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This paper empirically studies the government task assignment problem in the context of dynamic policy implementation. We focus on effective mitigation policy design to reduce virus spread in the COVID-19 pandemics. We start by estimating a structural SIR model with regional spillover effects using indirect inference to model virus transmission. Then, we develop and estimate a dynamic game model where each U.S. state independently forms mitigation policies. Socially optimal mitigation policy is then solved by minimizing the sum of local governments' welfare loss using estimated weights on different sectors. Counterfactual analysis of centralized decision-making is conducted to compare the social welfare gain (loss) should the US adopt a mitigation policy at the federal level.