Detecting Suspected Epidemic Cases Using Trajectory Big Data (CSIAM Transactions on Applied Mathematics, 2020)
Emerging infectious diseases are existential threats to human health andglobal stability. The recent outbreaks of the novel coronavirus COVID-19 have rapidlyformed a global pandemic, causing hundreds of thousands of infections and huge economic loss. The WHO declares that more precise measures to track, detect and isolateinfected people are among the most effective means to quickly contain the outbreak.Based on trajectory provided by the big data and the mean field theory, we establish anaggregated risk mean field that contains information of all risk-spreading particles byproposing a spatio-temporal model named HiRES risk map. It has dynamic fine spatialresolution and high computation efficiency enabling fast update. We then propose anobjective individual epidemic risk scoring model named HiRES-p based on HiRES riskmaps, and use it to develop statistical inference and machine learning methods for detecting suspected epidemic-infected individuals. We conduct numerical experimentsby applying the proposed methods to study the early outbreak of COVID-19 in China.Results show that the HiRES risk map has strong ability in capturing global trend andlocal variability of the epidemic risk, thus can be applied to monitor epidemic risk atcountry, province, city and community levels, as well as at specific high-risk locationssuch as hospital and station. HiRES-p score seems to be an effective measurement ofpersonal epidemic risk. The accuracy of both detecting methods are above 90% whenthe population infection rate is under 20%, which indicates great application potentialin epidemic risk prevention and control practice.