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We investigate the emergence, mutation profile, and dissemination of SARS-CoV-2 lineage B.1.214.2, first identified in Belgium in January 2021. This variant, featuring a 3-amino acid insertion in the spike protein similar to the Omicron variant, was speculated to enhance transmissibility or immune evasion. Initially detected in international travelers, it substantially transmitted in Central Africa, Belgium, Switzerland, and France, peaking in April 2021. Our travel-aware phylogeographic analysis, incorporating travel history, estimated the origin to the Republic of the Congo, with primary European entry through France and Belgium, and multiple smaller introductions during the epidemic. We correlate its spread with human travel patterns and air passenger data. Further, upon reviewing national reports of SARS-CoV-2 outbreaks in Belgian nursing homes, we found this strain caused moderately severe outcomes (8.7% case fatality ratio). A distinct nasopharyngeal immune response was observed in elderly patients, characterized by 80% unique signatures, higher B- and T-cell activation, increased type I IFN signaling, and reduced NK, Th17, and complement system activation, compared to similar outbreaks. This unique immune response may explain the variant's epidemiological behavior and underscores the need for nasal vaccine strategies against emerging variants., (© 2024. The Author(s).)

Opioid misuse continues to cause significant harm. To investigate current research, we conducted a scoping literature review of disease spread models of opioid misuse from January 2000 to December 2022. In total, 85 studies were identified and examined for the opioids modeled, model type, data sources used and model calibration and validation. Most of the studies (58%, 49) only modeled heroin; the next largest categories were prescription opioids and unspecified opioids which accounted for 9% (8) each. Most models were theoretical compartmental models (57) or applied compartmental models (21). Previously published research was the most used data source (38), and a majority of the model validation involved the researchers setting initial conditions to verify theoretical results (30). To represent typical opioid use more accurately, multiple opioids need to be incorporated into the disease spread models, and applying different modeling techniques may allow other insights into opioid misuse spread.

Avoiding physical contact is regarded as one of the safest and most advisable strategies to follow to reduce pathogen spread. The flip side of this approach is that a lack of social interactions may negatively affect other dimensions of health, like induction of immunosuppressive anxiety and depression or preventing interactions of importance with a diversity of microbes, which may be necessary to train our immune system or to maintain its normal levels of activity. These may in turn negatively affect a population's susceptibility to infection and the incidence of severe disease. We suggest that future pandemic modelling may benefit from relying on 'SIR+ models': epidemiological models extended to account for the benefits of social interactions that affect immune resilience. We develop an SIR+ model and discuss which specific interventions may be more effective in balancing the trade-off between minimizing pathogen spread and maximizing other interaction-dependent health benefits. Our SIR+ model reflects the idea that health is not just the mere absence of disease, but rather a state of physical, mental and social well-being that can also be dependent on the same social connections that allow pathogen spread, and the modelling of public health interventions for future pandemics should account for this multidimensionality., (© 2024. The Author(s).)

Over the past quarter-century, the field of evolutionary biology has been transformed by the emergence of complete genome sequences and the conceptual framework known as the 'Net of Life.' This paradigm shift challenges traditional notions of evolution as a tree-like process, emphasizing the complex, interconnected network of gene flow that may blur the boundaries between distinct lineages. In this context, gene loss, rather than horizontal gene transfer, is the primary driver of gene content, with vertical inheritance playing a principal role. The 'Net of Life' not only impacts our understanding of genome evolution but also has profound implications for classification systems, the rapid appearance of new traits, and the spread of diseases. Here, we explore the core tenets of the 'Net of Life' and its implications for genome-scale phylogenetic divergence, providing a comprehensive framework for further investigations in evolutionary biology., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Within-host interactions among coinfecting parasites can have major consequences for individual infection risk and disease severity. However, the impact of these within-host interactions on between-host parasite transmission, and the spatial scales over which they occur, remain unknown. We developed and apply a novel spatially explicit analysis to parasite infection data from a wild wood mouse ( Apodemus sylvaticus ) population. We previously demonstrated a strong within-host negative interaction between two wood mouse gastrointestinal parasites, the nematode Heligmosomoides polygyrus and the coccidian Eimeria hungaryensis , using drug-treatment experiments. Here, we show this negative within-host interaction can significantly alter the between-host transmission dynamics of E. hungaryensis , but only within spatially restricted neighbourhoods around each host. However, for the closely related species E. apionodes , which experiments show does not interact strongly with H. polygyrus , we did not find any effect on transmission over any spatial scale. Our results demonstrate that the effects of within-host coinfection interactions can ripple out beyond each host to alter the transmission dynamics of the parasites, but only over local scales that likely reflect the spatial dimension of transmission. Hence there may be knock-on consequences of drug treatments impacting the transmission of non-target parasites, altering infection risks even for non-treated individuals in the wider neighbourhood.

African swine fever (ASF) is an infectious and highly fatal disease affecting wild and domestic swine, which is unstoppably spreading worldwide. In Europe, wild boars are one of the main drivers of spread, transmission, and maintenance of the disease. Landscape connectivity studies are the main discipline to analyze wild-species dispersal networks, and it can be an essential tool to predict dispersal-wild boar movement routes and probabilities and therefore the associated potential ASF spread through the suitable habitat. We aimed to integrate wild boar habitat connectivity predictions with their occurrence, population abundance, and ASF notifications to calculate the impact (i.e., the capacity of a landscape feature to favor ASF spread) and the risk (i.e., the likelihood of a habitat patch becoming infected) of wild boar infection across Europe. Furthermore, we tested the accuracy of the risk of infection by comparing the results with the temporal distribution of ASF cases. Our findings identified the areas with the highest impact and risk factors within Europe's central and Eastern regions where ASF is currently distributed. Additionally, the impact factor was 31 times higher on habitat patches that were infected vs non-infected, proving the utility of the proposed approach and the key role of wild boar movements in ASF-spread. All data and resulting maps are openly accessible and usable., (© 2024. The Author(s).)

Research on pandemics in institutional settings often assumes that all human interactions within a jail pose similar viral transmission risks. We developed an agent-based model (ABM) called Simulation Applications for Forecasting Effective Responses in Corrections (SAFER-C™) to simulate nine scenarios of possible interactions and virus transmission among incarcerated individuals and jail staff and tested this assumption. We found that resumption of high-contact activities has a greater impact on the number of infections, while out-of-cell group sizes and initial vaccination rates had lower impact. This work emphasizes the importance of understanding and modeling human interactions in confinement facilities, as well as understanding, responding to, and limiting the mechanism of viral transmission in jails. Insights from ABMs provide correctional administrators with realistic options for managing responses.

Middle East Respiratory Syndrome (MERS-CoV) is a coronavirus-caused viral respiratory infection initially detected in Saudi Arabia in 2012. In UAE, high seroprevalence (97.1) of MERS-CoV in camels was reported in several Emirate of Abu Dhabi studies, including camels in zoos, public escorts, and slaughterhouses. The objectives of this research include simulation of MERS-CoV spread using a customized animal disease spread model (i.e., customized stochastic model for the UAE; analyzing the MERS-CoV spread and prevalence based on camels age groups and identifying the optimum control MERS-CoV strategy. This study found that controlling animal mobility is the best management technique for minimizing epidemic length and the number of affected farms. This study also found that disease dissemination differs amongst camels of three ages: camel kids under the age of one, young camels aged one to four, and adult camels aged four and up; because of their immunological state, kids, as well as adults, had greater infection rates. To save immunization costs, it is advised that certain age groups be targeted and that intense ad hoc unexpected vaccinations be avoided. According to the study, choosing the best technique must consider both efficacy and cost.

