EXTENSION OF A FOREST-FIRE MODEL TO SIMULATE HOW COVID-19 VIRUS CAUSES OSCILLATORY POPULATION DYNAMICS

Abstract

The global spread of infectious diseases and their associated social impacts have been proved to cause havoc to humans with the recent pandemic invasion of COVID-19. Understanding the spread pattern of COVID-19 and predicting the disease dynamics have been inevitable to support government in the public and private health fields in setting up strategies for alleviating the dire seriousness of the pandemic around the World. The spread of infectious disease and the resulting population dynamics could be modeled and simulated by extending the classic "forest-fire" model with an introduction of 3 model parameters of p, q, r that control the probability of infection, immunity, and spread. Then, by applying the mathematical model of damped harmonic oscillation from physics, the effect of these parameters on the amplitude and frequency of oscillations of an epidemic could be characterized. We concluded that this model could predict a wide range of behaviors including Covid-10 that could aid epidemiologists and policymakers. Further study might be required for practical application.