Abstract

Biosurveillance is the ongoing systematic collection, analysis, and interpretation of biospheric data essential to planning, implementing, and evaluating national security interests. Biosurveillance efforts often focus on disease activity and threats to human, animal, or plant health to improve situational awareness of and early warnings of disease emergence or activity. Wastewater-based epidemiology is an important subdomain of biosurveillance that monitors the consumption of, or exposure to, chemicals and pathogens at the community/population level. However, ecological, environmental, economic, cultural, and political variability yields an asymmetric landscape of threats, subsequent exposure(s), and community burden. This work presents an exact approach based on mixed-integer programming to optimize sensor placement in a wastewater network. The primary goal is to maximize the amount of information captured from the network while narrowing down the potential geographical sources of chemical or biological markers (i.e., signals). We use the wastewater systems of Los Angeles (USA) and Regina (Canada) to demonstrate the scalability of our approach in realistic scenarios and how it can enhance stakeholders’ ability to identify emerging threats or assess the effectiveness of strategic interventions post-deployment.