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• W4220655672 abstract "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." @default.
• W4220655672 created "2022-04-03" @default.
• W4220655672 creator A5011024223 @default.
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• W4220655672 date "2022-04-01" @default.
• W4220655672 modified "2023-09-30" @default.
• W4220655672 title "Predicting COVID‐19 Cases From Atmospheric Parameters Using Machine Learning Approach" @default.
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• W4220655672 doi "https://doi.org/10.1029/2021gh000509" @default.
• W4220655672 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/35415381" @default.
• W4220655672 hasPublicationYear "2022" @default.
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