Objective: This study sought to better understand the types of locations that serve as hubs for the transmission of COVID-19., Methods: Contact tracers interviewed individuals who tested positive for SARS-CoV-2 between November 2020 and March 2021, as well as the people with whom those individuals had contact. We conducted a 2-mode social network analysis of people by the types of places they visited, focusing on the forms of centrality exhibited by place types., Results: The most exposed locations were grocery stores, commercial stores, restaurants, commercial services, and schools. These types of locations also have the highest "betweenness," meaning that they tend to serve as hubs between other kinds of locations since people would usually visit more than 1 location in a day or when infected. The highest pairs of locations were grocery store/retail store, restaurant/retail store, and restaurant/grocery store. Schools are not at the top but are 3 times in the top 7 pairs of locations and connected to the 3 types of locations in those top pairs., Conclusions: As the pandemic progressed, location hotspots shifted between businesses, schools, and homes. In this social network analysis, certain types of locations appeared to be potential hubs of transmission.

Climate change is acknowledged to directly affect not only the environment, economy, and society but also the transmission dynamics of infectious diseases, thereby impacting public health. The recent experiences with the spread of SARS-CoV-2 and Monkeypox have highlighted the complex and interconnected nature of infectious diseases, which are strongly linked to various determinants of health. Considering these challenges, adopting a new vision such as the trans-disciplinary approach appears to be imperative. This paper proposes a new theory about viruses' spread, based on a biological model, accounting for the optimisation of energy and material resources for organisms' survival and reproduction in the environment. The approach applies Kleiber's law scaling theory, originally developed in biology, to model community dynamics in cities. A simple equation can be used to model pathogen spread without accounting for each species' physiology by leveraging the superlinear scaling of variables with population size. This general theory offers several advantages, including the ability to explain the rapid and surprising spread of both SARS-CoV-2 and Monkeypox. The proposed model shows similarities in the spreading processes of both viruses, based on the resulting scaling factors, and opens new avenues for research. By fostering cooperation and integrating knowledge from different disciplines to effectively tackle the multifaceted dimensions of disease outbreaks, we can work towards preventing future health emergencies., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Inc. All rights reserved.)

Objectives/hypothesis: Recent investigations into the behavior of aerosolized emissions from the oral cavity have shown that particulate emissions do indeed occur during speech. To date, there is little information about the relative contribution of different speech sounds in producing particle emissions in a free field. This study compares airborne aerosol generation in participants producing isolated speech sounds: fricative consonants, plosive consonants, and vowel sounds., Study Design: Prospective, reversal experimental design, where each participant served as their own control and all participants were exposed to all stimuli., Methods: While participants produced isolated speech tasks, a planar beam of laser light, a high-speed camera, and image software calculated the number of particulates detected over time. This study compared airborne aerosols emitted by human participants at a distance of 2.54 cm between the laser sheet and the mouth., Results: Statistically significant increases in particulate count over ambient dust distribution for all speech sounds. When collapsed across loudness levels, emitted particles in vowel sounds were statistically greater than consonants, suggesting that mouth opening, as opposed to the place of vocal tract constriction or manner of sound production, might also be influential in the degree to which particulates become aerosolized during speech., Conclusions: The results of this research will inform boundary conditions for computational models of aerosolized particulates during speech., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 The Voice Foundation. Published by Elsevier Inc. All rights reserved.)

Background: The world is undergoing an unprecedented wave of urbanization. However, the effect of rapid urbanization during the early or middle stages of urbanization on seasonal influenza transmission remains unknown. Since about 70% of the world population live in low-income countries, exploring the impact of urbanization on influenza transmission in urbanized countries is significant for global infection prediction and prevention., Objective: The aim of this study was to explore the effect of rapid urbanization on influenza transmission in China., Methods: We performed spatiotemporal analyses of province-level influenza surveillance data collected in Mainland China from April 1, 2010, to March 31, 2017. An agent-based model based on hourly human contact-related behaviors was built to simulate the influenza transmission dynamics and to explore the potential mechanism of the impact of urbanization on influenza transmission., Results: We observed persistent differences in the influenza epidemic attack rates among the provinces of Mainland China across the 7-year study period, and the attack rate in the winter waves exhibited a U-shaped relationship with the urbanization rates, with a turning point at 50%-60% urbanization across Mainland China. Rapid Chinese urbanization has led to increases in the urban population density and percentage of the workforce but decreases in household size and the percentage of student population. The net effect of increased influenza transmission in the community and workplaces but decreased transmission in households and schools yielded the observed U-shaped relationship., Conclusions: Our results highlight the complicated effects of urbanization on the seasonal influenza epidemic in China. As the current urbanization rate in China is approximately 59%, further urbanization with no relevant interventions suggests a worrisome increasing future trend in the influenza epidemic attack rate., (©Hao Lei, Nan Zhang, Beidi Niu, Xiao Wang, Shenglan Xiao, Xiangjun Du, Tao Chen, Lei Yang, Dayan Wang, Benjamin Cowling, Yuguo Li, Yuelong Shu. Originally published in JMIR Public Health and Surveillance (https://publichealth.jmir.org), 07.07.2023.)

Aim: To determine the effects of obesity in childhood on SARS-CoV-2 infection., Methods: A population-based, cross-sectional study combining the Israeli Growth Survey and COVID-19 data for children with at least one SARS-CoV-2 test from 16 February 2020 to 20 December 2021. Overweight and obesity status were based on body mass index and the Center for Disease Control criteria. Multivariate logistics regression was performed to validate reliability for weight categories at the age of approximately 6 years compared with weights at approximately 12 years., Results: A total of 444 868 records for children with an overall positivity rate of 22% were studied. The mean age was 9.5 years. The odds ratios of children with obesity or overweight after controlling for sex at 6 years to test positive were 1.07-1.12 and 1.06-1.08 (depending on the model), respectively, compared to those with healthy range body mass index., Conclusion: Excess weight appears to increase the risk of SARS-CoV-2 infection. This finding should be considered for public health planning. For example, children with overweight and obesity should be prioritised for vaccination. Excess weight in childhood can be harmful at a young age and not only for long-term health., (© 2023 Foundation Acta Paediatrica. Published by John Wiley & Sons Ltd.)

Background: Climate affects the thermal adaptation and distribution of hosts, and drives the spread of Chytridiomycosis-a keratin-associated infectious disease of amphibians caused by the sister pathogens Batrachochytrium dendrobatidi (Bd) and B. salamandrivorans (Bsal). We focus on their climate-pathogen relationships in Eurasia, the only region where their geographical distributions overlap. Eurasia harbours invaded and native areas of both pathogens and the natural habitats where they co-exist, making it an ideal region to examine their environmental niche correlations. Our understanding of how climate change will affect their distribution is broadened by the differences in climate correlates and niche characteristics between Bd and Bsal in Asia and Europe. This knowledge has potential conservation implications, informing future spread of the disease in different regions., Results: We quantified the environmental niche overlap between Bd and Bsal in Eurasia using niche analyses. Results revealed partial overlap in the niche with a unique 4% of non-overlapping values for Bsal, suggesting segregation along certain climate axes. Bd tolerates higher temperature fluctuations, while Bsal requires more stable, lower temperature and wetter conditions. Projections of their Realized Climatic Niches (RCNs) to future conditions show a larger expansion of suitable ranges (SRs) for Bd compared to Bsal in both Asia and Europe, with their centroids shifting in different directions. Notably, both pathogens' highly suitable areas in Asia are expected to shrink significantly, especially under the extreme climate scenarios. In Europe, they are expected to expand significantly., Conclusions: Climate change will impact or increase disease risk to amphibian hosts, particularly in Europe. Given the shared niche space of the two pathogens across available climate gradients, as has already been witnessed in Eurasia with an increased range expansion and niche overlap due to climate change, we expect that regions where Bsal is currently absent but salamanders are present, and where Bd is already prevalent, may be conducive for the spread of Bsal., (© 2023. The Author(s).)

