Statistical Modeling for the Prediction of Infectious Disease Dissemination With Special Reference to COVID-19 Spread - Yadav, Subhash Kumar; Akhter, Yusuf (Frontiers in Public Health, 2021)
In this review, we have discussed the different statistical modeling and prediction techniques for various infectious diseases including the recent pandemic of COVID-19. The distribution fitting, time series modeling along with the predictive monitoring approaches and epidemiological modeling were illustrated. When the epidemiology data is sufficient to fit with the required sample size, the normal distribution in general or other theoretical distributions are fitted and best fitted distribution is chosen for the prediction of spread of the disease. As the infectious diseases, develop over time and we have data on the single variable that is number of infections happened, therefore, time series models are fitted and the prediction is done on the basis of the best fitted model. Monitoring approaches may be also applied to time series models which could estimate the parameters more precisely. In epidemiological modeling, more biological parameters are incorporated in the models and the forecasting of the disease spread is carried out. Ultimately, we came up with, how to improve the existing modeling methods, the use of fuzzy variables and detection of fraud in the provided data. Ultimately, we have reviewed the results of recent statistical modeling efforts to predict the course of COVID-19 spread.