Advanced SIRD model for predicting epidemic propagation
Abstract
The sudden outbreak of the coronavirus disease (COVID-19) has created a public health crisis and impacted the world economy. We could contain the virus and have a low virus death rate if the cases were less than the capacity of hospitals. Hence, the forecast of the virus evolution is crucial. However, tackling the spread of this virus is very challenging. This has increased the interest in mathematical models that help health officials and governments implement measures that mitigate the spread of COVID-19. One well-known model for predicting infectious diseases is the SIRD model. Although this model is powerful, it is not entirely accurate because it does not consider many factors such as travel, vaccination, and birth rates, which affect the total number of cases. Here, we present an improved version of the SIRD (Susceptible, Infectious, Recovered, or Deceased) model. This model is a non-linear discrete dynamical system that incorporates multiple parameters to make the prediction more accurate. Furthermore, we developed an interactive code that illustrates how the model behaves with changes in the parameters. Representative plots are included in the study to prove the adaptability and effectiveness of the model. Our model provides a realistic, qualitative representation of the epidemic that may help governments and health officials take action to stop the spread of the virus.
Brief Biography
Abdullah is a high school senior interested in mathematics and mathematical modeling. He interned in the summer of 2021 at Prof. Diogo Gomes group at KAUST, where he developed advanced models that could predict the propagation of viral diseases (e. g. COVID-19 and Omicron). He is also a winner of several national awards like the 2nd grand award in the national olympiad for scientific creativity, two silvers, and a gold kangaroo medal, and a special award from ExxonMobil.