Impact of initial infected characteristics in an agent-based model of infectious respiratory disease: A methodological study

There is limited existing literature about how infectious conditions are introduced in agent-based models (ABMs). This methodological study investigated the impact of the number, timing, and age of initial infected agents on the development of infectious disease outbreaks in ABMs, using influenza as an example. With an ABM, we modeled influenza in different size United States counties with different initial case characteristics including initial case number, timing, and age and calculated attack rate, season peak timing, and epidemic duration for each scenario. Increasing number of initial cases increased attack rate which plateaued at an initial case number proportional to population size. However, using a small initial case number resulted in many simulations with <1% attack rate. Introducing cases over time rather than at a single time point had minimal impact on attack rate but moved the season peak later in the season. Seeding infections in a younger age group increased the likelihood of a successful outbreak, attack rate, and epidemic duration.

In an ABM of an infectious respiratory disease, the characteristics of the initial infected cases impacted the resulting outbreak. The outbreak was accelerated, lengthened, or even nonexistent depending on how many, which, and when agents are first infected. Moving forward, it is important for ABM studies to justify seeding method and describe how it may impact results. Our study can be used to inform parameterization of initial conditions for ABMs of infectious diseases, enabling better forecasting of outbreaks and impacts of interventions.