Background: Norovirus is associated with approximately 18% of the global burden of gastroenteritis and affects all age groups. There is currently no licensed vaccine or available antiviral treatment. However, well-designed early warning systems and forecasting can guide nonpharmaceutical approaches to norovirus infection prevention and control., Objective: This study evaluates the predictive power of existing syndromic surveillance data and emerging data sources, such as internet searches and Wikipedia page views, to predict norovirus activity across a range of age groups across England., Methods: We used existing syndromic surveillance and emerging syndromic data to predict laboratory data indicating norovirus activity. Two methods are used to evaluate the predictive potential of syndromic variables. First, the Granger causality framework was used to assess whether individual variables precede changes in norovirus laboratory reports in a given region or an age group. Then, we used random forest modeling to estimate the importance of each variable in the context of others with two methods: (1) change in the mean square error and (2) node purity. Finally, these results were combined into a visualization indicating the most influential predictors for norovirus laboratory reports in a specific age group and region., Results: Our results suggest that syndromic surveillance data include valuable predictors for norovirus laboratory reports in England. However, Wikipedia page views are less likely to provide prediction improvements on top of Google Trends and Existing Syndromic Data. Predictors displayed varying relevance across age groups and regions. For example, the random forest modeling based on selected existing and emerging syndromic variables explained 60% variance in the ≥65 years age group, 42% in the East of England, but only 13% in the South West region. Emerging data sets highlighted relative search volumes, including "flu symptoms," "norovirus in pregnancy," and norovirus activity in specific years, such as "norovirus 2016." Symptoms of vomiting and gastroenteritis in multiple age groups were identified as important predictors within existing data sources., Conclusions: Existing and emerging data sources can help predict norovirus activity in England in some age groups and geographic regions, particularly, predictors concerning vomiting, gastroenteritis, and norovirus in the vulnerable populations and historical terms such as stomach flu. However, syndromic predictors were less relevant in some age groups and regions likely due to contrasting public health practices between regions and health information-seeking behavior between age groups. Additionally, predictors relevant to one norovirus season may not contribute to other seasons. Data biases, such as low spatial granularity in Google Trends and especially in Wikipedia data, also play a role in the results. Moreover, internet searches can provide insight into mental models, that is, an individual's conceptual understanding of norovirus infection and transmission, which could be used in public health communication strategies., (©Nikola Ondrikova, John P Harris, Amy Douglas, Helen E Hughes, Miren Iturriza-Gomara, Roberto Vivancos, Alex J Elliot, Nigel A Cunliffe, Helen E Clough. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 08.05.2023.)

Background: Rabies is a deadly zoonotic disease with nearly 100% fatality rate. In the United States, rabies virus persists in wildlife reservoirs, with occasional spillover into humans and domestic animals. The distribution of reservoir hosts in US counties plays an important role in public health decision-making, including the recommendation of lifesaving postexposure prophylaxis upon suspected rabies exposures. Furthermore, in surveillance data, it is difficult to discern whether counties have no cases reported because rabies was not present or because counties have an unreported rabies presence. These epizootics are monitored by the National Rabies Surveillance System (NRSS), to which approximately 130 state public health, agriculture, and academic laboratories report animal rabies testing statistics. Historically, the NRSS classifies US counties as free from terrestrial rabies if, over the previous 5 years, they and any adjacent counties did not report any rabies cases and they tested ≥15 reservoir animals or 30 domestic animals., Objective: This study aimed to describe and evaluate the historical NRSS rabies-free county definition, review possibilities for improving this definition, and develop a model to achieve more precise estimates of the probability of terrestrial rabies freedom and the number of reported county-level terrestrial rabies cases., Methods: Data submitted to the NRSS by state and territorial public health departments and the US Department of Agriculture Wildlife Services were analyzed to evaluate the historical rabies-free definition. A zero-inflated negative binomial model created county-level predictions of the probability of rabies freedom and the expected number of rabies cases reported. Data analyzed were from all animals submitted for laboratory diagnosis of rabies in the United States from 1995 to 2020 in skunk and raccoon reservoir territories, excluding bats and bat variants., Results: We analyzed data from 14,642 and 30,120 county-years in the raccoon and skunk reservoir territories, respectively. Only 0.85% (9/1065) raccoon county-years and 0.79% (27/3411) skunk county-years that met the historical rabies-free criteria reported a case in the following year (99.2% negative predictive value for each), of which 2 were attributed to unreported bat variants. County-level model predictions displayed excellent discrimination for detecting zero cases and good estimates of reported cases in the following year. Counties classified as rabies free rarely (36/4476, 0.8%) detected cases in the following year., Conclusions: This study concludes that the historical rabies freedom definition is a reasonable approach for identifying counties that are truly free from terrestrial raccoon and skunk rabies virus transmission. Gradations of risk can be measured using the rabies prediction model presented in this study. However, even counties with a high probability of rabies freedom should maintain rabies testing capacity, as there are numerous examples of translocations of rabies-infected animals that can cause major changes in the epidemiology of rabies., (©Amber Kunkel, Gabriella Veytsel, Sarah Bonaparte, Haillie Meek, Xiaoyue Ma, Amy J Davis, Jesse Bonwitt, Ryan M Wallace. Originally published in JMIR Public Health and Surveillance (https://publichealth.jmir.org), 07.04.2023.)

Lacking a federal policy to control the spread of COVID-19, state governors ordered lockdowns and mask mandates, at different times, generating a massive natural experiment. The authors exploit this natural experiment to address four issues: (1) Were lockdowns effective in reducing infections? (2) What were the costs to consumers? (3) Did lockdowns increase (signaling effect) or reduce (substitution effect) consumers' mask adoption? (4) Did governors' decisions depend on medical science or nonmedical drivers? Analyses via difference-in-differences and generalized synthetic control methods indicate that lockdowns causally reduced infections. Although lockdowns reduced infections by 480 per million consumers per day (equivalent to a reduction of 56%), they reduced customer satisfaction by 2.2%, consumer spending by 7.5%, and gross domestic product by 5.4% and significantly increased unemployment by 2% per average state by the end of the observation period. A counterfactual analysis shows that a nationwide lockdown on March 15, 2020, would have reduced total cases by 60%, whereas the absence of any state lockdowns would have resulted in five times more cases by April 30. The average cost of reducing the number of cases by one new infection was about $28,000 in lower gross domestic product., Competing Interests: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article., (© American Marketing Association 2023.)

Background: Early detection and response to influenza and COVID-19 outbreaks in aged care facilities (ACFs) are critical to minimizing health impacts. The Sydney Local Health District (SLHD) Public Health Unit (PHU) has developed and implemented a novel web-based app with integrated functions for online line listings, detection algorithms, and automatic notifications to responders, to assist ACFs in outbreak response. The goal of the Influenza Outbreak Communication, Advice and Reporting (FluCARE) app is to reduce time delays to notifications, which we hope will reduce the spread, duration, and health impacts of an influenza or COVID-19 outbreak, as well as ease workload burdens on ACF staff., Objective: The specific aims of the study were to (1) evaluate the acceptability and user satisfaction of the implementation and use of FluCARE in helping ACFs recognize, notify, and manage influenza and COVID-19 outbreaks in their facility; (2) identify the safety of FluCARE and any potential adverse outcomes of using the app; and (3) identify any perceived barriers or facilitators to the implementation and use of FluCARE from the ACF user perspective., Methods: The FluCARE app was piloted from September 2019 to December 2020 in the SLHD. Associated implementation included promotion and engagement, user training, and operational policies. Participating ACF staff were invited to complete a posttraining survey. Staff were also invited to complete a postpilot evaluation survey that included the user Mobile Application Rating Scale (uMARS) measuring app acceptance, utility, and barriers and facilitators to use. An issues log was also prospectively maintained to assess safety. Survey data were analyzed descriptively or via content analysis where appropriate., Results: Surveys were completed by 31 consenting users from 27 ACFs. FluCARE was rated 3.91 of 5 overall on the uMARS. Of the 31 users, 25 (80%) would definitely use FluCARE for future outbreaks, and all users agreed that the app was useful for identifying influenza and COVID-19 outbreaks at their facilities. There were no reported critical issues with incorrect or missed outbreak detection. User training, particularly online training modules, and technical support were identified as key facilitators to FluCARE use., Conclusions: FluCARE is an acceptable, useful, and safe app to assist ACF staff with early detection and response to influenza and COVID-19 outbreaks. This study supports feasibility for ongoing implementation and efficacy evaluation, followed by scale-up into other health districts in New South Wales., (©Emma Quinn, Kai Hsun Hsiao, Travers Johnstone, Maria Gomez, Arun Parasuraman, Andrew Ingleton, Nicholas Hirst, Zeina Najjar, Leena Gupta. Originally published in JMIR Formative Research (https://formative.jmir.org), 13.03.2023.)

