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When raccoon rabies first invaded the mid-Atlantic United States, epizootics were larger, longer, and more pronounced than those in its historic, more southern, range, suggesting a North-South gradient in disease dynamics. In addition, due to higher raccoon densities and concentrated feeding sources, urban areas might sustain larger epizootics, suggesting an urban-rural gradient might likewise influence dynamics. Here we leverage long-term surveillance data on raccoon rabies, collated by the Centers for Disease Control and Prevention, United States Department of Agriculture, and state and local public health agencies to better understand the role of latitude and urbanness for raccoon rabies epizootiology. Our analysis utilizes surveillance data from the 20 states composing the raccoon rabies enzootic area across 2006–2018. We identified effects of latitude and human population density (a proxy for urbanness) on the county-level probability of detecting raccoon rabies. We find that: 1) in the northeastern US, more samples are submitted in the summer, and more positive results are obtained, albeit with a lower likelihood of a given sample being found to be rabid, while these trends are independent of season at southern latitudes; 2) the association between urbanness and risk of rabies cases varies across latitude, with greater rabies presence in rural vs. urban counties in the south and a more consistent risk across urbanness in the north; and 3) the most consistent predictors of raccoon rabies detection are spatiotemporal effects, suggesting that recent detection of cases in a county or its neighbors are more informative of raccoon rabies dynamics than are general metrics like latitude and urbanness. Statistical and spatial long-term studies like these not only can improve understanding of wildlife disease patterns but can help guide public health and wildlife management efforts in areas most at risk for raccoon rabies virus infection. 

Social and spatial structures of host populations play important roles in pathogen transmission. For environmentally transmitted pathogens, the host space use interacts with both the host social structure and the pathogen’s environmental persistence (which determines the time-lag across which two hosts can transmit). Together, these factors shape the epidemiological dynamics of environmentally transmitted pathogens. While the importance of both social and spatial structures and environmental pathogen persistence has long been recognized in epidemiology, they are often considered separately. A better understanding of how these factors interact to determine disease dynamics is required for developing robust surveillance and management strategies. Here, we use a simple agent-based model where we vary host mobility (spatial), host gregariousness (social) and pathogen decay (environmental persistence), each from low to high levels to uncover how they affect epidemiological dynamics. By comparing epidemic peak, time to epidemic peak and final epidemic size, we show that longer infectious periods, higher group mobility, larger group size and longer pathogen persistence lead to larger, faster growing outbreaks, and explore how these processes interact to determine epidemiological outcomes such as the epidemic peak and the final epidemic size. We identify general principles that can be used for planning surveillance and control for wildlife host–pathogen systems with environmental transmission across a range of spatial behaviour, social structure and pathogen decay rates. This article is part of the theme issue ‘The spatial–social interface: a theoretical and empirical integration’. 

Influenza A virus (IAV) is a multi-host pathogen maintained in water birds and capable of spillover into humans, wildlife, and livestock. Prior research has focused on dabbling ducks as a known IAV reservoir species, yet our understanding of influenza dynamics in other water birds, including gulls, is lacking. Here, we quantify morphological and environmental drivers of serological (antibody detection by ELISA) and virological (viral RNA detection by PCR) prevalence in two gull species: ring-billed (Larus delawarensis) and Franklin’s (Leucophaeus pipixcan) gulls. Across 12 months and 10 locations, we tested over 1500 gulls for influenza viral RNA, and additionally tested antibody levels in nearly 1000 of these. We find substantial virus prevalence and a large, nonoverlapping seroprevalence, with significant differences across age and species classifications. The body condition index had minimal explanatory power to predict (sero)positivity, and the effect of the surrounding environment was idiosyncratic. Our results hint at a nontrivial relationship between virus and seropositivity, highlighting serological surveillance as a valuable counterpoint to PCR. By providing indication of both past infections and susceptibility to future infections, serosurveillance can help inform the distribution of limited resources to maximize surveillance effectiveness for a disease of high human, wildlife, and livestock concern. 

Abstract Animal space use and spatial overlap can have important consequences for population‐level processes such as social interactions and pathogen transmission. Identifying how environmental variability and inter‐individual variation affect spatial patterns and in turn influence interactions in animal populations is a priority for the study of animal behaviour and disease ecology. Environmental food availability and macroparasite infection are common drivers of variation, but there are few experimental studies investigating how they affect spatial patterns of wildlife.Bank voles (Clethrionomys glareolus) are a tractable study system to investigate spatial patterns of wildlife and are amenable to experimental manipulations. We conducted a replicated, factorial field experiment in which we provided supplementary food and removed helminths in vole populations in natural forest habitat and monitored vole space use and spatial overlap using capture–mark–recapture methods.Using network analysis, we quantified vole space use and spatial overlap. We compared the effects of food supplementation and helminth removal and investigated the impacts of season, sex and reproductive status on space use and spatial overlap.We found that food supplementation decreased vole space use while helminth removal increased space use. Space use also varied by sex, reproductive status and season. Spatial overlap was similar between treatments despite up to threefold differences in population size.By quantifying the spatial effects of food availability and macroparasite infection on wildlife populations, we demonstrate the potential for space use and population density to trade‐off and maintain consistent spatial overlap in wildlife populations. This has important implications for spatial processes in wildlife including pathogen transmission. 

