Abstract:
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Ever since Ronald Ross tipped over a stagnant water tank in India and hypothesized that mosquitoes may be responsible for malaria, mathematical models have been developed for prediction and detection of infectious diseases. Approaches range from deterministic models to more computationally-driven agent-based models (ABMs). ABMs are simulations of interactions between "agents" generated from data and their environment, and provide a closer look at how behaviors, demography, and movement can influence the spread of disease. The synthetic "agents" in infectious disease modeling represent humans and are generated using spatio-demographic information from Census products. However, the rise of vector-borne diseases underscore the importance of generating mosquitoes, ticks, and other carriers of disease. We sample from species' spatial distributions, incorporating ecological factors to generate synthetic populations of vectors for use in ABMs. With the rise of the Zika virus, we explore the merits and limitations of several mathematical models, including ABMs. This talk will cover the historical context of disease modelling and the value of incorporating disease vectors in these studies.
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