Individual‐based Computational Modeling of Smallpox Epidemic Control Strategies

TLDR

The authors created an individual-based computational model to study smallpox epidemic transmission and control in response to bioterrorism concerns. The model simulates an artificial society of individual agents interacting in homes, workplaces, schools, and hospitals, with realistic disease parameters, to generate epidemic dynamics and evaluate eight response scenarios at two population scales. The simulation demonstrated that contact tracing, targeted vaccination of contacts, reactive vaccination of hospital workers, and isolation of cases could contain smallpox in both town and city scenarios.

Abstract

In response to concerns about possible bioterrorism, the authors developed an individual‐based (or “agent‐based”) computational model of smallpox epidemic transmission and control. The model explicitly represents an “artificial society” of individual human beings, each implemented as a distinct object, or data structure in a computer program. These agents interact locally with one another in code‐represented social units such as homes, workplaces, schools, and hospitals. Over many iterations, these microinteractions generate large‐scale macroscopic phenomena of fundamental interest such as the course of an epidemic in space and time. Model variables (incubation periods, clinical disease expression, contagiousness, and physical mobility) were assigned following realistic values agreed on by an advisory group of experts on smallpox. Eight response scenarios were evaluated at two epidemic scales, one being an introduction of ten smallpox cases into a 6,000‐person town and the other an introduction of 500 smallpox cases into a 50,000‐person town. The modeling exercise showed that contact tracing and vaccination of household, workplace, and school contacts, along with prompt reactive vaccination of hospital workers and isolation of diagnosed cases, could contain smallpox at both epidemic scales examined.