The Role of Predictive Models in Shaping Health-care Policies during COVID-19 Pandemics: Lessons Learned and Future Directions

Abstract

Abstract COVID-19 has substantially impacted health systems and the global economy. Moreover, it is more likely to have a detrimental influence on unstable nations than on more stable ones. All these consequences encouraged investigators to develop mathematical models to predict new cases, fatalities, and recoveries. These models allow a better understanding of the reasons and mechanisms of infection spread and preventive methods. In addition, these models help us understand the infection’s origin, mode of transmission, and the impact of national responses on these variables. On the other hand, the efficacy and accuracy of these models during the COVID-19 pandemic are questionable. This review highlights several types of predictive models for forecasting the transmission of infectious diseases. Despite the crucial role of mathematical models in understanding outbreaks, most models fail because of the misunderstanding of their assumptions or the misuse of the best model for the targeted scenario.