Modeling the COVID-19 Outbreak in China through Multi-source Information Fusion

Modeling the outbreak of a novel epidemic, such as coronavirus disease 2019 (COVID-19), is crucial for estimating its dynamics, predicting future spread and evaluating the effects of different interventions. However, there are three issues that make this modeling a challenging task: uncertainty in d...

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Detalles Bibliográficos
Autores principales: Wu, Lin, Wang, Lizhe, Li, Nan, Sun, Tao, Qian, Tangwen, Jiang, Yu, Wang, Fei, Xu, Yongjun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2020
Materias:
Commentary
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7409870/
https://www.ncbi.nlm.nih.gov/pubmed/32914143
http://dx.doi.org/10.1016/j.xinn.2020.100033
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author Wu, Lin
Wang, Lizhe
Li, Nan
Sun, Tao
Qian, Tangwen
Jiang, Yu
Wang, Fei
Xu, Yongjun
author_facet Wu, Lin
Wang, Lizhe
Li, Nan
Sun, Tao
Qian, Tangwen
Jiang, Yu
Wang, Fei
Xu, Yongjun
author_sort Wu, Lin
collection PubMed
description Modeling the outbreak of a novel epidemic, such as coronavirus disease 2019 (COVID-19), is crucial for estimating its dynamics, predicting future spread and evaluating the effects of different interventions. However, there are three issues that make this modeling a challenging task: uncertainty in data, roughness in models, and complexity in programming. We addressed these issues by presenting an interactive individual-based simulator, which is capable of modeling an epidemic through multi-source information fusion.
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spelling pubmed-74098702020-08-07 Modeling the COVID-19 Outbreak in China through Multi-source Information Fusion Wu, Lin Wang, Lizhe Li, Nan Sun, Tao Qian, Tangwen Jiang, Yu Wang, Fei Xu, Yongjun Innovation (Camb) Commentary Modeling the outbreak of a novel epidemic, such as coronavirus disease 2019 (COVID-19), is crucial for estimating its dynamics, predicting future spread and evaluating the effects of different interventions. However, there are three issues that make this modeling a challenging task: uncertainty in data, roughness in models, and complexity in programming. We addressed these issues by presenting an interactive individual-based simulator, which is capable of modeling an epidemic through multi-source information fusion. Elsevier 2020-08-06 /pmc/articles/PMC7409870/ /pubmed/32914143 http://dx.doi.org/10.1016/j.xinn.2020.100033 Text en © 2020 The Author(s) https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Commentary
Wu, Lin
Wang, Lizhe
Li, Nan
Sun, Tao
Qian, Tangwen
Jiang, Yu
Wang, Fei
Xu, Yongjun
Modeling the COVID-19 Outbreak in China through Multi-source Information Fusion
title Modeling the COVID-19 Outbreak in China through Multi-source Information Fusion
title_full Modeling the COVID-19 Outbreak in China through Multi-source Information Fusion
title_fullStr Modeling the COVID-19 Outbreak in China through Multi-source Information Fusion
title_full_unstemmed Modeling the COVID-19 Outbreak in China through Multi-source Information Fusion
title_short Modeling the COVID-19 Outbreak in China through Multi-source Information Fusion
title_sort modeling the covid-19 outbreak in china through multi-source information fusion
topic Commentary
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7409870/
https://www.ncbi.nlm.nih.gov/pubmed/32914143
http://dx.doi.org/10.1016/j.xinn.2020.100033
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