Covid-19 Case Prediction using Nesting Fitting

Abstract

In this project, a new method of predicting COVID-19 cases is built based on the Logistic model. The new method combined different fitting methods and build a new method that can predict the cases of COVID-19 in a short period. The Nesting Fitting Method is focusing on a temporal series dataset for the virus cases, and it is much better than logistic and sliding windows methods. This model considers more parameters such as region and secondary outbreak. We also compared the prediction of total confirmed cases of COVID-19 in the world with three other methods. The results showed that the prediction using the Nesting Fitting Method is precise and should be suitable for the region where a second outbreak has happened.