Movement and spatial specificity support scaling in ant colonies and immune systems: Application to national biosurveillance

Abstract

Data obtained from biosurveillance can be used by public health systems to detect and respond to disease outbreaks and save lives. However, existing data is distributed across large geographic areas, and both the quality and type of data vary in space and time. We discuss a framework for analyzing biosurveillance information to minimize detection time and maximize detection accuracy while scaling the analysis over large regions. We propose that strategies used by canonical biological complex systems, which are adapted to diverse environments, provide good models for the design of a robust, adaptive, and scalable biosurveillance system. Drawing from knowledge of the adaptive immune system, and ant colonies, we examine strategies that support the scaling of detection in order to search and respond in large areas with dynamic distributions of data. Based on this research, we discuss a bioinspired approach for a distributed, adaptive, and scalable biosurveillance system.