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Lockdown policies, such as stay-at-home orders, are known to be effective in controlling the spread of the novel coronavirus disease 2019. However, concerns over economic burdens of these policies rapidly propelled U.S. states to move toward reopening in the early stage of the pandemic. Decision making in most states has been challenging, especially because of a dearth of quantitative evidence on health gains versus economic burdens of different policies. To assist decision makers, we study the health and economic impacts of various lockdown policies across U.S. states and shed light on policies that are most effective. To this end, we make use of detailed data from 50 U.S. states plus the District of Columbia on various factors, including number of tests, positive and negative results, hospitalizations, ICU beds and ventilators used, residents’ mobility obtained from cellphone data, and deaths. Our analyses allow quantifying the total cost versus the total quality-adjusted life year (QALY) associated with various lockdown policies. We utilize a compartmental model with Markov chain Monte Carlo simulation to estimate the spread of disease. To calibrate our model separately for each U.S. state, we make use of empirical data on the intensity of intervention policies, age, ratio of Black/Hispanic populations, per capita income, residents’ mobility, and number of daily tests and feed them to a longitudinal mixed-effect model. Finally, we utilize a microsimulation model to estimate the total cost and total QALY for each state and perform cost-effectiveness analysis to identify policies that would have worked best. Our results show that, compared with no intervention during March–June 2020, the policies undertaken across the United States saved, on average, about 41,284.51 years’ worth of QALY (per 100K capita), incurring $164.01 million (per 100K capita). Had the states undertaken more strict policies during the same time frame than those they adopted, these values would be 44,909.41 years and $117.28 million, respectively. By quantifying the impact of various lockdown policies separately for each state, our results allow federal and state authorities to avoid following a “one size fits all” strategy and instead enact policies that are better suited for each state. Specifically, by studying the trade-offs between health gains and economic impacts, we identify the particular states that would have benefited from implementing more restrictive policies. Finally, in addition to shedding light on the impact of lockdown policies during our study period (March–June 2020), our results have important implications on curbing future fast-spreading variants of the coronavirus or other related potential epidemics.