the Egyptian Fulbright Mission Program. M M Santric-Milicevic acknowledges the Ministry of Education, Science and Technological Development of the Republic of Serbia (contract number 175087). R Sarmiento-Suarez received institutional support from Applied and Environmental Sciences University (Bogota, Colombia) and ISCIII (Madrid, Spain). A E Schutte received support from the South African National Research Foundation SARChI Initiative (GUN 86895) and Medical Research Council. S T S Skou is currently funded by a grant from Region Zealand (Exercise First) and a grant from the European Research Council under the EU's Horizon 2020 research and innovation program (grant agreement number 801790). J B Soriano is funded by Centro de Investigacion en Red de Enfermedades Respiratorias, ISCIII. R Tabares-Seisdedos was supported in part by the national grant PI17/00719 from ISCIII-FEDER. N Taveira was partially supported by the European & Developing Countries Clinical Trials Partnership, the EU (LIFE project, reference RIA2016MC-1615). S Tyrovolas was supported by the Foundation for Education and European Culture, the Sara Borrell postdoctoral programme (reference number CD15/00019 from ISCIII-FEDER). S B Zaman received a scholarship from the Australian Government research training programme in support of his academic career., "Peer Reviewed"
Published: 2020
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19. Mapping malaria seasonality in Madagascar using health facility data

Author: Katherine E. Battle, Sedera Aurélien Mioramalala, Ewan Cameron, Emma L. Collins, Mauricette Andriamananjara Nambinisoa, Rosalind E. Howes, Susan F. Rumisha, Suzanne H. Keddie, Fanjasoa Rakotomanana, Anita Nandi, Jennifer Rozier, Samir Bhatt, Harry S. Gibson, Chantal Hendriks, Daniel J. Weiss, Tim C.D. Lucas, Peter W. Gething, Rohan Arambepola, Su Yun Kang, and Michele Nguyen
Subjects: Data Analysis, 030231 tropical medicine, Indoor residual spraying, lcsh:Medicine, law.invention, 03 medical and health sciences, 0302 clinical medicine, Health facility, law, parasitic diseases, Madagascar, Medicine, Humans, 030212 general & internal medicine, Health facility data, biology, business.industry, Incidence, lcsh:R, Anopheles, Regression analysis, General Medicine, Vegetation, Seasonality, biology.organism_classification, medicine.disease, Malaria, Transmission (mechanics), Physical geography, Health Facilities, Seasons, Geostatistical model, business, Research Article
Abstract: Background Many malaria-endemic areas experience seasonal fluctuations in case incidence as Anopheles mosquito and Plasmodium parasite life cycles respond to changing environmental conditions. Identifying location-specific seasonality characteristics is useful for planning interventions. While most existing maps of malaria seasonality use fixed thresholds of rainfall, temperature, and/or vegetation indices to identify suitable transmission months, we construct a statistical modelling framework for characterising the seasonal patterns derived directly from monthly health facility data. Methods With data from 2669 of the 3247 health facilities in Madagascar, a spatiotemporal regression model was used to estimate seasonal patterns across the island. In the absence of catchment population estimates or the ability to aggregate to the district level, this focused on the monthly proportions of total annual cases by health facility level. The model was informed by dynamic environmental covariates known to directly influence seasonal malaria trends. To identify operationally relevant characteristics such as the transmission start months and associated uncertainty measures, an algorithm was developed and applied to model realisations. A seasonality index was used to incorporate burden information from household prevalence surveys and summarise ‘how seasonal’ locations are relative to their surroundings. Results Positive associations were detected between monthly case proportions and temporally lagged covariates of rainfall and temperature suitability. Consistent with the existing literature, model estimates indicate that while most parts of Madagascar experience peaks in malaria transmission near March–April, the eastern coast experiences an earlier peak around February. Transmission was estimated to start in southeast districts before southwest districts, suggesting that indoor residual spraying should be completed in the same order. In regions where the data suggested conflicting seasonal signals or two transmission seasons, estimates of seasonal features had larger deviations and therefore less certainty. Conclusions Monthly health facility data can be used to establish seasonal patterns in malaria burden and augment the information provided by household prevalence surveys. The proposed modelling framework allows for evidence-based and cohesive inferences on location-specific seasonal characteristics. As health surveillance systems continue to improve, it is hoped that more of such data will be available to improve our understanding and planning of intervention strategies.
Published: 2020

