Abstract
Since the first case of the novel coronavirus disease (COVID-19) was confirmed in Wuhan, China, social distancing has been promoted worldwide, including in the United States, as a major community mitigation strategy. However, our understanding remains limited in how people would react to such control measures, as well as how people would resume their normal behaviours when those orders were relaxed. We utilize an integrated dataset of real-time mobile device location data involving 100 million devices in the contiguous United States (plus Alaska and Hawaii) from February 2, 2020 to May 30, 2020. Built upon the common human mobility metrics, we construct a Social Distancing Index (SDI) to evaluate people’s mobility pattern changes along with the spread of COVID-19 at different geographic levels. We find that both government orders and local outbreak severity significantly contribute to the strength of social distancing. As people tend to practice less social distancing immediately after they observe a sign of local mitigation, we identify several states and counties with higher risks of continuous community transmission and a second outbreak. Our proposed index could help policymakers and researchers monitor people’s real-time mobility behaviours, understand the influence of government orders, and evaluate the risk of local outbreaks.
Highlights
Since the first case of the novel coronavirus disease (COVID-19) was confirmed in Wuhan, China, social distancing has been promoted worldwide, including in the United States, as a major community mitigation strategy
How do people react to government actions and perform social distancing? What is the reopening readiness of each region? How can we measure the risk of a second outbreak? This paper proposes a Social Distancing Index (SDI) based on mobile device location data to reveal people’s mobility patterns in response to the COVID-19 outbreak, social distancing policies, and reopening plans
Existing studies on impact assessment of control measures mainly estimate related modelling parameters by Markov Chain Monte Carlo (MCMC)[4]; utilize the simulation models to estimate contact network based on a synthetic population[5]; estimate the contact patterns using survey data, modelling and simulation[6,7]; and collect people’s behaviour reactions through dedicated surveys[8]
Summary
Since the first case of the novel coronavirus disease (COVID-19) was confirmed in Wuhan, China, social distancing has been promoted worldwide, including in the United States, as a major community mitigation strategy. Built upon the common human mobility metrics, we construct a Social Distancing Index (SDI) to evaluate people’s mobility pattern changes along with the spread of COVID-19 at different geographic levels. We find that both government orders and local outbreak severity significantly contribute to the strength of social distancing. Our proposed index could help policymakers and researchers monitor people’s real-time mobility behaviours, understand the influence of government orders, and evaluate the risk of local outbreaks. This paper proposes a Social Distancing Index (SDI) based on mobile device location data to reveal people’s mobility patterns in response to the COVID-19 outbreak, social distancing policies, and reopening plans. Category-based indices are usually built upon a single variable and the score-based ones are more capable of integrating multiple metrics to be more informative
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