# A Networked Meta-Population Epidemic Model with Population Flow and Its Application to the Prediction of the COVID-19 Pandemic

## TL;DR

This paper introduces a new model for predicting how epidemics spread by considering asymptomatic individuals and population movements, and shows how controlling movement can reduce infections.

## Contribution

A joint estimation framework for simultaneously identifying network structure and epidemic parameters in a networked SAIR model with population flow.

## Key findings

- The joint estimation framework reduces estimation error compared to separate identification methods.
- Population flow control strategies effectively reduce the number of infections during an epidemic.
- The model's sensitivity to population movements is evaluated through the basic reproduction number.

## Abstract

This article addresses the crucial issues of how asymptomatic individuals and population movements influence the spread of epidemics. Specifically, a discrete-time networked Susceptible-Asymptomatic-Infected-Recovered (SAIR) model that integrates population flow is introduced to investigate the dynamics of epidemic transmission among individuals. In contrast to existing data-driven system identification approaches that identify the network structure or system parameters separately, a joint estimation framework is developed in this study. The joint framework incorporates historical measurements and enables the simultaneous estimation of transmission topology and epidemic factors. The use of the joint estimation scheme reduces the estimation error. The stability of equilibria and convergence behaviors of proposed dynamics are then analyzed. Furthermore, the sensitivity of the proposed model to population movements is evaluated in terms of the basic reproduction number. This article also rigorously investigates the effectiveness of non-pharmaceutical interventions via distributively controlling population flow in curbing virus transmission. It is found that the population flow control strategy reduces the number of infections during the epidemic.