Background: While many studies evaluated the reliability of digital mobility metrics as a proxy of SARS-CoV-2 transmission potential, none examined the relationship between dining-out behavior and the superspreading potential of COVID-19., Objective: We employed the mobility proxy of dining out in eateries to examine this association in Hong Kong with COVID-19 outbreaks highly characterized by superspreading events., Methods: We retrieved the illness onset date and contact-tracing history of all laboratory-confirmed cases of COVID-19 from February 16, 2020, to April 30, 2021. We estimated the time-varying reproduction number (R t ) and dispersion parameter (k), a measure of superspreading potential, and related them to the mobility proxy of dining out in eateries. We compared the relative contribution to the superspreading potential with other common proxies derived by Google LLC and Apple Inc., Results: A total of 6391 clusters involving 8375 cases were used in the estimation. A high correlation between dining-out mobility and superspreading potential was observed. Compared to other mobility proxies derived by Google and Apple, the mobility of dining-out behavior explained the highest variability of k (ΔR-sq=9.7%, 95% credible interval: 5.7% to 13.2%) and R t (ΔR-sq=15.7%, 95% credible interval: 13.6% to 17.7%)., Conclusions: We demonstrated that there was a strong link between dining-out behaviors and the superspreading potential of COVID-19. The methodological innovation suggests a further development using digital mobility proxies of dining-out patterns to generate early warnings of superspreading events., (©Ka Chun Chong, Kehang Li, Zihao Guo, Katherine Min Jia, Eman Yee Man Leung, Shi Zhao, Chi Tim Hung, Carrie Ho Kwan Yam, Tsz Yu Chow, Dong Dong, Huwen Wang, Yuchen Wei, Eng Kiong Yeoh. Originally published in JMIR Public Health and Surveillance (https://publichealth.jmir.org), 07.03.2023.)

Here we investigate the effects of extensive sociality and mobility on the oral microbiome of 138 Agta hunter-gatherers from the Philippines. Our comparisons of microbiome composition showed that the Agta are more similar to Central African BaYaka hunter-gatherers than to neighbouring farmers. We also defined the Agta social microbiome as a set of 137 oral bacteria (only 7% of 1980 amplicon sequence variants) significantly influenced by social contact (quantified through wireless sensors of short-range interactions). We show that large interaction networks including strong links between close kin, spouses and even unrelated friends can significantly predict bacterial transmission networks across Agta camps. Finally, we show that more central individuals to social networks are also bacterial supersharers. We conclude that hunter-gatherer social microbiomes are predominantly pathogenic and were shaped by evolutionary tradeoffs between extensive sociality and disease spread., Competing Interests: The authors declare none., (© The Author(s) 2023.)

Background: Undiagnosed tuberculosis (TB) cases are the major challenge to TB control in Nigeria. An early warning outbreak recognition system (EWORS) is a system that is primarily used to detect infectious disease outbreaks; this system can be used as a case-based geospatial tool for the real-time identification of hot spot areas with clusters of TB patients. TB screening targeted at such hot spots should yield more TB cases than screening targeted at non-hot spots., Objective: We aimed to demonstrate the effectiveness of an EWORS for TB hot spot mapping as a tool for detecting areas with increased TB case yields in high TB-burden states of Nigeria., Methods: KNCV Tuberculosis Foundation Nigeria deployed an EWORS to 14 high-burden states in Nigeria. The system used an advanced surveillance mechanism to identify TB patients' residences in clusters, enabling it to predict areas with elevated disease spread (ie, hot spots) at the ward level. TB screening outreach using the World Health Organization 4-symptom screening method was conducted in 121 hot spot wards and 213 non-hot spot wards selected from the same communities. Presumptive cases identified were evaluated for TB using the GeneXpert instrument or chest X-ray. Confirmed TB cases from both areas were linked to treatment. Data from the hot spot and non-hot spot wards were analyzed retrospectively for this study., Results: During the 16-month intervention, a total of 1,962,042 persons (n=734,384, 37.4% male, n=1,227,658, 62.6% female) and 2,025,286 persons (n=701,103, 34.6% male, n=1,324,183, 65.4% female) participated in the community TB screening outreaches in the hot spot and non-hot spot areas, respectively. Presumptive cases among all patients screened were 268,264 (N=3,987,328, 6.7%) and confirmed TB cases were 22,618 (N=222,270, 10.1%). The number needed to screen to diagnose a TB case in the hot spot and non-hot spot areas was 146 and 193 per 10,000 people, respectively., Conclusions: Active TB case finding in EWORS-mapped hot spot areas yielded higher TB cases than the non-hot spot areas in the 14 high-burden states of Nigeria. With the application of EWORS, the precision of diagnosing TB among presumptive cases increased from 0.077 to 0.103, and the number of presumptive cases needed to diagnose a TB case decreased from 14.047 to 10.255 per 10,000 people., (©Chidubem Ogbudebe, Dohyo Jeong, Bethrand Odume, Ogoamaka Chukwuogo, Cyril Dim, Sani Useni, Okey Okuzu, Chenchita Malolan, Dohyeong Kim, Fiemu Nwariaku, Nkiru Nwokoye, Stephanie Gande, Debby Nongo, Rupert Eneogu, Temitayo Odusote, Salewa Oyelaran, Obioma Chijioke-Akaniro, Nrip Nihalani, Chukwuma Anyaike, Mustapha Gidado. Originally published in JMIR Public Health and Surveillance (https://publichealth.jmir.org), 08.02.2023.)

The new Acute Respiratory Syndrome, COVID-19, has affected the health and the economy worldwide. Therefore, scientists have been looking for ways to understand this disease. In this context, the main objective of this study was the spatialization of COVID-19, thinking in distinguishing areas with high transmissibility yet, verifying if these areas were associated with the elderly population occurrence. The work was delineated, supposing that spatialization could support the decision-making to combat the outbreak and that the same method could be used for spatialization and prevent other diseases. The study area was a municipality near Sao Paulo Metropolis, one of Brazil's main disease epicenters. Using official data and an empirical Bayesian model, we spatialized people infected by region, including older people, obtaining reasonable adjustment. The results showed a weak correlation between regions infected and older adults. Thus, we define a robust model that can support the definition of actions aiming to control the COVID-19 spread., Competing Interests: Conflicts of interestThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. The authors declare the following financial interests/personal relationships which may be considered as potential competing interests., (© The Author(s), under exclusive licence to Springer Nature B.V. 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.)

White-nose syndrome has been decimating populations of several bat species since its first occurrence in the Northeastern United States in the winter 2006-2007. The spread of the disease has been monitored across the continent through the collaboration of many organizations. Inferring the rate of spread of the disease and predicting its arrival at new locations is critical when assessing the current and predicting the future status and trends of bat species. We developed a model of disease spread that simultaneously achieves high-predictive performance, computational efficiency, and interpretability. We modeled white-nose syndrome spread using Gaussian process variations to infer the spread rate of the disease front, identify areas of anomalous time of arrival, and provide future forecasts of the expected time of arrival throughout North America. Cross-validation of model predictive performance identified a stationary Gaussian process without an additional residual error process as the best-supported model. Results indicated that white-nose syndrome is likely to spread throughout the entire continental United States by 2030. These annually updatable model predictions will be useful in determining the horizon over which disease management actions must take place as well as in status and trend assessments of disease-affected bats., (© 2022 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd.)

