Human mobility restrictions and the spread of the Novel Coronavirus (2019-nCoV) in China

By Hanming Fang, Long Wang, Yang Yang. medRxiv, 2020

667 citations recorded by the source corpus.

We quantify the causal impact of human mobility restrictions, particularly the lockdown of the city of Wuhan on January 23, 2020, on the containment and delay of the spread of the Novel Coronavirus (2019-nCoV). We employ a set of difference-in-differences (DID) estimations to disentangle the lockdown effect on human mobility reductions from other confounding effects including panic effect, virus effect, and the Spring Festival effect. We find that the lockdown of Wuhan reduced inflow into Wuhan by 76.64%, outflows from Wuhan by 56.35%, and within-Wuhan movements by 54.15%. We also estimate the dynamic effects of up to 22 lagged population inflows from Wuhan and other Hubei cities, the epicenter of the 2019-nCoV outbreak, on the destination cities' new infection cases. We find, using simulations with these estimates, that the lockdown of the city of Wuhan on January 23, 2020 contributed significantly to reducing the total infection cases outside of Wuhan, even with the social distancing measures later imposed by other cities. We find that the COVID-19 cases would be 64.81% higher in the 347 Chinese cities outside Hubei province, and 52.64% higher in the 16 non-Wuhan cities inside Hubei, in the counterfactual world in which the city of Wuhan were not locked down from January 23, 2020. We also find that there were substantial undocumented infection cases in the early days of the 2019-nCoV outbreak in Wuhan and other cities of Hubei province, but over time, the gap between the officially reported cases and our estimated "actual" cases narrows significantly. We also find evidence that enhanced social distancing policies in the 63 Chinese cities outside Hubei province are effective in reducing the impact of population inflows from the epicenter cities in Hubei province on the spread of 2019-nCoV virus in the destination cities elsewhere.

Subfields: Economics and Econometrics, Global and Planetary Change, Modeling and Simulation

View Paper: https://www.semanticscholar.org/paper/a154475ed4e3727de8fe27aac58a87cd310d34a9

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Citations in the corpus

Fear, lockdown, and diversion: Comparing drivers of pandemic economic decline 2020 (2020, neutral): “…et al. (2020), Barrios et al. (2020), Chen et al. (2020), Cicala et al. (2020), Couture et al. (2020), Cronin and Evans (2020), Dave et al. (2020a), Fang et al. (2020), Goldfarb and Tucker (2020), Gupta et al. (2020a, 2020b), Maloney and Taskin (2020) and Nguyen et al. (2020), Amuedo-Dorantes et…”
Lost in lockdown? COVID-19, social distancing, and mental health in Germany (2020, background): “…time of the SARS epidemic in 2003 (Yip et al. 2010).4 Social distancing measures such as stay–at–home orders are effective in containing the spread of COVID-19 (Fang et al. 2020), but, in addition to the outbreak of a pandemic itself, potentially cause severe mental illness (Brooks et al. 2020).”
Using Difference-in-Differences to Identify Causal Effects of COVID-19 Policies (2020, background): “At least five recent papers use DD methods to show that non-pharmaceutical interventions reduce interactions, infections, or deaths (Dave et al., 2020; Fang et al., 2020; Friedson et al., 2020; Gupta et al., 2020; Hsiang et al., 2020).”
A literature review of the economics of COVID‐19 (2020, background): “For example, Fang et al. (2020) argue that if lockdown policies had not been imposed in Wuhan, then the infection rates would have been 65% higher in cities outside of Wuhan.”
JUE insight: Learning epidemiology by doing: The empirical implications of a Spatial-SIR model with behavioral responses (2020, neutral): “(2020) and Farboodi et al. (2020), for a similar reduced form approach.”
Do Stay-at-Home Orders Cause People to Stay at Home? Effects of Stay-at-Home Orders on Consumer Behavior (2020, background): “[19] Hanming Fang, Long Wang, and Yang Yang.”