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Abstract Spatial aggregation of environmental or trophically transmitted parasites has the potential to influence host–parasite interactions. The distribution of parasites on hosts is one result of those interactions, and the role of spatial aggregation is unclear. We use a spatially explicit agent‐based model to determine how spatial aggregation of parasites influences the distribution of parasite burdens across a range of parasite densities and host recovery rates. Our model simulates the random movement of hosts across landscapes with varying spatial configurations of areas occupied by environmental parasites, allowing hosts to acquire parasites they encounter and subsequently lose them. When parasites are more spatially aggregated in the environment, the aggregation of parasite burdens on hosts is higher (i.e., more hosts with few parasites, fewer hosts with many parasites), but the effect is less pronounced at high parasite density and fast host recovery rates. In addition, the correlation between individual hosts' final parasite burdens and their cumulative parasite burdens (including lost parasites) is greater at higher levels of spatial parasite aggregation. Our work suggests that fine‐scale spatial patterns of parasites can play a strong role in shaping how hosts are parasitized, particularly when parasite density is low‐to‐moderate and recovery rates are slow. 

ABSTRACT Efficient learning about disease dynamics in free‐ranging wildlife systems can benefit from active surveillance that is standardized across different ecological contexts. For example, active surveillance that targets specific individuals and populations with standardized sampling across ecological contexts (landscape‐scale targeted surveillance) is important for developing a mechanistic understanding of disease emergence, which is the foundation for improving risk assessment of zoonotic or wildlife‐livestock disease outbreaks and predicting hotspots of disease emergence. However, landscape‐scale targeted surveillance systems are rare and challenging to implement. Increasing experience and infrastructure for landscape‐scale targeted surveillance will improve readiness for rapid deployment of this type of surveillance in response to new disease emergence events. Here, we describe our experience developing and rapidly deploying a landscape‐scale targeted surveillance system for severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) in two free‐ranging deer species across their ranges in the United States. Our surveillance system was designed to collect data across individual, population, and landscape scales for future analyses aimed at understanding mechanisms and risk factors of SARS‐CoV‐2 transmission, evolution, and persistence. Our approach leveraged partnerships between state and federal public service sectors and academic researchers in a landscape‐scale targeted surveillance research network. Methods describe our approach to developing the surveillance network and sampling design. Results report challenges with implementing our intended sampling design, specifically how the design was adapted as different challenges arose and summarize the sampling design that has been implemented thus far. In the discussion, we describe strategies that were important for the successful deployment of landscape‐scale targeted surveillance, development and operation of the research network, construction of similar networks in the future, and analytical approaches for the data based on the sampling design. 

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. 

This repository release is to provide a DOI for the R code underlying a forthcoming publication in Ecosphere: Title: Parasite spatial distribution shapes parasite aggregation on host populations, but not at high parasite density Authors: Chris Wojan, Allison Shaw, Meggan Craft 

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.