@conference {1525292,
	title = {Inferring between-population differences in COVID-19 dynamics},
	booktitle = {AI for Social Good Workshop},
	year = {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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},
	author = {Bryan Wilder and Marie Charpingon and Jackson Killian and Ou, Han-Ching and Mate, Aditya and Shahin Jabbari and Perrault, Andrew and Angel Desai, Milind}
}
