Investigating transmission dynamics of influenza in a public indoor venue: An agent-based modeling approach

Despite much effort, influenza continues to be one of the major public health concerns. To understand its transmission dynamics, extensive epidemiological models have been developed in various spatial contexts, from small-scale community to larger-scale area such as city and country. However, there is a significant lack of rigorous models designated for characterizing specific transmission patterns in fine-scale spatial contexts, particularly, in public indoor venues such as shopping malls, airports, and grocery stores. In fact, the transmissions in such settings could be very critical as the infected individuals can quickly spread the disease area wide. In modeling the transmission dynamics in public venues, one of the biggest challenges is to appropriately capture the random contacts occurred between individuals within a dynamic and highly mobile population. Focusing on the public indoor environment, this study conceptualized an agent-based modeling framework for investigating human-to-human transmissions via an explicitly represented contact network. By applying this framework, a computer simulation model was developed to mimic how the influenza can be transmitted in a real shopping mall in the U.S. The impacts of three contributing factors - timing, number and role (shopper or mall employee) of the introductory cases - was examined under different hypothetical disease scenarios using computer experiments. Furthermore, the effectiveness of employee-targeted vaccination and public social distancing strategies were evaluated with different vaccine efficacies and mall hour reductions, respectively. Our findings are valuable for local health authorities to take actions as they prepare and control the seasonal flu as well as the potential epidemics.

Automated summary

Despite efforts to understand influenza transmission dynamics, there is a lack of rigorous models for specific transmission patterns in fine-scale spatial contexts, particularly in public indoor venues. The study developed an agent-based modeling framework to investigate human-to-human transmissions in a shopping mall and examined the impacts of timing, number, and role of introductory cases under different disease scenarios using computer simulations. The findings have implications for local health authorities in preparing and controlling seasonal flu and potential epidemics.