20. Author response: The origins and relatedness structure of mixed infections vary with local prevalence of P. falciparum malaria

Author: Daniel J. Weiss, Jacob Almagro-Garcia, Alistair Miles, Richard D. Pearson, Dominic P. Kwiatkowski, Michele Nguyen, Sha Joe Zhu, Roberto Amato, Tim C.D. Lucas, Peter W. Gething, Gil McVean, and Jason A. Hendry
Subjects: Geography, medicine, medicine.disease, Malaria, Demography, Mixed infection
Published: 2019
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21. Mapping the global prevalence, incidence, and mortality of Plasmodium falciparum, 2000-17: a spatial and temporal modelling study

Author: Ursula Dalrymple, Nancy Fullman, Emma L. Collins, Simon I. Hay, Naomi Gray, Joseph R Harris, Ryan M Barber, Tim C.D. Lucas, Ewan Cameron, Xie Rachel Kulikoff, David L. Smith, Harry S. Gibson, Michael Kutz, Susan F. Rumisha, Michael P Thorn, Chantal Huynh, Jennifer Rozier, Donal Bisanzio, Daniel A. Pfeffer, Rosalind E. Howes, Alan D. Lopez, Haidong Wang, Mohsen Naghavi, Sun Yun Kang, Ali H. Mokdad, Daniel C Casey, Katya Anne Shackelford, Anita Nandi, Theo Vos, Suzanne H. Keddie, Samir Bhatt, Katherine E. Battle, Stephen S Lim, Michele Nguyen, Daniel May, Puja C Rao, Katherine A. Twohig, Christopher J L Murray, Grant Nguyen, Amelia Bertozzi-Villa, Peter W. Gething, Daniel J. Weiss, Medical Research Council (MRC), and Bill & Melinda Gates Foundation
Subjects: AFRICA, Cross-sectional study, Plasmodium vivax, Population, 030204 cardiovascular system & hematology, Global Health, Article, 03 medical and health sciences, Medicine, General & Internal, MALARIA, 0302 clinical medicine, Spatio-Temporal Analysis, General & Internal Medicine, parasitic diseases, Global health, medicine, Prevalence, Humans, Organizational Objectives, 030212 general & internal medicine, Malaria, Falciparum, Mortality, education, 11 Medical and Health Sciences, Africa South of the Sahara, education.field_of_study, Science & Technology, biology, Incidence (epidemiology), Incidence, Plasmodium falciparum, General Medicine, medicine.disease, biology.organism_classification, Verbal autopsy, ENDEMICITY, Geography, Cross-Sectional Studies, Life Sciences & Biomedicine, Malaria, Demography
Abstract: Background: Since 2000, the scale-up of malaria control interventions has substantially reduced morbidity and mortality caused by the disease globally, fuelling bold aims for disease elimination. In tandem with increased availability of geospatially resolved data, malaria control programmes increasingly use high-resolution maps to characterise spatially heterogeneous patterns of disease risk and thus efficiently target areas of high burden. Methods: We updated and refined the Plasmodium falciparum parasite rate and clinical incidence models for sub-Saharan Africa, which rely on cross-sectional survey data for parasite rate and intervention coverage. For malaria endemic countries outside of sub-Saharan Africa, we produced estimates of parasite rate and incidence by applying an ecological downscaling approach to malaria incidence data acquired via routine surveillance. Mortality estimates were derived by linking incidence to systematically derived vital registration and verbal autopsy data. Informed by high-resolution covariate surfaces, we estimated P falciparum parasite rate, clinical incidence, and mortality at national, subnational, and 5 × 5 km pixel scales with corresponding uncertainty metrics. Findings: We present the first global, high-resolution map of P falciparum malaria mortality and the first global prevalence and incidence maps since 2010. These results are combined with those for Plasmodium vivax (published separately) to form the malaria estimates for the Global Burden of Disease 2017 study. The P falciparum estimates span the period 2000–17, and illustrate the rapid decline in burden between 2005 and 2017, with incidence declining by 27·9% and mortality declining by 42·5%. Despite a growing population in endemic regions, P falciparum cases declined between 2005 and 2017, from 232·3 million (95% uncertainty interval 198·8–277·7) to 193·9 million (156·6–240·2) and deaths declined from 925 800 (596 900–1 341 100) to 618 700 (368 600–952 200). Despite the declines in burden, 90·1% of people within sub-Saharan Africa continue to reside in endemic areas, and this region accounted for 79·4% of cases and 87·6% of deaths in 2017. Interpretation: High-resolution maps of P falciparum provide a contemporary resource for informing global policy and malaria control planning, programme implementation, and monitoring initiatives. Amid progress in reducing global malaria burden, areas where incidence trends have plateaued or increased in the past 5 years underscore the fragility of hard-won gains against malaria. Efforts towards elimination should be strengthened in such areas, and those where burden remained high throughout the study period.
Published: 2018

