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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’. 

More than 1.6 million Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) tests were administered daily in the United States at the peak of the epidemic, with a significant focus on individual treatment. Here, we show that objective-driven, strategic sampling designs and analyses can maximize information gain at the population level, which is necessary to increase situational awareness and predict, prepare for, and respond to a pandemic, while also continuing to inform individual treatment. By focusing on specific objectives such as individual treatment or disease prediction and control (e.g., via the collection of population-level statistics to inform lockdown measures or vaccine rollout) and drawing from the literature on capture–recapture methods to deal with nonrandom sampling and testing errors, we illustrate how public health objectives can be achieved even with limited test availability when testing programs are designed a priori to meet those objectives.