An empirical model for the infectious disease spread predictions

Abstract

Disease-spreading simulation has received significant attention in research. In general, there are two approaches to simulating a pandemic. The first one assumes a homogeneous population and uses differential equations to model the number of people in each compartment: Susceptible, Exposed, Infectious, and Recovered. The other approach reflects the population’s heterogeneity and utilizes face-to-face contact records for disease-spreading simulation. The objective of this study is to build an empirical model that follows the second approach, reflecting the dynamics of disease transmission, and then to compare it with other efficient approximating models such as network-based models. We will show that the presented empirical model only slightly enhances simulation accuracy despite its expensive time and space complexity. However, if the time step of the network-based model is inappropriately set, the disparity in simulation performance between the empirical and network-based models can be substantial. Therefore, the empirical model can serve as a benchmark to identify the optimal time step of the network-based model.