Community Transmission of Respiratory Infectious Diseases using Agent-based and Compartmental Models
Non-pharmaceutical interventions (NPIs) were the mainstay to control the spread of COVID-19 at the start of the pandemic. Mathematical modeling has played an important role in determining the effects of these NPIs. An agent-based model and a compartmental model (i.e., extended susceptible-exposed-infectious-recovered) were formulated to simulate the spread of a respiratory infection between two neighboring communities. The study aimed to determine the effects of non-pharmaceutical interventions such as social distancing, community lockdowns and the use of protective gears. The chance of traveling to another community and within the community during the lockdown, and an initial percentage of exposed and infected individuals in both communities influenced the increase in the number of newly infected individuals on both models. It was shown through simulations that an increase in exposed individuals increased the number of new infections; hence, the need for amplified testing-isolation efforts. Protection level of 75-100% effectiveness impeded disease transmission. Inter- or intra-community travels can be an option given that strict preventive measures (e.g., non-pharmaceutical interventions) were observed. The ideal setup for neighboring communities was to implement lockdown when there is a high risk of local transmission while individuals observe social distancing, maximizing protective measures and isolating the exposed. The results of the agent-based and compartmental models showed similar qualitative dynamics; the differences were due to different spatiotemporal heterogeneity and stochasticity. These models can aid decision-makers in designing infectious disease-related policies to protect individuals while continuing population movement.