Short-Term Forecasts of COVID-19 Spread Across Indian States Until 1 May 2020

The very first case of corona-virus illness was recorded on 30 January 2020, in India and the number of infected cases, including the death toll, continues to rise. In this paper, we present short-term forecasts of COVID-19 for 28 Indian states and five union territories using real-time data from 30 January to 21 April 2020. Applying Holt's second-order exponential smoothing method and autoregressive integrated moving average (ARIMA) model, we generate 10-day ahead forecasts of the likely number of infected cases and deaths in India for 22 April to 1 May 2020. Our results show that the number of cumulative cases in India will rise to 36335.63 [PI 95% (30884.56, 42918.87)], concurrently the number of deaths may increase to 1099.38 [PI 95% (959.77, 1553.76)] by 1 May 2020. Further, we have divided the country into severity zones based on the cumulative cases. According to this analysis, Maharashtra is likely to be the most affected states with around 9787.24 [PI 95% (6949.81, 13757.06)] cumulative cases by 1 May 2020. However, Kerala and Karnataka are likely to shift from the red zone (i.e. highly affected) to the lesser affected region. On the other hand, Gujarat and Madhya Pradesh will move to the red zone. These results mark the states where lockdown by 3 May 2020, can be loosened.

[1]  Pierre Magal,et al.  Predicting the cumulative number of cases for the COVID-19 epidemic in China from early data , 2020, medRxiv.

[2]  Rajan Gupta,et al.  Trend Analysis and Forecasting of COVID-19 outbreak in India , 2020, medRxiv.

[3]  Aboul Ella Hassanien,et al.  DAY LEVEL FORECASTING FOR CORONAVIRUS DISEASE (COVID-19) , 2020, International Journal of Healthcare Information Systems and Informatics.

[4]  P. Phillips,et al.  Testing the null hypothesis of stationarity against the alternative of a unit root: How sure are we that economic time series have a unit root? , 1992 .

[5]  R. Adhikari,et al.  Age-structured impact of social distancing on the COVID-19 epidemic in India , 2020, 2003.12055.

[6]  D. Dickey,et al.  Testing for unit roots in autoregressive-moving average models of unknown order , 1984 .

[7]  Weizhong Yang,et al.  Forecasting incidence of hemorrhagic fever with renal syndrome in China using ARIMA model , 2011, BMC infectious diseases.

[8]  J. M. García,et al.  A statistical analysis of the seasonality in pulmonary tuberculosis , 2000, European Journal of Epidemiology.

[9]  P. Young,et al.  Time series analysis, forecasting and control , 1972, IEEE Transactions on Automatic Control.

[10]  Asep K. Supriatna,et al.  Application of Holt exponential smoothing and ARIMA method for data population in West Java , 2017 .

[11]  Marta Giovanetti,et al.  Application of the ARIMA model on the COVID-2019 epidemic dataset , 2020, Data in Brief.

[12]  Gwilym M. Jenkins,et al.  Time series analysis, forecasting and control , 1971 .

[13]  J. Hyman,et al.  Short-term Forecasts of the COVID-19 Epidemic in Guangdong and Zhejiang, China: February 13–23, 2020 , 2020, Journal of clinical medicine.

[14]  Stanley Jere,et al.  Forecasting Inflation Rate of Zambia Using Holt’s Exponential Smoothing , 2016 .

[15]  K. Hadri.  Testing The Null Hypothesis Of Stationarity Against The Alternative Of A Unit Root In Panel Data With Serially Correlated Errors , 1999 .

[16]  J. Hyman,et al.  Real-time forecasts of the 2019-nCoV epidemic in China from February 5th to February 24th, 2020 , 2020, 2002.05069.

[17]  Mullica Jaroensutasinee,et al.  Development of temporal modeling for prediction of dengue infection in northeastern Thailand. , 2012, Asian Pacific journal of tropical medicine.

[18]  H. Cui,et al.  Comparison of the Ability of ARIMA, WNN and SVM Models for Drought Forecasting in the Sanjiang Plain, China , 2019, Natural Resources Research.