Prediction and analysis of COVID-19 daily new cases and cumulative cases: times series forecasting and machine learning models.

ABSTRACT:

Background

COVID-19 poses a severe threat to global human health, especially the USA, Brazil, and India cases continue to increase dynamically, which has a far-reaching impact on people's health, social activities, and the local economic situation.

Methods

The study proposed the ARIMA, SARIMA and Prophet models to predict daily new cases and cumulative confirmed cases in the USA, Brazil and India over the next 30 days based on the COVID-19 new confirmed cases and cumulative confirmed cases data set(May 1, 2020, and November 30, 2021) published by the official WHO, Three models were implemented in the R 4.1.1 software with forecast and prophet package. The performance of different models was evaluated by using root mean square error (RMSE), mean absolute error (MAE) and mean

SUBMITTER: Wang Y 

PROVIDER: S-EPMC9131989 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature