Identify hidden spreaders of pandemic over contact tracing networks
The COVID-19 infection cases have surged globally, causing devastations to both the society and economy. A key factor contributing to the sustained spreading is the presence of a large number of asymptomatic or hidden spreaders, who mix among the susceptible population without being detected or quarantined. Due to the continuous emergence of new virus variants, even if vaccines have been widely used, the detection of asymptomatic infected persons is still important in the epidemic control. Based on the unique characteristics of COVID-19 spreading dynamics, here we propose a theoretical framework capturing the transition probabilities among different infectious states in a network, and extend it to an efficient algorithm to identify asymptotic individuals. We find that using pure physical spreading equations, the hidden spreaders of COVID-19 can be identified with remarkable accuracy, even with incomplete information of the contract-tracing networks. Furthermore, our framework can be useful for other epidemic diseases that also feature asymptomatic spreading.
National Natural Science Foundation of China ; Natural Science Foundation of Guangdong for Distinguished Youth Scholar, Guangdong Provincial Department of Science and Technology[2020B1515020052] ; Guangdong High-Level Personnel of Special Support Program, Young Top Notch Talents in Technological Innovation[2019TQ05X138] ; NUS AcRF[A-0004550-00-00]
|WOS Research Area|
Science & Technology - Other Topics
|WOS Accession No|
Cited Times [WOS]:0
|Document Type||Journal Article|
|Department||Department of Statistics and Data Science|
1.School of Data and Computer Science,Sun Yat-sen University,Guangzhou,510006,China
3.Institute of High Performance Computing,Agency for Science,Technology and Research (A*STAR),Singapore,138632,Singapore
4.Department of Physics,National University of Singapore,Singapore,117551,Singapore
5.Kellogg School of Management,Northwestern University,Evanston,United States
6.Department of Statistics and Data Science,College of Science,Southern University of Science and Technology,Shenzhen,518055,China
7.Institute of Neuroscience,Technical University of Munich,Munich,80802,Germany
|Corresponding Author Affilication||Department of Statistics and Data Science; College of Science|
Huang，Shuhong,Sun，Jiachen,Feng，Ling,et al. Identify hidden spreaders of pandemic over contact tracing networks[J]. Scientific Reports,2023,13(1).
Huang，Shuhong,Sun，Jiachen,Feng，Ling,Xie，Jiarong,Wang，Dashun,&Hu，Yanqing.(2023).Identify hidden spreaders of pandemic over contact tracing networks.Scientific Reports,13(1).
Huang，Shuhong,et al."Identify hidden spreaders of pandemic over contact tracing networks".Scientific Reports 13.1(2023).
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