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 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. Plain Language Summary The world is racing to combat the COVID‐19 pandemic. Predicting the number of cases within a location is important in curtailing the spread of the virus. In this study, we used machine learning to predict future infections based on past infection cases and atmospheric predictors. Our results showed that the approach is effective in predicting the COVID‐19 cases within the locations considered. Key Points This study explored the potential of predicting COVID‐19 cases using various machine learning algorithms Different machine learning algorithms suggests different lags for efficient prediction Temperature was found to be the best predictor for COVID‐19 cases within the study locations