Mandatory surveillance testing programs are popular policies aimed to control SARS-CoV-2 and may be considered for future epidemics. However, if people believe that testing lowers their risk of infection, such policies could increase risky behavior and may even cause increased pathogen spread. Using data from two US universities, we find that frequent mandatory testing is associated with greater participation in events linked to COVID-19 spread. Women seem to be driving this association, and mediation analyses suggest this is partly due to women's higher perception of COVID-related health risks. Our results show the potential for adverse effects from epidemic control policies, both on average and across population subgroups. Undertaking mitigation measures to reduce such unintended consequences may therefore be important., (© The Author(s) 2022. Published by Oxford University Press on behalf of National Academy of Sciences.)

Most animal disease surveillance systems concentrate efforts in blocking transmission pathways and tracing back infected contacts while not considering the risk of transporting animals into areas with elevated disease risk. Here, we use a suite of spatial statistics and social network analysis to characterize animal movement among areas with an estimated distinct risk of disease circulation to ultimately enhance surveillance activities. Our model utilized equine infectious anemia virus (EIAV) outbreaks, between-farm horse movements, and spatial landscape data from 2015 through 2017. We related EIAV occurrence and the movement of horses between farms with climate variables that foster conditions for local disease propagation. We then constructed a spatially explicit model that allows the effect of the climate variables on EIAV occurrence to vary through space (i.e., non-stationary). Our results identified important areas in which in-going movements were more likely to result in EIAV infections and disease propagation. Municipalities were then classified as having high 56 (11.3%), medium 48 (9.66%), and low 393 (79.1%) spatial risk. The majority of the movements were between low-risk areas, altogether representing 68.68% of all animal movements. Meanwhile, 9.48% were within high-risk areas, and 6.20% were within medium-risk areas. Only 5.37% of the animals entering low-risk areas came from high-risk areas. On the other hand, 4.91% of the animals in the high-risk areas came from low- and medium-risk areas. Our results demonstrate that animal movements and spatial risk mapping could be used to make informed decisions before issuing animal movement permits, thus potentially reducing the chances of reintroducing infection into areas of low risk., (© 2022 The Authors. Transboundary and Emerging Diseases published by Wiley-VCH GmbH.)

Pesticides are a major anthropogenic threat to the biodiversity of freshwater ecosystems, having the potential to affect non-target aquatic organisms and disrupt the processes in which they intervene. Important knowledge gaps have been recognised concerning the ecological effects of synthetic fungicides on non-target symbiotic aquatic fungi and the ecological processes where they intervene. The goal of this work was to assess the influence of three commonly used fungicides (myclobutanil, metalaxyl and cymoxanil), which differ in their mode of action, on a host (the crustacean Daphnia magna) × parasite (the yeast Metschnikowia bicuspidata) experimental model. Using a set of life history experiments, we evaluated the effect of each fungicide on the outcome of this relationship (disease) and on the fitness of both host and parasite. Contrasting results were observed: (i) cymoxanil and metalaxyl were overall innocuous to host and parasite at the tested concentrations, although host reproduction was occasionally reduced in the simultaneous presence of parasite and fungicide; (ii) on the contrary, myclobutanil displayed a clear antifungal effect, decreasing parasite prevalence and alleviating infection signs in the hosts. This antiparasitic effect of myclobutanil was further investigated with a follow-up experiment that manipulated the timing of application of the fungicide, to understand which stage of parasite development was most susceptible: while myclobutanil did not interfere in the early stages of infection, its antifungal activity was clearly observable at a later stage of the disease (by impairing the production of transmission stages of the parasite). More research is needed to understand the broader consequences of this parasite-clearance effect, especially in face of increasing evidence that parasites are ecologically more important than their cryptic nature might suggest., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 The Authors. Published by Elsevier B.V. All rights reserved.)

Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Background: Serial testing for SARS-CoV-2 is recommended to reduce spread of the virus; however, little is known about adherence to recommended testing schedules and reporting practices to health departments., Objective: The Self-Testing for Our Protection from COVID-19 (STOP COVID-19) study aims to examine adherence to a risk-based COVID-19 testing strategy using rapid antigen tests and reporting of test results to health departments., Methods: STOP COVID-19 is a 12-week digital study, facilitated using a smartphone app for testing assistance and reporting. We are recruiting 20,000 participants throughout the United States. Participants are stratified into high- and low-risk groups based on history of COVID-19 infection and vaccination status. High-risk participants are instructed to perform twice-weekly testing for COVID-19 using rapid antigen tests, while low-risk participants test only in the case of symptoms or exposure to COVID-19. All participants complete COVID-19 surveillance surveys, and rapid antigen results are recorded within the smartphone app. Primary outcomes include participant adherence to a risk-based serial testing protocol and percentage of rapid tests reported to health departments., Results: As of February 2022, 3496 participants have enrolled, including 1083 high-risk participants. Out of 13,730 tests completed, participants have reported 13,480 (98.18%, 95% CI 97.9%-98.4%) results to state public health departments with full personal identifying information or anonymously. Among 622 high-risk participants who finished the study period, 35.9% showed high adherence to the study testing protocol. Participants with high adherence reported a higher percentage of test results to the state health department with full identifying information than those in the moderate- or low-adherence groups (high: 71.7%, 95% CI 70.3%-73.1%; moderate: 68.3%, 95% CI 66.0%-70.5%; low: 63.1%, 59.5%-66.6%)., Conclusions: Preliminary results from the STOP COVID-19 study provide important insights into rapid antigen test reporting and usage, and can thus inform the use of rapid testing interventions for COVID-19 surveillance., (©Carly Herbert, Vik Kheterpal, Thejas Suvarna, John Broach, Juan Luis Marquez, Ben Gerber, Summer Schrader, Christopher Nowak, Emma Harman, William Heetderks, Nisha Fahey, Elizabeth Orvek, Peter Lazar, Julia Ferranto, Kamran Noorishirazi, Shivakumar Valpady, Qiming Shi, Honghuang Lin, Kathryn Marvel, Laura Gibson, Bruce Barton, Stephenie Lemon, Nathaniel Hafer, David McManus, Apurv Soni. Originally published in JMIR Formative Research (https://formative.jmir.org), 16.06.2022.)

Multiple introductions of SARS-COV-2 Omicron variant BA.1 and BA.1.1. lineages to Finland were detected in early December 2021. Within 3 weeks, Omicron overtook Delta as the most common variant in the capital region. Sequence analysis demonstrated the emergence and spread through community transmission of a large cluster of BA.1.1 virus.

Many aspects of the porcine reproductive and respiratory syndrome virus (PRRSV) between-farm transmission dynamics have been investigated, but uncertainty remains about the significance of farm type and different transmission routes on PRRSV spread. We developed a stochastic epidemiological model calibrated on weekly PRRSV outbreaks accounting for the population dynamics in different pig production phases, breeding herds, gilt development units, nurseries and finisher farms, of three hog producer companies. Our model accounted for indirect contacts by the close distance between farms (local transmission), between-farm animal movements (pig flow) and reinfection of sow farms (re-break). The fitted model was used to examine the effectiveness of vaccination strategies and complementary interventions such as enhanced PRRSV detection and vaccination delays and forecast the spatial distribution of PRRSV outbreak. The results of our analysis indicated that for sow farms, 59% of the simulated infections were related to local transmission (e.g. airborne, feed deliveries, shared equipment) whereas 36% and 5% were related to animal movements and re-break, respectively. For nursery farms, 80% of infections were related to animal movements and 20% to local transmission; while at finisher farms, it was split between local transmission and animal movements. Assuming that the current vaccines are 1% effective in mitigating between-farm PRRSV transmission, weaned pigs vaccination would reduce the incidence of PRRSV outbreaks by 3%, indeed under any scenario vaccination alone was insufficient for completely controlling PRRSV spread. Our results also showed that intensifying PRRSV detection and/or vaccination pigs at placement increased the effectiveness of all simulated vaccination strategies. Our model reproduced the incidence and PRRSV spatial distribution; therefore, this model could also be used to map current and future farms at-risk. Finally, this model could be a useful tool for veterinarians, allowing them to identify the effect of transmission routes and different vaccination interventions to control PRRSV spread., (© 2021 Wiley-VCH GmbH.)

