Population Flow Drives Spatio-temporal Distribution of COVID-19 in China

Abstract

Sudden, large-scale, and diffuse human migration can amplify localized outbreaks into widespread epidemics.1–4 Rapid and accurate tracking of aggregate population flows may therefore be epidemiologically informative. Here, we use mobile-phone-data-based counts of 11,478,484 people egressing or transiting through the prefecture of Wuhan between 1 January and 24 January 2020 as they moved to 296 prefectures throughout China. First, we document the efficacy of quarantine in ceasing movement. Second, we show that the distribution of population outflow from Wuhan accurately predicts the relative frequency and geographic distribution of COVID-19 infections through February 19, 2020, across all of China. Third, we develop a spatio-temporal “risk source” model that leverages population flow data (which operationalizes risk emanating from epidemic epicenters) to not only forecast confirmed cases, but also to identify high-transmission-risk locales at an early stage. Fourth, we use this risk source model to statistically derive the geographic spread of COVID-19 and the growth pattern based on the population outflow from Wuhan; the model yields a benchmark trend and an index for assessing COVID-19 community transmission risk over time for different locations. This approach can be used by policy-makers in any nation with available data to make rapid and accurate risk assessments and to plan allocation of limited resources ahead of ongoing outbreaks.