Volume 1, Issue 1
Detecting Suspected Epidemic Cases Using Trajectory Big Data

Chuansai Zhou, Wen Yuan, Jun Wang, Haiyong Xu, Yong Jiang, Xinmin Wang, Qiuzi Han Wen & Pingwen Zhang

CSIAM Trans. Appl. Math., 1 (2020), pp. 186-206.

Published online: 2020-03

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  • Abstract

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.

  • Keywords

Trajectory big data, spatio-temporal modeling, machine learning, suspected case detection, epidemic risk prevention and control.

  • AMS Subject Headings

62, 92

  • Copyright

COPYRIGHT: © Global Science Press

  • Email address

chuansai@pku.edu.cn (Chuansai Zhou)

yuanwen9510@pku.edu.cn (Wen Yuan)

jwwwang@pku.edu.cn (Jun Wang)

xuhaiyong@chinamobile.com (Haiyong Xu)

jiangyong@chinamobile.com (Yong Jiang)

qiuzi.wh@pku.edu.cn (Qiuzi Han Wen)

pzhang@pku.edu.cn (Pingwen Zhang)

  • BibTex
  • RIS
  • TXT
@Article{CSIAM-AM-1-186, author = {Zhou , Chuansai and Yuan , Wen and Wang , Jun and Xu , Haiyong and Jiang , Yong and Wang , Xinmin and Han 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 = {

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.

}, 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} }
TY - JOUR T1 - Detecting Suspected Epidemic Cases Using Trajectory Big Data AU - Zhou , Chuansai AU - Yuan , Wen AU - Wang , Jun AU - Xu , Haiyong AU - Jiang , Yong AU - Wang , Xinmin AU - Han Wen , Qiuzi AU - Zhang , Pingwen JO - CSIAM Transactions on Applied Mathematics VL - 1 SP - 186 EP - 206 PY - 2020 DA - 2020/03 SN - 1 DO - http://dor.org/10.4208/csiam-am.2020-0006 UR - https://global-sci.org/intro/article_detail/csiam-am/16799.html KW - Trajectory big data, spatio-temporal modeling, machine learning, suspected case detection, epidemic risk prevention and control. AB -

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.

Chuansai Zhou, Wen Yuan, Jun Wang, Haiyong Xu, Yong Jiang, Xinmin Wang, Qiuzi Han Wen & Pingwen Zhang. (2020). Detecting Suspected Epidemic Cases Using Trajectory Big Data. CSIAM Transactions on Applied Mathematics. 1 (1). 186-206. doi:10.4208/csiam-am.2020-0006
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