22. ROLE of inter-related population-level host traits in determining pathogen richness and zoonotic risk

Author: Hilde Wilkinson-Herbots, Kate E. Jones, and Tim C.D. Lucas
Subjects: Persistence (psychology), Population level, Host (biology), Ecology, Population size, Metapopulation, Species richness, Biology, Pathogen, Zoonotic disease
Abstract: Zoonotic diseases are an increasingly important source of human infectious diseases, and host pathogen richness of reservoir host species is a critical driver of spill-over risk. Population-level traits of hosts such as population size, host density and geographic range size have all been shown to be important determinants of host pathogen richness. However, empirically identifying the independent influences of these traits has proven difficult as many of these traits directly depend on each other. Here we develop a mechanistic, metapopulation, susceptible-infected-recovered model to identify the independent influences of these population-level traits on the ability of a newly evolved pathogen to invade and persist in host populations in the presence of an endemic pathogen. We use bats as a case study as they are highly social and an important source of zoonotic disease. We show that larger populations and group sizes had a greater influence on the chances of pathogen invasion and persistence than increased host density or the number of groups. As anthropogenic change affects these traits to different extents, this increased understanding of how traits independently determine pathogen richness will aid in predicting future zoonotic spill-over risk.
Published: 2017
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23. Challenges of Using Bioacoustics to Globally Monitor Bats

Author: Kim Mroz, Kate E. Jones, Charlotte L. Walters, Tim C.D. Lucas, Alanna Collen, and Catherine A. Sayer
Subjects: Engineering, education.field_of_study, Operations research, business.industry, Bioacoustics, media_common.quotation_subject, Environmental resource management, Population, Biodiversity, Global change, Identification (information), Public participation, business, education, Citizen engagement, Diversity (politics), media_common
Abstract: As bats are important biodiversity indicators, monitoring their populations is becoming increasingly important to understand the impacts of global change. Bats leak information about themselves into the environment in the form of ultrasonic calls. Using these calls to globally survey bat populations may offer a more efficient alternative or addition to traditional methods for bat monitoring. We identify three of the most important challenges to the development of a global acoustic bat monitoring programme: the robust identification of acoustic signals, the ability to develop meaningful population trends from acoustic activity, and engaging a global audience to take part. We discuss the rapid progress in all three of these areas, for example, development of comprehensive call libraries, quantitative regional tools for call identification, new statistical methods to monitor trends and a resurgence of interest in the public participation in science and monitoring of nature. We also discuss the important gaps in our knowledge and where resources could be best focused to build a global programme. Specifically, tropical areas present a particular challenge: they have high species-richness; species acoustic diversity is poorly documented; call similarity of species is very high, making robust call identification more challenging; and traditionally these areas have had a lower citizen engagement in biodiversity monitoring.
Published: 2013
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24. A generalized random encounter model for estimating animal density with remote sensor data

