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COVID-19 Epidemic Analysis in India with Multi-Source State-Level Datasets

The COVID-19 pandemic has been a global crisis affecting billions of people and causing countless economic losses. Different approaches have been proposed for combating this crisis, including both medical measures and technical innovations, e.g., artificial intelligence technologies to diagnose and...

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Detalles Bibliográficos
Autor principal: Wang, Qirui
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9039780/
https://www.ncbi.nlm.nih.gov/pubmed/35496053
http://dx.doi.org/10.1155/2022/2601149
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author Wang, Qirui
author_facet Wang, Qirui
author_sort Wang, Qirui
collection PubMed
description The COVID-19 pandemic has been a global crisis affecting billions of people and causing countless economic losses. Different approaches have been proposed for combating this crisis, including both medical measures and technical innovations, e.g., artificial intelligence technologies to diagnose and predict COVID-19 cases. While there is much attention being paid to the USA and China, little research attention has been drawn to less developed countries, e.g., India. In this study, I conduct an analysis of the COVID-19 epidemic in India, with datasets collected from different sources. Several machine learning models have been built to predict the COVID-19 spread, with different combinations of input features, in which the Transformer is proven as the most precise one. I also find that the Facebook mobility dataset is the most useful for predicting the number of confirmed cases. However, I find that the datasets from different sources are not very effective when predicting the number of deaths caused by the COVID-19 infection.
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spelling pubmed-90397802022-04-27 COVID-19 Epidemic Analysis in India with Multi-Source State-Level Datasets Wang, Qirui Biomed Res Int Research Article The COVID-19 pandemic has been a global crisis affecting billions of people and causing countless economic losses. Different approaches have been proposed for combating this crisis, including both medical measures and technical innovations, e.g., artificial intelligence technologies to diagnose and predict COVID-19 cases. While there is much attention being paid to the USA and China, little research attention has been drawn to less developed countries, e.g., India. In this study, I conduct an analysis of the COVID-19 epidemic in India, with datasets collected from different sources. Several machine learning models have been built to predict the COVID-19 spread, with different combinations of input features, in which the Transformer is proven as the most precise one. I also find that the Facebook mobility dataset is the most useful for predicting the number of confirmed cases. However, I find that the datasets from different sources are not very effective when predicting the number of deaths caused by the COVID-19 infection. Hindawi 2022-04-25 /pmc/articles/PMC9039780/ /pubmed/35496053 http://dx.doi.org/10.1155/2022/2601149 Text en Copyright © 2022 Qirui Wang. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Wang, Qirui
COVID-19 Epidemic Analysis in India with Multi-Source State-Level Datasets
title COVID-19 Epidemic Analysis in India with Multi-Source State-Level Datasets
title_full COVID-19 Epidemic Analysis in India with Multi-Source State-Level Datasets
title_fullStr COVID-19 Epidemic Analysis in India with Multi-Source State-Level Datasets
title_full_unstemmed COVID-19 Epidemic Analysis in India with Multi-Source State-Level Datasets
title_short COVID-19 Epidemic Analysis in India with Multi-Source State-Level Datasets
title_sort covid-19 epidemic analysis in india with multi-source state-level datasets
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9039780/
https://www.ncbi.nlm.nih.gov/pubmed/35496053
http://dx.doi.org/10.1155/2022/2601149
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