We present a new mathematical model of disease spread reflecting some specialties of the COVID-19 epidemic by elevating the role of hierarchic social clustering of population. The model can be used to explain slower approaching herd immunity, e.g., in Sweden, than it was predicted by a variety of other mathematical models and was expected by epidemiologists; see graphs Fig. 1, 2. The hierarchic structure of social clusters is mathematically modeled with ultrametric spaces having treelike geometry. To simplify mathematics, we consider trees with the constant number p > 1 of branches leaving each vertex. Such trees are endowed with an algebraic structure, these are p -adic number fields. We apply theory of the p -adic diffusion equation to describe a virus spread in hierarchically clustered population. This equation has applications to statistical physics and microbiology for modeling dynamics on energy landscapes. To move from one social cluster (valley) to another, a virus (its carrier) should cross a social barrier between them. The magnitude of a barrier depends on the number of social hierarchy's levels composing this barrier. We consider linearly increasing barriers. A virus spreads rather easily inside a social cluster (say working collective), but jumps to other clusters are constrained by social barriers. This behavior matches with the COVID-19 epidemic, with its cluster spreading structure. Our model differs crucially from the standard mathematical models of spread of disease, such as the SIR-model; in particular, by notion of the probability to be infected (at time t in a social cluster C ). We present socio-medical specialties of the COVID-19 epidemic supporting our model., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. The author would like to thank Philippe Grangier who stimulated this research by his own attempt to model COVID-19 spread (unpublished) and provided interesting information about disease clustering in France as well as for discussion on COVID-19 “hidden variables”, Sergey Kozyrev for discussions on ultrametric dynamics, Anja Nertyk information about epidemic in Sweden, and Arkady Plotnisky and Karl Svozil for providing information from American and Austrian sources. The paper was published with the financial support of the Ministry of Education and Science of the Russian Federation as part of the program of the Mathematical Center for Fundamental and Applied Mathematics under the agreement N 075-15-2019-1621 ., (© 2021 The Author.)

African swine fever (ASF) was diagnosed for the first time in Romania in a backyard holding in Satu-Mare County in July 2017. Since then, more than 3800 outbreaks occurred in the entire country. Disease control strategies in the backyard sector rely almost exclusively on reactive measures implemented upon appearance of clinical signs and laboratory confirmation of ASF. In our descriptive study, infection course and outbreak investigation data of 56 affected backyard holdings in Satu-Mare County has been investigated. Early disease detection based on clinical signs appeared to be efficient. In the majority of outbreaks, ASF was detected within the first 2 weeks after the estimated virus introduction. A clinical phase of 2-8 days was observed before pigs either succumbed to the disease or control measures were implemented on affected farms. A moderate on-farm transmissibility of ASF virus between pigs was observed. Four clusters of outbreaks were identified indicating virus perpetuation and transmission from farm to farm. To suspend infection chains, rapid intervention by isolating affected farms combined with effective biosecurity measures is required. However, due to the backyard peculiarities, quick and effective implementation of control measures has shown to be rather difficult., (© 2021 The Authors. Veterinary Medicine and Science published by John Wiley & Sons Ltd.)

Recently, the sudden outbreak of the COVID-19 virus caused a major health crisis by affecting masses around the world. The virus, which is known to be highly contagious, has forced the research community and governments to fight the disease and take prompt actions by applying various strategies to keep the numbers under control. These strategies range from imposing strict social distancing measures, isolating infected cases, and enforcing either a partial or a full lockdown, to mathematical modeling and contact-tracing applications. In this work, we survey the current contact-tracing apps and organize them based on underlying technologies such as Bluetooth, Wi-Fi, GPS, geofencing, and Quick Response (QR) codes. We compare the main features of 22 existing applications and highlight each of the pros and cons associated with these different technologies., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

Since 2007, African swine fever virus (ASFV) has spread to countries in Europe, Asia and Oceania and has caused devastating impacts on pigs and the pork industry. Transmission can be direct or indirect, and epidemiologic scenarios have been described in which spread occurs between free-living and domestic pigs. The purpose of this scoping review was to identify primary research in which authors made statements to support ASFV transmission between free-living and domestic pigs and assess the circumstances in which transmission events occurred. A search was conducted in five bibliographic databases and the grey literature. Two reviewers (from a team of ten) independently screened each record and charted data (demographics of the pig populations, their husbandry [domestic pigs] and habitat [free-living pigs], the spatial and temporal distribution of ASF, the occurrence or burden of ASF in the populations, and whether ticks were present in the geographic range of the pig populations). Data synthesis included statistics and a narrative summary. From 1,349 records screened, data were charted from 46 individual studies published from 1985 to 2020. Outbreak investigations revealed that whilst poor biosecurity of domestic pig operations was often reported, direct contact resulting in transmission between free-living and domestic pigs was rarely reported. Studies in which quantitative associations were made generally found that spread within populations was more important than spread between populations, although this was not always the case, particularly when domestic pigs were free-ranging. We conclude that there is limited evidence that transmission of ASFV between free-living and domestic pigs is an important feature of ASF epidemiology, especially in the current ASF epidemic in Europe and the Russian Federation. If ASFV elimination cannot be achieved in free-living pigs, compartmentalization of domestic pig populations from free-living populations via biosecurity strategies could be used to support trade of domestic pigs., (© 2021 Wiley-VCH GmbH.)

Wheat blast (WB) caused by Magnaporthe oryzae pathotype Triticum (MoT) is an important fungal disease in tropical and subtropical wheat production regions. The disease was initially identified in Brazil in 1985, and it subsequently spread to some major wheat-producing areas of the country as well as several South American countries such as Bolivia, Paraguay, and Argentina. In recent years, WB has been introduced to Bangladesh and Zambia via international wheat trade, threatening wheat production in South Asia and Southern Africa with the possible further spreading in these two continents. Resistance source is mostly limited to 2NS carriers, which are being eroded by newly emerged MoT isolates, demonstrating an urgent need for identification and utilization of non-2NS resistance sources. Fungicides are also being heavily relied on to manage WB that resulted in increasing fungal resistance, which should be addressed by utilization of new fungicides or rotating different fungicides. Additionally, quarantine measures, cultural practices, non-fungicidal chemical treatment, disease forecasting, biocontrol etc., are also effective components of integrated WB management, which could be used in combination with varietal resistance and fungicides to obtain reasonable management of this disease., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Singh, Gahtyari, Roy, Roy, He, Tembo, Xu, Juliana, Sonder, Kabir and Chawade.)

Host genetic diversity tends to limit disease spread in nature and buffers populations against epidemics. Genetic diversity in wildlife is expected to receive increasing attention in contexts related to disease transmission and human health. Ungulates such as wild boar ( Sus scrofa ) and red deer ( Cervus elaphus ) are important zoonotic hosts that can be precursors to disease emergence and spread in humans. Tuberculosis is a zoonotic disease with relevant consequences and can present high prevalence in wild boar and red deer populations. Here, we review studies on the genetic diversity of ungulates and determine to what extent these studies consider its importance on the spread of disease. This assessment also focused on wild boar, red deer, and tuberculosis. We found a disconnection between studies treating genetic diversity and those dealing with infectious diseases. Contrarily, genetic diversity studies in ungulates are mainly concerned with conservation. Despite the existing disconnection between studies on genetic diversity and studies on disease emergence and spread, the knowledge gathered in each discipline can be applied to the other. The bidirectional applications are illustrated in wild boar and red deer populations from Spain, where TB is an important threat for wildlife, livestock, and humans.

