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Humans live in complex socio-ecological systems where we interact with parasites and pathogens that spend time in abiotic and biotic environmental reservoirs (e.g., water, air, soil, other vertebrate hosts, vectors, intermediate hosts). Through a synthesis of published literature, we reviewed the life cycles and environmental persistence of 150 parasites and pathogens tracked by the World Health Organization's Global Burden of Disease study. We used those data to derive the time spent in each component of a pathogen's life cycle, including total time spent in humans versus all environmental stages. We found that nearly all infectious organisms were “environmentally mediated” to some degree, meaning that they spend time in reservoirs and can be transmitted from those reservoirs to human hosts. Correspondingly, many infectious diseases were primarily controlled through environmental interventions (e.g., vector control, water sanitation), whereas few (14%) were primarily controlled by integrated methods (i.e., combining medical and environmental interventions). Data on critical life history attributes for most of the 150 parasites and pathogens were difficult to find and often uncertain, potentially hampering efforts to predict disease dynamics and model interactions between life cycle time scales and infection control strategies. We hope that this synthetic review and associated database serve as a resource for understanding both common patterns among parasites and pathogens and important variability and uncertainty regarding particular infectious diseases. These insights can be used to improve systems-based approaches for controlling environmentally mediated diseases of humans in an era where the environment is rapidly changing. 

Abstract Habitat alteration can influence suitability, creating ecological traps where habitat preference and fitness are mismatched. Despite their importance, ecological traps are notoriously difficult to identify and their impact on host–pathogen dynamics remains largely unexplored. Here we assess individual bat survival and habitat preferences in the midwestern United States before, during, and after the invasion of the fungal pathogen that causes white-nose syndrome. Despite strong selection pressures, most hosts continued to select habitats where disease severity was highest and survival was lowest, causing continued population declines. However, some individuals used refugia where survival was higher. Over time, a higher proportion of the total population used refugia than before pathogen arrival. Our results demonstrate that host preferences for habitats with high disease-induced mortality can create ecological traps that threaten populations, even in the presence of accessible refugia. 

Schistosome parasites infect more than 200 million people annually, mostly in sub-Saharan Africa, where people may be co-infected with more than one species of the parasite. Infection risk for any single species is determined, in part, by the distribution of its obligate intermediate host snail. As the World Health Organization reprioritizes snail control to reduce the global burden of schistosomiasis, there is renewed importance in knowing when and where to target those efforts, which could vary by schistosome species. This study estimates factors associated with schistosomiasis risk in 16 villages located in the Senegal River Basin, a region hyperendemic for Schistosoma haematobium and S . mansoni . We first analyzed the spatial distributions of the two schistosomes’ intermediate host snails ( Bulinus spp. and Biomphalaria pfeifferi , respectively) at village water access sites. Then, we separately evaluated the relationships between human S . haematobium and S . mansoni infections and (i) the area of remotely-sensed snail habitat across spatial extents ranging from 1 to 120 m from shorelines, and (ii) water access site size and shape characteristics. We compared the influence of snail habitat across spatial extents because, while snail sampling is traditionally done near shorelines, we hypothesized that snails further from shore also contribute to infection risk. We found that, controlling for demographic variables, human risk for S . haematobium infection was positively correlated with snail habitat when snail habitat was measured over a much greater radius from shore (45 m to 120 m) than usual. S . haematobium risk was also associated with large, open water access sites. However, S . mansoni infection risk was associated with small, sheltered water access sites, and was not positively correlated with snail habitat at any spatial sampling radius. Our findings highlight the need to consider different ecological and environmental factors driving the transmission of each schistosome species in co-endemic landscapes. 

Host–parasite dynamics are impacted by the relationship between host density and parasite transmission, and thus, all epidemiological models contain a central transmission–density function. Recent theoretical work demonstrates that this central parasite transmission function might be best represented by a nonlinear continuum from one linear extreme to another: density‐dependent transmission at low host densities to density‐independent transmission at high host densities. But how often are nonlinear transmission functions used, and when are they better at describing transmission in real host–parasite systems?

To quantify existing modelling practices, we systematically reviewed seven representative ecology journals, finding 262 studies containing host–parasite models that contained linear and/or nonlinear transmission functions. We also reviewed the literature to find 28 experimental and observational studies that compared multiple transmission functions in real host–parasite systems, and tallied which functions were best supported in those systems. Finally, we created a flexible model simulation tool to explore and quantify the bias in model parameter estimates that is created when using an inaccurate transmission function.

We found that most experimental and observational studies reported that nonlinear transmission–density functions outperformed simple linear transmission–density functions, supporting recent theoretical work. In contrast, most studies containing host–parasite models assumed that host density was constant and/or used a single, linear transmission function to explain how transmission rates changed with density. Using the wrong linear function and/or using a linear function when the underlying transmission–density relationship is even slightly nonlinear can substantially bias model parameter estimates, as demonstrated by our simulations over a broad parameter space.

Some modelling studies may be using linear functions in host–parasite systems where nonlinear functions are more appropriate. If true, these models would yield substantially biased parameter estimates. To avoid such biases that compromise ecological understanding and prediction, we recommend that future studies compare multiple transmission functions, including nonlinear options, whenever possible.