Effects of human mobility restrictions on the spread of COVID-19 in Shenzhen, China: a modelling study using mobile phone data

Abstract

Summary 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.

Spatiotemporal spread pattern of the COVID-19 cases in China

“…For example, they also unexpectedly experienced a serious COVID-19 pandemic (news.china.com.cn), which may be attributed to the tourists coming from Wuhan, and some of them have carried the SARS-CoV-2 virus [20]. Population migration is closely related to the deterioration of pandemic, and it is a harbinger of the future situation of epidemics [21,22].…”

“…Because COVID-19 has an incubation period of even more than 40 days, these people outflow from Wuhan could cause community-level infections in other cities. Timely travel restrictions have weakened the spread of the pandemic [22,23]. Population social gathering together with the infection source from exposure in Wuhan has occurred in many cities such as Tianjin, Beijing and Chengdu, which was an essential cause leading to the rapid deterioration of the COVID-19.…”

How Urban Factors Affect the Spatiotemporal Distribution of Infectious Diseases in Addition to Intercity Population Movement in China

“…For example, Wells and Sah [12] examined the influence of cross-national travel and border-control policies on the spread of COVID-19 at a global level. Zhou and Xu [13] used mobile phone sightings data to examine the potential effects of intra-city mobility restrictions on the spread of COVID-19 in the city of Shenzhen. Massaro and Kondor [14] studied the interaction between human mobility and outbreaks of infectious diseases and found that intra-city human mobility is a major factor in the spread of infectious diseases in Singapore.…”

Do they really wash their hands? Prevalence estimates for personal hygiene behaviour during the COVID-19 pandemic based on indirect questions

“…Adherence to “stay at home” directives can be inferred from mobility data retrieved from mobile phones. These data can be used to test whether, at group level, people’s mobility behaviour changes as a function of the social (physical) distancing guidelines [ 32 ]. However, for other types of protective measures such data are not publicly available because they concern intimate aspects of people’s personal lives.…”