Unknown

Dataset Information

0

Identify hidden spreaders of pandemic over contact tracing networks.


ABSTRACT: 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.

SUBMITTER: Huang S 

PROVIDER: S-EPMC10356757 | biostudies-literature | 2023 Jul

REPOSITORIES: biostudies-literature

altmetric image

Publications

Identify hidden spreaders of pandemic over contact tracing networks.

Huang Shuhong S   Sun Jiachen J   Feng Ling L   Xie Jiarong J   Wang Dashun D   Hu Yanqing Y  

Scientific reports 20230719 1


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  ...[more]

Similar Datasets

| S-EPMC7955065 | biostudies-literature
| S-EPMC7814176 | biostudies-literature
| S-EPMC1560336 | biostudies-literature
| S-EPMC8340850 | biostudies-literature
| S-EPMC7997996 | biostudies-literature
| S-EPMC6055149 | biostudies-literature
| S-EPMC9700234 | biostudies-literature
| S-EPMC7536383 | biostudies-literature
| S-EPMC1618487 | biostudies-literature
| S-EPMC9931320 | biostudies-literature