This paper studies the interplay between social distancing and the spread of the COVID-19 disease-a global pandemic that has affected most of the world's population. Our goals are to (1) to observe the correlation between the strictness of social distancing policies and the spread of disease and (2) to determine the optimal adoption level of social distancing policies. The earliest instances of the virus were found in China, and the virus has reached the United States with devastating consequences. Other countries severely affected by the pandemic are Brazil, Russia, the United Kingdom, Spain, India, Italy, and France. Although it is impossible to stop it, it is possible to slow down its spread to reduce its impact on the society and economy. Governments around the world have deployed various policies to reduce the virus spread in response to the pandemic. To assess the effectiveness of these policies, the system's dynamics of the society needs to be analyzed, which is generally not possible with mathematical linear equations or Monte Carlo methods because human society is a complex adaptive system with continuous feedback loops. Because of the challenges with the other methods, we chose agent-based methods to conduct our study. Moreover, recent agent-based modeling studies for the COVID-19 pandemic show significant promise in assisting decision-makers in managing the crisis by applying policies such as social distancing, disease testing, contact tracing, home isolation, emergency hospitalization, and travel prevention to reduce infection rates. Based on modeling studies conducted in Imperial College, increasing levels of interventions could slow the spread of disease and infection. We ran the model with six different percentages of social distancing while keeping the other parameters constant. The results show that social distancing affects the spread of COVID-19 significantly, in turn decreasing the spread of disease and infection rates when implemented at higher levels. We also validated these results by using the behavior space tool with ten experiments with varying social distancing levels. We conclude that applying and increasing social distancing policy levels leads to a significant reduction in infection spread and the number of deaths. Both experiments show that infection rates are reduced drastically when social distancing intervention is implemented between 80% to 100%.

Grapevine vein clearing virus (GVCV) causes severe stunting and death of cultivated grapevines and is prevalent in native Vitis spp. and Ampelopsis cordata in the Midwest region of the United States. GVCV can be transmitted from wild A. cordata to Vitis spp. by grape aphid ( Aphis illinoisensis ) under greenhouse conditions, but its prevalence, genetic composition, and genome number in native grape aphids are unknown. In this study, we collected grape aphids from native Vitaceae across the state of Missouri in 2018 and 2019, and conducted diagnostic, genetic, and quantitative analyses. GVCV was detected in 91 of the 105 randomly sampled communities on 71 Vitaceae plants (87%). It was present in 211 of 525 single grape aphids (40%). Diverse GVCV variants from aphids were present on both GVCV-negative and GVCV-positive plants. Identical GVCV variants were found in grape aphids sampled from wild and cultivated Vitaceae, indicating that viruliferous aphids likely migrate and disperse GVCV variants among wild and cultivated Vitaceae. In addition, we found that the number of GVCV genomes varies largely in the stylet and body of individual aphids. Our study provides a snapshot of GVCV epidemics and genetic structure in its mobile vector and sessile hosts. This presents a good model for studying the epidemiology, ecology, and evolution of a plant virus.

We estimate the effects of shelter-in-place (SIP) orders during the first wave of the COVID-19 pandemic. We do not find detectable effects of these policies on disease spread or deaths. We find small but measurable effects on mobility that dissipate over time. And we find small, delayed effects on unemployment. We conduct additional analyses that separately assess the effects of expanding versus withdrawing SIP orders and test whether there are spillover effects in other states. Our results are consistent with prior studies showing that SIP orders have accounted for a relatively small share of the mobility trends and economic disruptions associated with the pandemic. We reanalyze two prior studies purporting to show that SIP orders caused large reductions in disease prevalence, and show that those results are not reliable. Our results do not imply that social distancing behavior by individuals, as distinct from SIP policy, is ineffective., Competing Interests: The authors declare no competing interest.

Since March 2020, many countries around the world have been experiencing a large outbreak of a novel coronavirus (2019-nCoV). Because there is a higher rate of contact between humans in cities with higher population weighted densities, Covid-19 spreads faster in these areas. In this study, we examined the relationship between population weighted density and the spread of Covid-19. Using data from Turkey, we calculated the elasticity of Covid-19 spread with respect to population weighted density to be 0.67 after controlling for other factors. In addition to the density, the proportion of people over 65, the per capita GDP, and the number of total health care workers in each city positively contributed to the case numbers, while education level and temperature had a negative effect. We suggested a policy measure on how to transfer health care workers from different areas to the areas with a possibility of wide spread., Competing Interests: Declaration of Competing Interest The authors report no declarations of interest., (Copyright © 2020. Published by Elsevier B.V.)

Studying the spread of infections is an important tool in limiting or preventing future outbreaks. A first step in understanding disease dynamics is constructing networks that reproduce features of real-world interactions. In this paper, we generate networks that maintain some features of the partial interaction networks that were recorded in an existing diary-based survey at the University of Warwick. To preserve realistic structure in our artificial networks, we use a context-specific approach. In particular, we propose different algorithms for producing larger home, work and social networks. Our networks are able to maintain much of the interaction structure in the original diary-based survey and provide a means of accounting for the interactions of survey participants with non-participants. Simulating a discrete susceptible-infected-recovered model on the full network produces epidemic behaviour which shares characteristics with previous influenza seasons. Our approach allows us to explore how disease transmission and dynamic responses to infection differ depending on interaction context. We find that, while social interactions may be the first to be reduced after influenza infection, limiting work and school encounters may be significantly more effective in controlling the overall severity of the epidemic., Competing Interests: We declare we have no competing interests., (© 2021 The Authors.)

An outbreak of foot-and mouth disease (FMD) in an FMD-free country such as Austria would likely have serious consequences for the national livestock sector and economy. The objective of this study was to analyse the epidemiological and economic impact of an FMD outbreak in Austria in order to (i) evaluate the effectiveness of different control measures in two Austrian regions with different livestock structure and density, (ii) analyse the associated costs of the control measures and the losses resulting from trade restrictions on livestock and livestock products and (iii) assess the resources that would be required to control the FMD outbreak. The European Foot-and-Mouth Disease Spread Model (EuFMDiS) was used to simulate a potential FMD outbreak. Based on the epidemiological outputs of the model, the economic impact of the outbreak was assessed. The analysis of the simulations showed that the success of control strategies depends largely on the type of control measures, the geographical location, the availability of sufficient resources, and the speed of intervention. The comparison of different control strategies suggested that from an economic point of view the implementation of additional control measures, such as pre-emptive depopulation of susceptible herds, would be efficient if the epidemic started in an area with high livestock density. Depending on the chosen control measures and the affected region, the majority of the total costs would be attributable to export losses (e.g., each day of an FMD epidemic costs Austria € 9-16 million). Our analysis indicated that the currently estimated resources for surveillance, cleaning, and disinfection during an FMD outbreak in Austria would be insufficient, which would lead to an extended epidemic control duration. We have shown that the control of an FMD outbreak can be improved by implementing a contingency strategy adapted to the affected region and by placing particular focus on an optimal resource allocation and rapid detection of the disease in Austria. The model results can assist veterinary authorities in planning resources and implementing cost-effective control measures for future outbreaks of highly contagious viral diseases., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Marschik, Kopacka, Stockreiter, Schmoll, Hiesel, Höflechner-Pöltl, Käsbohrer and Pinior.)

