Abstract
Abstract As acute infectious pneumonia, the coronavirus disease-2019 (COVID-19) has created unique challenges for each nation and region. Both India and the United States (US) have experienced a second outbreak, resulting in a severe disease burden. The study aimed to develop optimal models to predict the daily new cases, in order to help to develop public health strategies. The autoregressive integrated moving average (ARIMA) models, generalised regression neural network (GRNN) models, ARIMA–GRNN hybrid model and exponential smoothing (ES) model were used to fit the daily new cases. The performances were evaluated by minimum mean absolute per cent error (MAPE). The predictive value with ARIMA (3, 1, 3) (1, 1, 1)14 model was closest to the actual value in India, while the ARIMA–GRNN presented a better performance in the US. According to the models, the number of daily new COVID-19 cases in India continued to decrease after 27 May 2021. In conclusion, the ARIMA model presented to be the best-fit model in forecasting daily COVID-19 new cases in India, and the ARIMA–GRNN hybrid model had the best prediction performance in the US. The appropriate model should be selected for different regions in predicting daily new cases. The results can shed light on understanding the trends of the outbreak and giving ideas of the epidemiological stage of these regions.
Highlights
IntroductionThe new Coronavirus Disease (COVID-19) has created unique challenges for each nation and region
As acute infectious pneumonia, the new Coronavirus Disease (COVID-19) has created unique challenges for each nation and region
The predictive value with autoregressive integrated moving average (ARIMA) (3, 1, 3) (1, 1, 1) 14 model was closest to the actual value in India, while the ARIMA-generalized regression neural network (GRNN) presented a better performance in the United States (US)
Summary
The new Coronavirus Disease (COVID-19) has created unique challenges for each nation and region. According to the latest World Health Organization (WHO) figure, as of May 30, 2021, there were a total of 169597415 confirmed COVID-19 cases worldwide, with a death toll of 3530582. With regard to US, there have been 32916501 confirmed cases, including 588292 deaths, ranking the first among all countries (https://covid19.who.int/table). It is of great significance to propose and fit a model to forecast the epidemic trend of the COVID-19 based on real-time monitoring data[2, 3]. Epidemiological time series forecasting plays an important role in disease surveillance, because it allows the managers to develop strategic planning, which helps to avoid a large scale of epidemic[5]. Many mathematical models, including regression analysis method, time series analysis method, and neural network technology, have been applied to predict the incidence of infectious diseases[6-9]. The autoregressive integrated moving average (ARIMA) model is a time series analysis method firstly proposed by Box and Jenkins in the 1970s, which works based on linear theory; The model mainly captures a linear relationship and assumes the normality of errors[10]
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