ABSTRACT
Using mathematical models to describe the dynamics of infectious-diseases transmission in large communities can help epidemiological scientists to understand different factors affecting epidemics as well as health authorities to decide measures effective for infection prevention. In this study, we use a discrete version of the Generalized Logistic Model (GLM) to describe the spread of the coronavirus disease 2019 (COVID-19) pandemic in Saudi Arabia. We assume that we are operating in discrete time so that the model is represented by a first-order difference equation, unlike time-continuous models, which employ differential equations. Using this model, we forecast COVID-19 spread in Saudi Arabia and we show that the short-term predicted number of cumulative cases is in agreement with the confirmed reports. © 2022
ABSTRACT
Mathematical modeling can be a powerful tool to predict disease spread in large populations as well as to understand different factors which can impact it such as social distancing and vaccinations. This study aimed to describe the spread the coronavirus disease 2019 (COVID-19) pandemic in Saudi Arabia using a simple discrete variant of the Gompertz model. Unlike time-continuous models which are based on differential equations, this model treats time as a discrete variable and is then represented by a first-order difference equation. Using this model, we performed a short-term prediction of the number of cumulative cases of COVID-19 in the country and we show that the results match the confirmed reports. © 2022 Fractals.