Wastewater data integration and modelling to accurately predict viral outbreaks in long-term care facilities

Project Summary As thought-leaders now deconstruct the recent global pandemic, there is a conclusive and
resounding argument to conduct enhanced surveillance to accurately anticipate future outbreaks due to
endemic viral pathogens and, that to minimize the global health impact of future outbreaks, we must target the
most vulnerable among us. In our recently funded SBIR Phase I project, our team of affiliate scientists
developed and implemented a wastewater-sampling approach to monitor for COVID-19 and demonstrated that
we can utilize predictive modelling approaches to anticipate future COVID outbreaks by up to seven days.
Importantly, these models are flexible and potentially generalizable: leveraging aspects of epidemic trajectories
that span numerous disease classes and types. As part of our SBIR Phase I efforts, we also talked to well over
100 different potential clients, industry thought leaders and influencers. These conversations, combined with
our technical research, have led us to recognize that the impact of our predictive technology is highest within
the U.S. long-term care facility (LTCF (including skilled nursing facilities (SNFs), assisted living facilities (ALFs)
and other congregate living facilities (CLFs)) market – a >$173 billion annual market which is rapidly expanding
with an aging U.S. population and rising health care costs, further confounded by a massive labor shortage in
LTCFs. Our non-invasive (facility-level sewage outflow) sampling which requires little-to-no facility staff time
and can lead to highly accurate predictions of impending outbreaks is poised to have a massive and disruptive
impact on best practices for infectious disease risk mitigation in the LTCF market. However, while our Phase I
work provided critical proof-of-concept data and a clear potential pathway for commercialization, key critical
gaps still exist including: (a) validating predictive ability at the facility level, (b) demonstrating the ability to
model diseases beyond just COVID-19 to maximize impact (e.g., RSV, Influenza and norovirus) and (c)
demonstrating the ability to make predictions in real-time that impact facility level infectious disease behaviors
to reduce outbreak impact and yield tangible ROI for LTCFs. Thus, in this Phase II proposal, we leverage our
globally recognized team of wastewater based epidemiology (WBE) and data science experts, in partnership
with two of the largest U.S. based LTCF networks (Good Samaritan Society; Western Home Services), and the
leading non-profit LTCF advocacy organization in the U.S. (LeadingAge) to conduct the critically necessary
next steps in testing and implementation of our Phase I technology, in order to position the Aquora SecureCare
technology for full commercialization. In Phase II, we will (Aim 1) demonstrate the ability to anticipate locations
with future outbreaks across a wide range of infectious disease targets with significant lead time and (Aim 2)
demonstrate how WBE model predictions can be optimized to be useful for LTCFs. This Phase II work will
provide the critically needed, validation requested by our emerging LTCF partners that will enable us to engage
with these and more partners in full “Phase III” commercialization and (external) investment.

Grant Number: 5R44AI170537-03
NIH Institute/Center: NIH
Principal Investigator: Aaron Best