Author: Kate E. Jones, Robin Freeman, Marcus J. Rowcliffe, Tim C.D. Lucas, and Elizabeth A. Moorcroft
Subjects: Acoustics and Ultrasonics, Arts and Humanities (miscellaneous), Computer science, Statistics, Wildlife, Biological system, Signal
Abstract: Acoustic detectors are commonly being used to monitor wildlife. Current estimators of abundance or density require recognition of individuals or the distance of the animal from the sensor, which is often difficult. The random encounter model (REM) has been successfully applied to count data without these requirements. However, count data from acoustic detectors do not fit the assumptions of the REM due to the directionality of animal signals. We developed a generalized REM (gREM), to estimate animal density from count data, derived for different combinations of sensor detection widths and animal signal widths. We tested the accuracy and precision of this model using simulations for different combinations of sensor detection and animal signal widths, number of captures, and animal movement models. The gREM produces accurate estimates of absolute animal density. However, larger sensor detection and animal signal widths, and larger number of captures give more precise estimates. Different animal movement models had no effect on the gREM. We conclude that the gREM provides an effective method to estimate animal densities in both marine and terrestrial environments. As acoustic detectors become more ubiquitous, the gREM will be increasingly useful for monitoring animal populations across broad spatial, temporal, and taxonomic scales.
Published: 2014
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24 results on '"Tim C.D. Lucas"'

1. Mapping malaria seasonality in Madagascar using health facility data

2. Responsible modelling: Unit testing for infectious disease epidemiology

3. Diving into the vertical dimension of elasmobranch movement ecology

4. Mapping Malaria by Sharing Spatial Information Between Incidence and Prevalence Data Sets

5. Engagement and adherence trade-offs for SARS-CoV-2 contact tracing

6. Dynamics of SARS-CoV-2 with waning immunity in the UK population

7. Predicted Impact of COVID-19 on Neglected Tropical Disease Programs and the Opportunity for Innovation

8. Global estimation of anti-malarial drug effectiveness for the treatment of uncomplicated Plasmodium falciparum malaria 1991-2019

9. A translucent box: interpretable machine learning in ecology

10. Engagement and adherence trade-offs for SARS-CoV-2 contact tracing

11. Responsible modelling: Unit testing for infectious disease epidemiology

12. Improving disaggregation models of malaria incidence by ensembling non-linear models of prevalence

13. A downscaling approach to compare COVID-19 count data from databases aggregated at different spatial scales

14. An imperfect tool: contact tracing could provide valuable reductions in COVID-19 transmission if good adherence can be achieved and maintained

15. Mapping the global endemicity and clinical burden of Plasmodium vivax, 2000–17: a spatial and temporal modelling study

16. Mapping malaria by sharing spatial information between incidence and prevalence datasets

17. A global map of travel time to cities to assess inequalities in accessibility in 2015

18. Global burden of 369 diseases and injuries in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019

19. Mapping malaria seasonality in Madagascar using health facility data

20. Author response: The origins and relatedness structure of mixed infections vary with local prevalence of P. falciparum malaria

21. Mapping the global prevalence, incidence, and mortality of Plasmodium falciparum, 2000-17: a spatial and temporal modelling study

22. ROLE of inter-related population-level host traits in determining pathogen richness and zoonotic risk

23. Challenges of Using Bioacoustics to Globally Monitor Bats

24. A generalized random encounter model for estimating animal density with remote sensor data

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24 results on '"Tim C.D. Lucas"'

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