Wastewater data integration and modelling to accurately predict community and organizational outbreaks due to viral pathogens - Project Summary. The COVID-19 pandemic has magnified the need for enhanced ability to accurately
anticipate future outbreaks due to novel and endemic viral pathogens. Without systematic surveillance, the
ability to head off outbreaks before they occur is challenging: the data from positive human test results is often
too late to prevent a major outbreak from occurring, despite substantial lockdown efforts. The key reason for
this delay is that people are infectious for days before (and if) they are diagnosed positive. We can no longer
rely on population-based testing, which (a) is delayed; (b) is non-random and expensive, exacerbating well-
known and understood health disparities; and (c) relies on highly accurate, widely distributed test availability
and use. Over the last fourteen months, our team of affiliated scientists has developed and implemented a
wastewater-sampling approach to monitor for COVID-19 and other viral pathogens. Our approach utilizes
unique genomic signatures of SARS-CoV-2 (the virus that causes COVID-19) to detect this pathogen in
wastewater, providing inexpensive and unbiased real-time data on COVID-19 infections in communities and
organizations. Our group has begun to contract with municipalities, academic entities and large manufacturing
companies to provide real-time, unbiased data on the presence of COVID-19. Currently, however, wastewater
COVID-19 data has primarily been used solely to determine the presence/absence of SARS-CoV-2 in
samples. We see a highly innovative and impactful opportunity to leverage these data further to anticipate the
timing, location, and severity of future outbreaks from SARS-CoV-2 and other novel and endemic viral
pathogens. The Superior Statistical Research (SSR) R&D team is an internationally recognized group of
wastewater and public health experts with cross-cutting expertise in statistics, data analysis, modelling,
computing, wastewater monitoring, and the ability to translate wastewater and health information into
actionable steps for organizations and communities. To address this opportunity, we propose a Phase I proof-
of-concept SBIR project with two Aims. First, we will demonstrate that it is possible to anticipate locations and
organizations with future outbreaks of COVID-19 with significant lead time. Second, we will demonstrate how
model predictions can be optimized to be useful for municipalities and organizations. Feasibility will be
determined by having models with excellent predictive ability (R2>0.90) (Aim 1) and by demonstrating the
profitability of the commercialization pathway (Aim 2). Phase I feasibility will allow us to extend modelling
capabilities beyond SARS-CoV-2 to other viral pathogens (e.g., influenza, norovirus, HIV): expanding
wastewater testing capabilities for these additional pathogens, and further roll-out and improvement of the
machine-learning/modelling effort in Phase II. Ultimately, we will have a full-service commercial set of
predictive models (Phase III) that can be combined with wastewater-monitoring programs at the community
and organizational level, leading to dramatic reductions in viral disease outbreaks.
