دورية أكاديمية
Predicting COVID-19 Cases From Atmospheric Parameters Using Machine Learning Approach.
| العنوان: | Predicting COVID-19 Cases From Atmospheric Parameters Using Machine Learning Approach. |
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| المؤلفون: | Ogunjo ST; Department of Physics Federal University of Technology Akure Akure Nigeria., Fuwape IA; Department of Physics Federal University of Technology Akure Akure Nigeria.; Office of the Vice Chancellor Michael and Cecilia Ibru University Ughelli Nigeria., Rabiu AB; Centre for Atmospheric Research National Space and Research Development Agency Anyigba Nigeria. |
| المصدر: | GeoHealth [Geohealth] 2022 Apr 01; Vol. 6 (4), pp. e2021GH000509. Date of Electronic Publication: 2022 Apr 01 (Print Publication: 2022). |
| نوع المنشور: | Journal Article |
| اللغة: | English |
| بيانات الدورية: | Publisher: Wiley Periodicals, Inc Country of Publication: United States NLM ID: 101706476 Publication Model: eCollection Cited Medium: Internet ISSN: 2471-1403 (Electronic) Linking ISSN: 24711403 NLM ISO Abbreviation: Geohealth Subsets: PubMed not MEDLINE |
| أسماء مطبوعة: | Original Publication: Hoboken, NJ : Wiley Periodicals, Inc., [2017]- |
| مستخلص: | 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. Competing Interests: The authors declare no conflicts of interest relevant to this study. (© 2022 The Authors. GeoHealth published by Wiley Periodicals LLC on behalf of American Geophysical Union.) |
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| فهرسة مساهمة: | Keywords: COVID‐19; deep learning; machine learning; pandemic |
| تواريخ الأحداث: | Date Created: 20220413 Latest Revision: 20240826 |
| رمز التحديث: | 20240826 |
| مُعرف محوري في PubMed: | PMC8983058 |
| DOI: | 10.1029/2021GH000509 |
| PMID: | 35415381 |
| قاعدة البيانات: | MEDLINE |
Full Text Finder | تدمد: | 2471-1403 |
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| DOI: | 10.1029/2021GH000509 |