The objective of this project, Systems analysis of social pathways of epidemics to reduce health disparities is to incorporate social behavior into mathematical models of infectious disease transmission dynamics, with a focus on influenza like illness. The inferences of this project will improve our understanding of the impact of different control and prevention strategies for infectious disease epidemics in general and influenza epidemics in particular. Our hypothesis is that individual behavior, disease dynamics, and interventions coevolve across multiple scales to create statistically and epidemiologically significant differences in the efficacy and social equity of public health policies such as infectious disease control strategies. This hypothesis will be tested by pursuing the following specific aims: 1. Identify social behaviors across communities that strongly a predict transmission dynamics of infectious disease epidemics. 2. Evaluate how the lack of dynamic behavioral response to epidemic evolution a ects previous model- based estimates for transmissibility and the efficacy of targeted, layered containment of pandemic influenza. 3. Analyze interactions between behavioral differences and epidemic interventions to facilitate the design of optimal interventions to reduce health disparities. This project extends well studied computational simulations to include people's behaviors relevant to infectious disease epidemics and will be used to determine the consequences of feedback between population-level e ects and individual-level behavior. In particular, we will determine the sensitivity of outcomes to particular behaviors. A survey designed to focus on those particular behaviors will be used to estimate variability across communities and to calibrate the simulations. Published models and results on influenza transmissibility and intervention effcacy will be revisited with the improved simulations. Initiall, our analysis will describe the mean performance of interventions over the whole population. The analyses will then extend to scenarios re ecting the observed variability in behavior to reveal how health disparities could arise from behavioral differences at the community level. Altogether, the results of the new and comparative analyses will inform the design of optimal epidemic interventions with fewer unintended consequences.
This research project will fill an important gap in understanding individual social behavior, disease dynamics and preventive interventions, especially in the domain of infectious disease spread. The knowledge and methods developed here will enable society to control outbreaks of infectious diseases effectively and equitably.
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