Quantifying How Social Mixing Patterns Affect Disease Transmission

S.E. LeGresley, S.Y. Del Valle, and J.M. Hyman

We analyze how disease spreads among diff erent age groups in an agent-based computer simulation of a synthetic population. Quantifying the relative importance of di fferent daily activities of a population is crucial for understanding the disease transmission and in guiding mitigation strategies. Although there is very little real-world data for these mixing patterns, there is mixing data from virtual world models, such as the Los Alamos Epidemic Simulation System (EpiSimS). We use this platform to analyze the synthetic mixing patterns generated in southern California and to estimate the number and duration of contacts between people of diff erent ages. We approximate the probability of transmission based on the duration of the contact, as well as a matrix that depicts who acquired infection from whom (WAIFW). We provide some of the first quantitative estimates of how infections spread among diff erent age groups based on the mixing patterns and activities at home, school, and work. The analysis of the EpiSimS data quantifi es the central role of schools in the early spread of an epidemic. Our results support the hypothesis that schools are the most likely place for early transmission and that mitigation strategies targeting school-aged children are one of the most eff ective strategies in fi ghting an epidemic.

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