Effects of human mobility restrictions on the spread of Covid-19 in Shenzhen, China

A modelling study using mobile phone data

Datos Bibliográficos

ID23375693
AutoresYing Zhou (0000-0001-7335-5601, Shenzhen University), Renzhe Xu (0000-0001-8418-0034, Shenzhen University), Dongsheng Hu (0000-0002-3247-5786, Shenzhen University Health Science Center), Yang Yue (0009-0001-8732-7617, Shenzhen University), Qingquan Li (0000-0002-2438-6046, Shenzhen University), Jizhe Xia (0000-0002-6436-7456, Shenzhen University, autor de correspondencia)
Año2020
Volumen2
Número8
Páginase417-e424
Fecha de publicación2020-08-01
Peer ReviewedSí
Open AccessSí
TipoARTICLE
RevistaThe Lancet Digital Health (JOURNAL)
Identificadores de la revistaISSN: 2589-7500 • E-ISSN: 2589-7500
EditorialElsevier BV (PUBLISHER)
DOI10.1016/s2589-7500(20)30165-5
PMID32835199
OpenAlexW3044014021
IdiomaEN
Citas recibidas58
Referencias citadas18

Background Restricting 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. Methods In 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 ( R 0 ), length of infectious period, and the number of initial cases. Findings 331 COVID-19 cases distributed across the ten regions of Shenzhen were reported on Feb 7, 2020. In our basic scenario ( R 0 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. 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. 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. Funding Guangdong Medical Science Fund, and National Natural Science Foundation of China.

Basic reproduction number · China · Coronavirus disease 2019 (COVID-19) · Disease · Environmental health · Geography · Infectious disease (medical specialty) · Mobile phone · Pandemic · Population · Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) · Telecommunications · Transmissibility (structural dynamics) · Transmission (telecommunications) · Computer Science · COVID-19 and Mental Health · COVID-19 Digital Contact Tracing · COVID-19 epidemiological studies · Demography · Internal Medicine · Medicine