Continuous learning and inference of individual probability of SARS-CoV-2 infection based on interaction data

Abstract This study presents a new approach to determine the likelihood of asymptomatic carriers of the SARS-CoV-2 virus by using interaction-based continuous learning and inference of individual probability (CLIIP) for contagious ranking. This approach is developed based on an individual directed g...

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Autores principales:	Shangching Liu, Koyun Liu, Hwaihai Chiang, Jianwei Zhang, Tsungyao Chang
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/a917d58029a44f128e3e181c6d1f4bdf
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spelling	oai:doaj.org-article:a917d58029a44f128e3e181c6d1f4bdf2021-12-02T14:16:17ZContinuous learning and inference of individual probability of SARS-CoV-2 infection based on interaction data10.1038/s41598-021-81809-02045-2322https://doaj.org/article/a917d58029a44f128e3e181c6d1f4bdf2021-01-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-81809-0https://doaj.org/toc/2045-2322Abstract This study presents a new approach to determine the likelihood of asymptomatic carriers of the SARS-CoV-2 virus by using interaction-based continuous learning and inference of individual probability (CLIIP) for contagious ranking. This approach is developed based on an individual directed graph (IDG), using multi-layer bidirectional path tracking and inference searching. The IDG is determined by the appearance timeline and spatial data that can adapt over time. Additionally, the approach takes into consideration the incubation period and several features that can represent real-world circumstances, such as the number of asymptomatic carriers present. After each update of confirmed cases, the model collects the interaction features and infers the individual person’s probability of getting infected using the status of the surrounding people. The CLIIP approach is validated using the individualized bidirectional SEIR model to simulate the contagion process. Compared to traditional contact tracing methods, our approach significantly reduces the screening and quarantine required to search for the potential asymptomatic virus carriers by as much as 94%.Shangching LiuKoyun LiuHwaihai ChiangJianwei ZhangTsungyao ChangNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-10 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Shangching Liu Koyun Liu Hwaihai Chiang Jianwei Zhang Tsungyao Chang Continuous learning and inference of individual probability of SARS-CoV-2 infection based on interaction data
description	Abstract This study presents a new approach to determine the likelihood of asymptomatic carriers of the SARS-CoV-2 virus by using interaction-based continuous learning and inference of individual probability (CLIIP) for contagious ranking. This approach is developed based on an individual directed graph (IDG), using multi-layer bidirectional path tracking and inference searching. The IDG is determined by the appearance timeline and spatial data that can adapt over time. Additionally, the approach takes into consideration the incubation period and several features that can represent real-world circumstances, such as the number of asymptomatic carriers present. After each update of confirmed cases, the model collects the interaction features and infers the individual person’s probability of getting infected using the status of the surrounding people. The CLIIP approach is validated using the individualized bidirectional SEIR model to simulate the contagion process. Compared to traditional contact tracing methods, our approach significantly reduces the screening and quarantine required to search for the potential asymptomatic virus carriers by as much as 94%.
format	article
author	Shangching Liu Koyun Liu Hwaihai Chiang Jianwei Zhang Tsungyao Chang
author_facet	Shangching Liu Koyun Liu Hwaihai Chiang Jianwei Zhang Tsungyao Chang
author_sort	Shangching Liu
title	Continuous learning and inference of individual probability of SARS-CoV-2 infection based on interaction data
title_short	Continuous learning and inference of individual probability of SARS-CoV-2 infection based on interaction data
title_full	Continuous learning and inference of individual probability of SARS-CoV-2 infection based on interaction data
title_fullStr	Continuous learning and inference of individual probability of SARS-CoV-2 infection based on interaction data
title_full_unstemmed	Continuous learning and inference of individual probability of SARS-CoV-2 infection based on interaction data
title_sort	continuous learning and inference of individual probability of sars-cov-2 infection based on interaction data
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/a917d58029a44f128e3e181c6d1f4bdf
work_keys_str_mv	AT shangchingliu continuouslearningandinferenceofindividualprobabilityofsarscov2infectionbasedoninteractiondata AT koyunliu continuouslearningandinferenceofindividualprobabilityofsarscov2infectionbasedoninteractiondata AT hwaihaichiang continuouslearningandinferenceofindividualprobabilityofsarscov2infectionbasedoninteractiondata AT jianweizhang continuouslearningandinferenceofindividualprobabilityofsarscov2infectionbasedoninteractiondata AT tsungyaochang continuouslearningandinferenceofindividualprobabilityofsarscov2infectionbasedoninteractiondata
_version_	1718391684683792384

Continuous learning and inference of individual probability of SARS-CoV-2 infection based on interaction data

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