Inferring between-population differences in COVID-19 dynamics

Citation:

Wilder B, Charpingon M, Killian J, Ou H-C, Mate A, Jabbari S, Perrault A, Angel Desai M. Inferring between-population differences in COVID-19 dynamics, in AI for Social Good Workshop. ; 2020.

Abstract:

As the COVID-19 pandemic continues, formulating targeted policy interventions supported by differential SARS-CoV2 transmission dynamics will be of vital importance to national and regional governments. We develop an individual-level model for SARS-CoV2 transmission that accounts for location-dependent distributions of age, household structure, and comorbidities. We use these distributions together with age-stratified contact matrices to instantiate specific models for Hubei, China; Lombardy, Italy; and New York, United States. We then develop a Bayesian inference framework which leverages data on reported deaths to obtain a posterior distribution over unknown parameters and infer differences in the progression of the epidemic in the three locations. These findings highlight the role of between-population variation in formulating policy interventions.

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