Center for Transformative Infectious Disease Research on Climate, Health and Equity in a Changing Environment (C-CHANGE)

PROJECT 1 PROJECT DESCRIPTION Our proposed studies will investigate how climate change affects risk of spillover of zoonotic infections from bats. Our work will help identify evidence-based strategies to prevent disease emergence in multiple ecological settings. Bats are the known or progenitor hosts of four of the nine diseases identified by the World Health Organization as research priorities. Bats also may be exquisitely vulnerable to changes in climate because their food resources are climate driven and physiologically they are sensitive to temperature changes. We will leverage two long-running datasets on emerging pathogens from bat hosts to examine the role of climate in driving shedding patterns and spatial behavior of bats and likelihood of exposures in humans. Our methods, innovative both conceptually and methodologically, will use two complementary datasets, one from Australia and one from South Africa. We will integrate these datasets to move beyond assumption-laden correlations of spillover risk towards mechanistic understanding of how climatic drivers interact with ecological, environmental, and host-viral drivers of infections to drive disease emergence. Our work will help test whether mechanisms that drive spillover in South Africa and Australia are similar and are therefore likely to be generalizable to other geographical areas. Our work sets the stage for remote sensing methods to identify places and periods to prioritize for prevention of spillover. In Aim 1, we will identify climate-driven effects on the health of bats, diet, and paramyxo-, corona-, and filovirus infection dynamics in Australia and South Africa, using retrospective data. Hypothesis: We will observe higher prevalence of viral shedding in sampled bats after or during specific climatic conditions (e.g., hot, dry, then wet conditions, flood, high rainfall) and higher prevalence will be correlated to markers of bat health, diet and viral infection dynamics. We will use novel explainable-AI and Bayesian multilevel models to analyze these data. In Aim 2: we will investigate how host space-use changes in relation to climate-driven resource availability and how these changes drive spatial and temporal overlap of bats and humans. Hypothesis: resource constraints on natural food sources will lead to increased contact between bats, humans, and bridging hosts. Our work will lead to strategies, derived from empirical data and modeling, that can help prevent spillover of pathogens from bats to humans. Preventing spillovers reduces human mortality and morbidity, is highly cost- effective when strategies are successful, and can lead to interventions that buffer effects of climate change on sensitive species such as bats.