PREDICTION OF COVID-19 CASES IN NEPAL USING THE COMBINATION OF EPIDEMIOLOGICAL AND TIME SERIES MODELS

Abstract

This work analyze the official data of coronavirus (Infected, Recovered and Death) and predict the evolution of the epidemic in Nepal. The generalized SEIR model has been applied with hybrid of ETS-ARIMA time series model for the time series analysis and predictions of evolution of Covid-19 cases (Quarantined, Recovered and Deaths). The prediction has been made for 30 days using the past data of thirteen months. The prediction made by generalized SEIR model has been corrected using two time series models, ETS and ARIMA model. The estimation error of generalized SEIR model is fed to ETS model to predict the error. Then, the predicted error by ETS model is added to the prediction made by generalized SEIR model. Now, the remaining error is again fed to ARIMA model to predict the error. The predicted error by ARIMA model is added to the prediction made by generalized SEIR model to get final prediction. Use of generalized SEIR model along with ETS and ARIMA model improve the time series prediction of coronavirus spread in case of Nepal as compared to generalized SEIR model. Also, the SEIR-ETS-ARIMA model reduce the estimation error as compared to SEIRD-ARIMA model. Improvement in all quality measures, MAE, MSE, RMSE and MAPE, has been observed.