Sampling manholes to home in on SARS-CoV-2 infections

Many individuals infected with the novel Coronavirus (SARS-CoV-2) suffer from intestinal infection as well as respiratory infection. These COVID-19-suffering individuals shed virus in their stool, resulting in municipal sewage systems carrying the virus and its genetic remnants. These viral traces can be detected in the sewage entering a wastewater treatment plant (WTP), often resulting in accurate estimates of the extent of infections over a community. In this paper, we develop algorithmic procedures that home in on locations and/or neighborhoods within the community that are most likely to have infections. Our novel data source is wastewater sampled and real-time tested from selected manholes. Our algorithms dynamically and adaptively point to a sequence of manholes to sample and test. The algorithms are often finished after 4 to 8 manhole samples, meaning that - in the field - the procedure can be carried out within one day. The goal is to provide timely information that will support faster more productive human testing for viral infection and thus reduce community spread of the disease. Leveraging the tree graph structure of the sewage system, we develop two heuristic algorithms, the first designed for a community that is certified at a given time to have zero infections and the second for a community known to have many infections. For the first, we assume that wastewater at the WTP has just revealed traces of SARS-CoV-2, indicating existence of a “Patient Zero” in the community. Our first algorithm usually identifies the city block in which the infected person resides. For the second, we home in on a most infected neighborhood of the community, where a neighborhood is usually several city blocks. We present computational results, some applied to the sewer system map of a New England town. The next step is to test our algorithmic procedures in the field, and to make appropriate adjustments