One of the primary goals of this research was to characterize the viability of biosurveillance models to provide operationally relevant information to decision makers, in order to identify areas for future research. Two critical characteristics differentiate this work from other infectious disease modeling reviews. First, we reviewed models that attempted to predict the disease event, not merely its transmission dynamics. Second, we considered models involving pathogens of concern as determined by the US National Select Agent Registry.
Background: A rich and diverse field of infectious disease modeling has emerged over the past 60 years and has advanced our understanding of population- and individual-level disease transmission dynamics, including risk factors, virulence and spatio-temporal patterns of disease spread. Recent modeling advances include biostatistical methods, and massive agent-based population, biophysical, ordinary differential equation, and ecological-niche models. Diverse data sources are being integrated into these models as well, such as demographics, remotely-sensed measurements and imaging, environmental measurements, and surrogate data such as news alerts and social media. Yet, there remains a gap in the sensitivity and specificity of these models not only in tracking infectious disease events but also predicting their occurrence.
Objective
The objective of this manuscript is to present a systematic review of biosurveillance models that operate on select agents and can forecast the occurrence of a disease event.