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 in uenza like illness. The inferences of this project will improve our understand- ing of the impact of di erent control and prevention strategies for infectious disease epidemics in general and in uenza epidemics in particular. Our hypothesis is that individual behavior, disease dynamics, and interventions coevolve across multi- ple scales to create statistically and epidemiologically signi cant di erences in the ecacy and social equity of public health policies such as infectious disease control strategies. This hypothesis will be tested by pursuing the following speci c aims: 1. Identify social behaviors across communities that strongly a ect 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 ecacy of targeted, layered containment of pandemic in uenza. 3. Analyze interactions between behavioral di erences 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 infec- tious 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 partic- ular 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 in uenza transmis- sibility and intervention ecacy will be revisited with the improved simulations. Initially, 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 di erences 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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