tetano
Editor, Senior Moderator
Int Health
. 2021 Jun 5;ihab031.
doi: 10.1093/inthealth/ihab031. Online ahead of print.
Short-term forecasting of the COVID-19 outbreak in India
Sherry Mangla[SUP] 1 [/SUP], Ashok Kumar Pathak[SUP] 1 [/SUP], Mohd Arshad[SUP] 2 3 [/SUP], Ubydul Haque[SUP] 4 [/SUP]
Affiliations
Abstract
As the outbreak of coronavirus disease 2019 (COVID-19) is rapidly spreading in different parts of India, a reliable forecast for the cumulative confirmed cases and the number of deaths can be helpful for policymakers in making the decisions for utilizing available resources in the country. Recently, various mathematical models have been used to predict the outbreak of COVID-19 worldwide and also in India. In this article we use exponential, logistic, Gompertz growth and autoregressive integrated moving average (ARIMA) models to predict the spread of COVID-19 in India after the announcement of various unlock phases. The mean absolute percentage error and root mean square error comparative measures were used to check the goodness-of-fit of the growth models and Akaike information criterion for ARIMA model selection. Using COVID-19 pandemic data up to 20 December 2020 from India and its five most affected states (Maharashtra, Karnataka, Andhra Pradesh, Tamil Nadu and Kerala), we report 15-days-ahead forecasts for cumulative confirmed cases and the number of deaths. Based on available data, we found that the ARIMA model is the best-fitting model for COVID-19 cases in India and its most affected states.
Keywords: ARIMA; COVID-19; forecasting; logistic growth model.
. 2021 Jun 5;ihab031.
doi: 10.1093/inthealth/ihab031. Online ahead of print.
Short-term forecasting of the COVID-19 outbreak in India
Sherry Mangla[SUP] 1 [/SUP], Ashok Kumar Pathak[SUP] 1 [/SUP], Mohd Arshad[SUP] 2 3 [/SUP], Ubydul Haque[SUP] 4 [/SUP]
Affiliations
- PMID: 34091670
- DOI: 10.1093/inthealth/ihab031
Abstract
As the outbreak of coronavirus disease 2019 (COVID-19) is rapidly spreading in different parts of India, a reliable forecast for the cumulative confirmed cases and the number of deaths can be helpful for policymakers in making the decisions for utilizing available resources in the country. Recently, various mathematical models have been used to predict the outbreak of COVID-19 worldwide and also in India. In this article we use exponential, logistic, Gompertz growth and autoregressive integrated moving average (ARIMA) models to predict the spread of COVID-19 in India after the announcement of various unlock phases. The mean absolute percentage error and root mean square error comparative measures were used to check the goodness-of-fit of the growth models and Akaike information criterion for ARIMA model selection. Using COVID-19 pandemic data up to 20 December 2020 from India and its five most affected states (Maharashtra, Karnataka, Andhra Pradesh, Tamil Nadu and Kerala), we report 15-days-ahead forecasts for cumulative confirmed cases and the number of deaths. Based on available data, we found that the ARIMA model is the best-fitting model for COVID-19 cases in India and its most affected states.
Keywords: ARIMA; COVID-19; forecasting; logistic growth model.