Wavelet-based Statistical and Mathematical Analysis of Spread of COVID-19

Samreen Fatima, Mehwish Shafi Khan, Yumna Sajid


The outbreak of coronavirus-19 (NCoV-19) has developed a universal crisis due to high rate of infection and mortality. Therefore, the researchers are using various available methods to study the pattern of spread of COVID-19 which will help in planning to control the disease and to manage the health care resources. This study compares Autoregressive Integrated Moving Average (ARIMA) (statistical), Logistic, Gompertz (mathematical) and their hybrid using Wavelet-based Forecast (WBF) models to model and predict the number of confirmed cases of COVID-19. The study area includes the countries: Iran, Italy, Pakistan, Saudi Arabia, USA, UK and Canada. Moreover, root mean squares error (RMSE) is used to compare the performance of studied models. Empirical analysis shows that confirmed cases could be adequately modelled using ARIMA and ARIMA-WBF for all the countries under consideration. However, for future prediction significance of the models varies region to region.

Keywords: COVID-19; ARIMA; Logistic; Gompertz; Wavelet-based-forecast 

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