Ongoing environmental changes alter how natural selection shapes animal migration. Understanding how these changes play out theoretically can be done using evolutionary game theoretic (EGT) approaches, such as looking for evolutionarily stable strategies. Here, we first describe historical patterns of how EGT models have explored different drivers of migration. We find that there are substantial gaps in both the taxa (mammals, amphibians, reptiles, insects) and mechanisms (mutualism, interspecific competition) included in past EGT models of migration. Although enemy interactions, including parasites, are increasingly considered in models of animal migration, they remain the least studied of factors for migration considered to date. Furthermore, few papers look at changes in migration in response to perturbations (e.g. climate change, new species interactions). To address this gap, we present a new EGT model to understand how infection with a novel parasite changes host migration. We find three possible outcomes when migrants encounter novel parasites: maintenance of migration (despite the added infection cost), loss of migration (evolutionary shift to residency) or population collapse, depending on the risk and cost of getting infected, and the cost currency. Our work demonstrates how emerging infection can alter animal behaviour such as migration. This article is part of the theme issue ‘Half a century of evolutionary games: a synthesis of theory, application and future directions’. 

Abstract Understanding how the movement of individuals affects disease dynamics is critical to accurately predicting and responding to the spread of disease in an increasingly interconnected world. In particular, it is not yet known how movement between patches affects local disease dynamics (e.g., whether pathogen prevalence remains steady or oscillates through time). Considering a set of small, archetypal metapopulations, we find three surprisingly simple patterns emerge in local disease dynamics following the introduction of movement between patches: (1) movement between identical patches with cyclical pathogen prevalence dampens oscillations in the destination while increasing synchrony between patches; (2) when patches differ from one another in the absence of movement, adding movement allows dynamics to propagate between patches, alternatively stabilizing or destabilizing dynamics in the destination based on the dynamics at the origin; and (3) it is easier for movement to induce cyclical dynamics than to induce a steady-state. Considering these archetypal networks (and the patterns they exemplify) as building blocks of larger, more realistically complex metapopulations provides an avenue for novel insights into the role of host movement on disease dynamics. Moreover, this work demonstrates a framework for future predictive modelling of disease spread in real populations. 

Close contacts between individuals provide opportunities for the transmission of diseases, including COVID-19. While individuals take part in many different types of interactions, including those with classmates, co-workers and household members, it is the conglomeration of all of these interactions that produces the complex social contact network interconnecting individuals across the population. Thus, while an individual might decide their own risk tolerance in response to a threat of infection, the consequences of such decisions are rarely so confined, propagating far beyond any one person. We assess the effect of different population-level risk-tolerance regimes, population structure in the form of age and household-size distributions, and different interaction types on epidemic spread in plausible human contact networks to gain insight into how contact network structure affects pathogen spread through a population. In particular, we find that behavioural changes by vulnerable individuals in isolation are insufficient to reduce those individuals’ infection risk and that population structure can have varied and counteracting effects on epidemic outcomes. The relative impact of each interaction type was contingent on assumptions underlying contact network construction, stressing the importance of empirical validation. Taken together, these results promote a nuanced understanding of disease spread on contact networks, with implications for public health strategies. 

Close contacts between individuals provide opportunities for the transmission of diseases, including COVID-19. While individuals take part in many different types of interactions, including those with classmates, co-workers and household members, it is the conglomeration of all of these interactions that produces the complex social contact network interconnecting individuals across the population. Thus, while an individual might decide their own risk tolerance in response to a threat of infection, the consequences of such decisions are rarely so confined, propagating far beyond any one person. We assess the effect of different population-level risk-tolerance regimes, population structure in the form of age and household-size distributions, and different interaction types on epidemic spread in plausible human contact networks to gain insight into how contact network structure affects pathogen spread through a population. In particular, we find that behavioural changes by vulnerable individuals in isolation are insufficient to reduce those individuals’ infection risk and that population structure can have varied and counteracting effects on epidemic outcomes. The relative impact of each interaction type was contingent on assumptions underlying contact network construction, stressing the importance of empirical validation. Taken together, these results promote a nuanced understanding of disease spread on contact networks, with implications for public health strategies.