  Emerging infectious diseases are existential threats to human health and
global stability. The recent outbreaks of the novel coronavirus COVID-19 have
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 provided by the big data and
the mean field theory, 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
individual epidemic risk scoring model named HiRES-p based on HiRES risk maps,
and use it to develop statistical inference and machine learning methods 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 hospital and
station. HiRES-p score seems to be an effective measurement of personal
epidemic risk. The accuracy of both detecting methods are above 90\% when the
population infection rate is under 20\%, which indicates great application
potential in epidemic risk prevention and control practice.
