Epidemic simulation framework : design, implementation, and accracy analysis

Abstract

Large-scale decisions can significantly impact the health, security, and economic well- being of a society, making it essential to provide appropriate tools and data for informed decision-making. However, in situations of uncertainty as witnessed with COVID-19, it is prudent to utilize simulation tools that can project future scenarios and assess their effects on a population. With the computational capabilities available today, population simulations can closely mimic real-world dynamics. By setting parameters and observing their impact, decision-makers can evaluate different scenarios and assess their consequences on the population. In this study, we present an Epidemic Simulation Framework to replicate the spread of infectious diseases within a population using the Susceptible-Infectious-Recovered (SIR) model on a 2D plane, supported by a software application tool. This tool serves as a valuable resource for researchers, policymakers, and the general public by allowing them to create and manipulate populations with varying sizes and characteristics, incorporating parameters such as vaccination, quarantine, and infection rates. By utilizing this tool, users can proficiently introduce infectious individuals and closely monitor the subsequent dynamics of disease spread. The tool not only offers real-time data concerning the distribution of individuals across different disease stages but also presents informative graphs and charts that vividly depict the progression of the epidemic. To evaluate the accuracy of this framework, we gathered authentic data on the dissemination of COVID-19 in Algeria. By comparing this data with the simulation results generated by the tool, we observed a noteworthy correlation between the two. This substantiates a strong correspondence between the simulation outcomes and the actual advancement of the disease.