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