Contact networks provide insights on disease spread due to the duration of close proximity interactions. For systems governed by consensus dynamics, network structure is key to optimising the spread of information. For disease spread over contact networks, the structure would be expected to be similarly influential. However, metrics that are essentially agnostic to the network's structure, such as weighted degree (strength) centrality and its variants, perform near-optimally in selecting effective spreaders. These degree-based metrics outperform eigenvector centrality, despite disease spread over a network being a random walk process. This paper improves eigenvector-based spreader selection by introducing the non-linear relationship between contact time and the probability of disease transmission into the assessment of network dynamics. This approximation of disease spread dynamics is achieved by altering the Laplacian matrix, which in turn highlights why nodes with a high degree are such influential disease spreaders. From this approach, a trichotomy emerges on the definition of an effective spreader where, for susceptible-infected simulations, eigenvector-based selections can either optimise the initial rate of infection, the average rate of infection, or produce the fastest time to full infection of the network. Simulated and real-world human contact networks are examined, with insights also drawn on the effective adaptation of ant colony contact networks to reduce pathogen spread and protect the queen ant., Competing Interests: Competing interestsThe authors declare that they have no competing interests., (© The Author(s) 2021.)

Recent controversies about wearing masks and getting vaccinated to slow the spread of coronavirus disease 2019 highlight the potential for individual rights and decision making to create widespread community-level outcomes. There is little work demonstrating the collective spillover effects of pandemic mitigation efforts. The authors contribute by visualizing the proportion of unvaccinated people who would become infected at different combinations of mask wearing and vaccination in a hypothetical community. A common pattern emerges across all assumptions: below some joint threshold of mask and vaccination rates, almost all unvaccinated people will eventually become infected, and beyond that threshold there is a steep drop leading to widespread community-level protection. What differs across settings is the timing and shape of the drop-off after crossing the threshold. The authors conclude that masking and vaccination are sensible and in the best interest of the population.

Controlling the regional re-emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) after its initial spread in ever-changing personal contact networks and disease landscapes is a challenging task. In a landscape context, contact opportunities within and between populations are changing rapidly as lockdown measures are relaxed and a number of social activities re-activated. Using an individual-based metapopulation model, we explored the efficacy of different control strategies across an urban-rural gradient in Wales, UK. Our model shows that isolation of symptomatic cases or regional lockdowns in response to local outbreaks have limited efficacy unless the overall transmission rate is kept persistently low. Additional isolation of non-symptomatic infected individuals, who may be detected by effective test-and-trace strategies, is pivotal to reducing the overall epidemic size over a wider range of transmission scenarios. We define an 'urban-rural gradient in epidemic size' as a correlation between regional epidemic size and connectivity within the region, with more highly connected urban populations experiencing relatively larger outbreaks. For interventions focused on regional lockdowns, the strength of such gradients in epidemic size increased with higher travel frequencies, indicating a reduced efficacy of the control measure in the urban regions under these conditions. When both non-symptomatic and symptomatic individuals are isolated or regional lockdown strategies are enforced, we further found the strongest urban-rural epidemic gradients at high transmission rates. This effect was reversed for strategies targeted at symptomatic individuals only. Our results emphasize the importance of test-and-trace strategies and maintaining low transmission rates for efficiently controlling SARS-CoV-2 spread, both at landscape scale and in urban areas.

Interactions among co-infecting pathogens are common across host taxa and can affect infectious disease dynamics. Host nutrition can mediate these among-pathogen interactions, altering the establishment and growth of pathogens within hosts. It is unclear, however, how nutrition-mediated among-pathogen interactions affect transmission and the spread of disease through populations. We manipulated the nitrogen (N) and phosphorus (P) supplies to oat plants in growth chambers and evaluated interactions between two aphid-vectored Barley and Cereal Yellow Dwarf Viruses: PAV and RPV. We quantified the effect of each virus on the other's establishment, within-plant density, and transmission. Co-inoculation significantly increased PAV density when N and P supplies were low and tended to increase RPV density when N supply was high. Co-infection increased PAV transmission when N and P supplies were low and tended to increase RPV transmission when N supply was high. Despite the parallels between the effects of among-pathogen interactions on density and transmission, changes in virus density only partially explained changes in transmission, suggesting that virus density-independent processes contribute to transmission. A mathematical model describing the spread of two viruses through a plant population, parameterized with empirically derived transmission values, demonstrated that nutrition-mediated among-pathogen interactions could affect disease spread. Interactions that altered transmission through virus density-independent processes determined overall disease dynamics. Our work suggests that host nutrition alters disease spread through among-pathogen interactions that modify transmission., (© 2020 by the Ecological Society of America.)

We present a mathematical model of disease (say a virus) spread that takes into account the hierarchic structure of social clusters in a population. It describes the dependence of epidemic's dynamics on the strength of barriers between clusters. These barriers are established by authorities as preventative measures; partially they are based on existing socio-economic conditions. We applied the theory of random walk on the energy landscapes represented by ultrametric spaces (having tree-like geometry). This is a part of statistical physics with applications to spin glasses and protein dynamics. To move from one social cluster (valley) to another, a virus (its carrier) should cross a social barrier between them. The magnitude of a barrier depends on the number of social hierarchy levels composing this barrier. Infection spreads rather easily inside a social cluster (say a working collective), but jumps to other clusters are constrained by social barriers. The model implies the power law, 1-t-a, for approaching herd immunity, where the parameter a is proportional to inverse of one-step barrier Δ. We consider linearly increasing barriers (with respect to hierarchy), i.e., the m -step barrier Δm=mΔ. We also introduce a quantity characterizing the process of infection distribution from one level of social hierarchy to the nearest lower levels, spreading entropy E. The parameter a is proportional to E.

Foot-and-mouth disease (FMD) is a highly contagious viral disease that affects ruminants and pigs. Countries with large exports of livestock products are highly vulnerable to economic damage following an FMD incursion. The faster disease spread is controlled, the lower the economic damage. During the past decades, the structure of livestock production has dramatically changed. To maintain the relevance of contingency plans, it is important to understand the effects of changes in herd structure on the spread and control of infectious diseases. In this study, we compare the spread and control of FMD based on 2006/2007 and 2018 livestock data. Spread of FMD in Denmark was simulated using the DTU-DADS model, applying different control measures. The number of cattle, swine and sheep/goat herds reduced from about 50,000 in total in 2006/2007 to about 33,000 in 2018. During this period, the average number of outgoing animal movements and the exports of swine and swine products increased by about 35% and 22%, respectively. This coincided with an overall increase in herd size of 14%. Using the EU and national control measures (Basic: 3 days standstill, depopulation of detected herds followed by cleaning and disinfection and establishment of control zones, where tracing, surveillance and contact restrictions are implemented), we found that the simulated epidemics in 2018 would be about 50% shorter in duration, affect about 50% fewer herds but cause more economic damage, compared to epidemics using 2006/2007 data. When 2006/2007 data were used, Basic + pre-emptive depopulation (Depop) overall was the optimal control strategy. When 2018 data were used, this was the case only when epidemics were initiated in cattle herds, whereas when epidemics were initiated in sow or sheep/goats herds, basic performed as well as Depop. The results demonstrate that regular assessment of measures to control the spread of infectious diseases is necessary for contingency planning., (© 2020 Blackwell Verlag GmbH.)

Besides the direct transport of animals, also indirect transmission routes, e.g., contact via contaminated vehicles, have to be considered. In this study, the transmission routes of a German pig trade network were illustrated as a monopartite animal movements network and two bipartite networks including information of the transport company and the feed producer which were projected on farm level ( n = 866) to enable a comparison. The networks were investigated with the help of network analysis and formed the basis for epidemiological models to evaluate the impact of different transmission routes on network structure as well as on potential epidemic sizes. The number of edges increased immensely from the monopartite animal movements network to both projected networks. The median centrality parameters revealed clear differences between the three representations. Furthermore, moderate correlation coefficients ranging from 0.55 to 0.68 between the centrality values of the animal movements network and the projected transportation network were obtained. The epidemiological models revealed significantly more infected farms for both projected networks (70% to 100%) compared to the animal movements network (1%). The inclusion of indirect transmission routes had an immense impact on the outcome of centrality parameters as well as on the results of the epidemiological models.