  Wastewater monitoring is an effective approach for the early detection of
viral and bacterial disease outbreaks. It has recently been used to identify
the presence of individuals infected with COVID-19. To monitor large
communities and accurately localize buildings with infected individuals with a
limited number of sensors, one must carefully choose the sampling locations in
wastewater networks. We also have to account for concentration requirements on
the collected wastewater samples to ensure reliable virus presence test
results. We model this as a sensor placement problem. Although sensor placement
for source localization arises in numerous problems, most approaches use
application-specific heuristics and fail to consider multiple source scenarios.
To address these limitations, we develop a novel approach that combines
Bayesian networks and discrete optimization to efficiently identify informative
sensor placements and accurately localize virus sources. Our approach also
takes into account concentration requirements on wastewater samples during
optimization. Our simulation experiments demonstrate the quality of our sensor
placements and the accuracy of our source localization approach. Furthermore,
we show the robustness of our approach to discrepancies between the virus
outbreak model and the actual outbreak rates.
