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During the early stage of the COVID-19 pandemic, many countries implementednon-pharmaceutical interventions (NPIs) to control the transmission ofSARS-CoV-2, the causative pathogen of COVID-19. Among those NPIs, quarantinemeasures were widely adopted and enforced through stay-at-home andshelter-in-place orders. Understanding the effectiveness of quarantine measurescan inform decision-making and control planning during the ongoing COVID-19pandemic and for future disease outbreaks. In this study, we use mathematicalmodels to evaluate the impact of quarantine measures on COVID-19 spread in fourcities that experienced large-scale outbreaks in the spring of 2020: Wuhan, NewYork, Milan, and London. We develop a susceptible-exposed-infected-removed(SEIR)-type model with a component of quarantine and couple this diseasetransmission model with a data assimilation method. By calibrating the model tocase data, we estimate key epidemiological parameters before lockdown in eachcity. We further examine the impact of quarantine rates on COVID-19 spreadafter lockdown using model simulations. Results indicate that quarantine ofsusceptible and exposed individuals and undetected infections is necessary tocontain the outbreak; however, the quarantine rates for these populations canbe reduced through faster isolation of confirmed cases. We generatecounterfactual simulations to estimate effectiveness of quarantine measures.Without quarantine measures, the cumulative confirmed cases could be 73, 22, 43and 93 times higher than reported numbers within 40 days after lockdown inWuhan, New York, Milan, and London. Our findings underscore the essential roleof quarantine during the early phase of the pandemic.

Evaluating the impact of quarantine measures on COVID-19 spread