Impacts of phenology on disease transmission dynamics

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The proposed projects are a comprehensive effort to rigorously investigate the contribution of phenology - or seasonal activity - to pathogen transmission dynamics using 3 widespread zoonotic pathogens carried by black-legged ticks. Variation in seasonal activity patterns and life-history events, which are well documented in species inhabiting temperate regions, can result in dramatic population dynamics including population extinctions or explosions. Phenological variation across time and geography also alters the frequency and strength of inter-species interactions and thus opportunities for pathogen transmission, which likely has profound impacts on pathogen transmission dynamics and human disease risk. Despite the likely importance to public health, the consequences of phenological variation on disease transmission dynamics remain notably under-studied in many complex zoonotic disease systems. The tick-borne disease system in North America provides the ideal system to determine the impact of phenology on pathogen transmission dynamics both theoretically and empirically. We will build and evaluate solvable analytical models, computational models, and advanced statistical models, all of which explicitly incorporate tick seasonal activity, and validate these models with empirical data from natural field sites. The proposed projects will (1) determine the quantitative impact of different phenological scenarios on the transmission dynamics of, and thus disease risk from, 3 human pathogens, (2) empirically evaluate model predictions, and (3) identify specific phenological and environmental features that result in different transmission dynamic outcomes among pathogen species. We will develop 3 modeling frameworks to investigate the impact of phenology on transmission dynamics, identify and quantify phenological and environmental drivers of transmission dynamics in 3 important human pathogens, and empirically validate the outcomes of the theoretical and statistical models. Empirical validation of both the phenological drivers and model predictions is essential not only for public health management but also to identify mechanistic processes driving these patterns. The proposed research provides both theoretical and empirical frameworks to investigate the impact of phenology on transmission dynamics in the multitude of disease systems involving multiple host species or life-stages from insect-vectored plant pathogens to pathogens with multi-host life-cycles.