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Predicting COVID‐19 Cases From Atmospheric Parameters Using Machine Learning Approach
The dynamical nature of COVID‐19 cases in different parts of the world requires robust mathematical approaches for prediction and forecasting. In this study, we aim to (a) forecast future COVID‐19 cases based on past infections, (b) predict current COVID‐19 cases using PM2.5, temperature, and humidi...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8983058/ https://www.ncbi.nlm.nih.gov/pubmed/35415381 http://dx.doi.org/10.1029/2021GH000509 |
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author | Ogunjo, S. T. Fuwape, I. A. Rabiu, A. B. |
author_facet | Ogunjo, S. T. Fuwape, I. A. Rabiu, A. B. |
author_sort | Ogunjo, S. T. |
collection | PubMed |
description | The dynamical nature of COVID‐19 cases in different parts of the world requires robust mathematical approaches for prediction and forecasting. In this study, we aim to (a) forecast future COVID‐19 cases based on past infections, (b) predict current COVID‐19 cases using PM2.5, temperature, and humidity data, using four different machine learning classifiers (Decision Tree, K‐nearest neighbor, Support Vector Machine, and Random Forest). Based on RMSE values, k‐nearest neighbor and support vector machine algorithms were found to be the best for predicting future incidences of COVID‐19 based on past histories. From the RMSE values obtained, temperature was found to be the best predictor for number of COVID‐19 cases, followed by relative humidity. Decision tree models was found to perform poorly in the prediction of COVID‐19 cases considering particulate matter and atmospheric parameters as predictors. Our results suggests the possibility of predicting virus infection using machine learning. This will guide policy makers in proactive monitoring and control. |
format | Online Article Text |
id | pubmed-8983058 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | John Wiley and Sons Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-89830582022-04-11 Predicting COVID‐19 Cases From Atmospheric Parameters Using Machine Learning Approach Ogunjo, S. T. Fuwape, I. A. Rabiu, A. B. Geohealth Research Article The dynamical nature of COVID‐19 cases in different parts of the world requires robust mathematical approaches for prediction and forecasting. In this study, we aim to (a) forecast future COVID‐19 cases based on past infections, (b) predict current COVID‐19 cases using PM2.5, temperature, and humidity data, using four different machine learning classifiers (Decision Tree, K‐nearest neighbor, Support Vector Machine, and Random Forest). Based on RMSE values, k‐nearest neighbor and support vector machine algorithms were found to be the best for predicting future incidences of COVID‐19 based on past histories. From the RMSE values obtained, temperature was found to be the best predictor for number of COVID‐19 cases, followed by relative humidity. Decision tree models was found to perform poorly in the prediction of COVID‐19 cases considering particulate matter and atmospheric parameters as predictors. Our results suggests the possibility of predicting virus infection using machine learning. This will guide policy makers in proactive monitoring and control. John Wiley and Sons Inc. 2022-04-01 /pmc/articles/PMC8983058/ /pubmed/35415381 http://dx.doi.org/10.1029/2021GH000509 Text en © 2022 The Authors. GeoHealth published by Wiley Periodicals LLC on behalf of American Geophysical Union. https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc-nd/4.0/ (https://creativecommons.org/licenses/by-nc-nd/4.0/) License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made. |
spellingShingle | Research Article Ogunjo, S. T. Fuwape, I. A. Rabiu, A. B. Predicting COVID‐19 Cases From Atmospheric Parameters Using Machine Learning Approach |
title | Predicting COVID‐19 Cases From Atmospheric Parameters Using Machine Learning Approach |
title_full | Predicting COVID‐19 Cases From Atmospheric Parameters Using Machine Learning Approach |
title_fullStr | Predicting COVID‐19 Cases From Atmospheric Parameters Using Machine Learning Approach |
title_full_unstemmed | Predicting COVID‐19 Cases From Atmospheric Parameters Using Machine Learning Approach |
title_short | Predicting COVID‐19 Cases From Atmospheric Parameters Using Machine Learning Approach |
title_sort | predicting covid‐19 cases from atmospheric parameters using machine learning approach |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8983058/ https://www.ncbi.nlm.nih.gov/pubmed/35415381 http://dx.doi.org/10.1029/2021GH000509 |
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