Detecting Suspected Epidemic Cases Using Trajectory Big Data
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Emerging infectious diseases are crucial threats to human health and global stability. The recent outbreak of the novel coronavirus COVID-19 has rapidly formed a global pandemic, causing hundreds of thousands of infections and huge economic loss. The WHO declares that more precise measures to track, detect and isolate infected people are among the most effective means to quickly contain the outbreak. Based on trajectory big data and the theory of mean-field, we establish an aggregated risk mean=field that contains information of all risk-spreading particles by proposing a spatio-temporal model named HiRES risk map. It has dynamic fine spatial resolution and high computation efficiency enabling fast update. We then propose an objective personal epidemic risk scoring model named HiRES-p based on HiRES risk maps, and use it to develop statistical inference based method and machine learning based method for detecting suspected epidemic-infected individuals. We conduct numerical experiments by 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 and local variability of the epidemic risk, thus can be applied to monitor epidemic risk at country, province, city and community levels, as well as at specific high-risk locations such as at hospital and station. HiRES-p score is an effective measurement of personal epidemic risk. The detection rates as de ned by successful classification are above 90 long as the population infection rate is under 20 application potential in epidemic risk prevention and control practice.
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