@Article{CSIAM-AM-1-186,
author = {Zhou , ChuansaiYuan , WenWang , JunXu , HaiyongJiang , YongWang , XinminHan Wen , Qiuzi and Zhang , Pingwen},
title = {Detecting Suspected Epidemic Cases Using Trajectory Big Data},
journal = {CSIAM Transactions on Applied Mathematics},
year = {2020},
volume = {1},
number = {1},
pages = {186--206},
abstract = {<p style="text-align: justify;">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.</p>},
issn = {2708-0579},
doi = {https://doi.org/10.4208/csiam-am.2020-0006},
url = {http://global-sci.org/intro/article_detail/csiam-am/16799.html}
}