Investigating the relationship between reopening the economy and implementing control measures during the COVID-19 pandemic.

HEALTH policy; COVID-19; PREVENTION of communicable diseases; MATHEMATICAL models; INTERPERSONAL relations; THEORY; STAY-at-home orders; SOCIAL distancing; POLICY sciences; COVID-19 pandemic; PATIENT safety

The COVID-19 pandemic has resulted in an enormous burden on population health and the economy around the world. Although most cities in the United States have reopened their economies from previous lockdowns, it was not clear how the magnitude of different control measures—such as face mask use and social distancing—may affect the timing of reopening the economy for a local region. This study aimed to investigate the relationship between reopening dates and control measures and identify the conditions under which a city can be reopened safely. This was a mathematical modeling study. We developed a dynamic compartment model to capture the transmission dynamics of COVID-19 in New York City. We estimated model parameters from local COVID-19 data. We conducted three sets of policy simulations to investigate how different reopening dates and magnitudes of control measures would affect the COVID-19 epidemic. The model estimated that maintaining social contact at 80% of the prepandemic level and a 50% face mask usage would prevent a major surge of COVID-19 after reopening. If social distancing were completely relaxed after reopening, face mask usage would need to be maintained at nearly 80% to prevent a major surge. Adherence to social distancing and increased face mask usage are keys to prevent a major surge after a city reopens its economy. The findings from our study can help policymakers identify the conditions under which a city can be reopened safely. [ABSTRACT FROM AUTHOR]

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