Abstract
BackgroundRestricting human mobility is an effective strategy used to control disease spread. However, whether mobility restriction is a proportional response to control the ongoing COVID-19 pandemic is unclear. We aimed to develop a model that can quantify the potential effects of various intracity mobility restrictions on the spread of COVID-19.MethodsIn this modelling study, we used anonymous and aggregated mobile phone sightings data to build a susceptible–exposed–infectious–recovered transmission model for COVID-19 based on the city of Shenzhen, China. We simulated how disease spread changed when we varied the type and magnitude of mobility restrictions in different transmission scenarios, with variables such as the basic reproductive number (R0), length of infectious period, and the number of initial cases.Findings331 COVID-19 cases distributed across the ten regions of Shenzhen were reported on Feb 7, 2020. In our basic scenario (R0 of 2·68), mobility reduction of 20–60% within the city had a notable effect on controlling COVID-19 spread: a flattening of the peak number of cases by 33% (95% UI 21–42) and delay to the peak number by 2 weeks with a 20% restriction, 66% (48–75) reduction and 4 week delay with a 40% restriction, and 91% (79–95) reduction and 14 week delay with a 60% restriction. The effects of mobility restriction were increased when combined with reductions of 25% or 50% in transmissibility of the virus. In specific analyses of mobility restrictions for individuals with symptomatic infections and for high-risk regions, these measures also had substantial effects on reducing the spread of COVID-19. For example, the peak of the epidemic was delayed by 2 weeks if the proportion of individuals with symptomatic infections who could move freely was maintained at 20%, and by 4 weeks if two high-risk regions were locked down. The simulation results were also affected by various transmission parameters.InterpretationOur model shows the effects of various types and magnitudes of mobility restrictions on controlling COVID-19 outbreaks at the city level in Shenzhen, China. The model could help policy makers to establish the optimal combinations of mobility restrictions during the COVID-19 pandemic, especially to assess the potential positive effects of mobility restriction on public health in view of the potential negative economic and societal effects.FundingGuangdong Medical Science Fund, and National Natural Science Foundation of China.
Highlights
In January 2020, an outbreak of COVID-19 began in Wuhan, China, and this disease spread rapidly to more than 200 countries.[1]
331 COVID-19 cases distributed across the ten regions of Shenzhen were reported on Feb 7, 2020
Interpretation Our model shows the effects of various types and magnitudes of mobility restrictions on controlling COVID-19 outbreaks at the city level in Shenzhen, China
Summary
In January 2020, an outbreak of COVID-19 began in Wuhan, China, and this disease spread rapidly to more than 200 countries.[1] COVID-19 is caused by a novel coronavirus known as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which has a very high transmissibility.[2,3] Restricting public mobility is a crucial public health tool to control respiratory infectious diseases. Such restrictions include physical distancing and community containment measures for reducing public transport use and public gatherings, school closures, and working from home where possible. This scarcity of evidence is probably because such studies only captured information on some subpopulations, such as school children or people at work, not the whole population.[5,6]
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