SBIR-STTR Award

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.

Public Health Relevance Statement:
Project Narrative The COVID-19 pandemic has magnified the need for enhanced ability to accurately anticipate future outbreaks due to novel and endemic viral pathogens due to biases and expense related to current state-of-the art techniques. Predictive statistical modelling of wastewater is a highly innovative and impactful opportunity to anticipate the timing, location, and severity of future outbreaks from SARS-CoV-2 and other novel and endemic viral pathogens. This project is focused on proving the feasibility of developing next-generation statistical models and demonstrating their unique effectiveness in anticipating outbreaks and as a practical and cost- saving approach for heading off future viral outbreaks.

Project Terms:
Communities; Complication; Data Analyses; Data Analysis; data interpretation; Cessation of life; Death; Diagnosis; Disease; Disorder; Disease Outbreaks; Outbreaks; Economics; Epidemic; Future; Growth; Generalized Growth; Tissue Growth; ontogeny; Head; Health; Recording of previous events; History; HIV; AIDS Virus; Acquired Immune Deficiency Syndrome Virus; Acquired Immunodeficiency Syndrome Virus; Human Immunodeficiency Viruses; LAV-HTLV-III; Lymphadenopathy-Associated Virus; Virus-HIV; Hospitalization; Hospital Admission; Human; Modern Man; Influenza; Grippe; Lead; Pb element; heavy metal Pb; heavy metal lead; Michigan; Statistical Models; Probabilistic Models; Probability Models; statistical linear mixed models; statistical linear models; Persons; Public Health; Research; Development and Research; R & D; R&D; research and development; statistics; Testing; Time; Translating; Virus Diseases; Viral Diseases; viral infection; virus infection; virus-induced disease; Virus; Work; Cost Savings; base; improved; Phase; Variant; Variation; Biological; biologic; Trust; uptake; Contracting Opportunities; Contracts; Knowledge; Life; programs; Scientist; Severities; Techniques; Location; Test Result; Viral; interest; Consult; Services; Municipalities; novel; Reporting; Modeling; Sampling; Norovirus; Norwalk-like Viruses; disparity in health; health disparity; Effectiveness; preventing; prevent; Address; Data; International; Small Business Innovation Research Grant; SBIR; Small Business Innovation Research; Pathway interactions; pathway; cost; data modeling; model of data; model the data; modeling of the data; predictive modeling; computer based prediction; prediction model; data integration; next generation; pathogen; Population; Prevalence; innovation; innovate; innovative; community organizations; commercialization; high risk; high reward; population based; Data Science; health care availability; access to health care; access to healthcare; accessibility of health care; accessibility to health care; accessibility to healthcare; health care access; health care service access; health care service availability; healthcare access; healthcare accessibility; healthcare availability; healthcare service access; healthcare service availability; genomic signature; genomic classifier; pathogenic virus; viral pathogen; virus pathogen; COVID-19; COVID19; CV-19; CV19; corona virus disease 2019; coronavirus disease 2019; coronavirus disease-19; coronavirus infectious disease-19; 2019-nCoV; 2019 novel corona virus; 2019 novel coronavirus; COVID-19 virus; COVID19 virus; CoV-2; CoV2; SARS corona virus 2; SARS-CO-V2; SARS-COVID-2; SARS-CoV-2; SARS-CoV2; SARS-associated corona virus 2; SARS-associated coronavirus 2; SARS-coronavirus-2; SARS-related corona virus 2; SARS-related coronavirus 2; SARSCoV2; Severe Acute Respiratory Coronavirus 2; Severe Acute Respiratory Distress Syndrome CoV 2; Severe Acute Respiratory Distress Syndrome Corona Virus 2; Severe Acute Respiratory Distress Syndrome Coronavirus 2; Severe Acute Respiratory Syndrome CoV 2; Severe Acute Respiratory Syndrome-associated coronavirus 2; Severe Acute Respiratory Syndrome-related coronavirus 2; Severe acute respiratory syndrome associated corona virus 2; Severe acute respiratory syndrome corona virus 2; Severe acute respiratory syndrome coronavirus 2; Severe acute respiratory syndrome related corona virus 2; Wuhan coronavirus; coronavirus disease 2019 virus; coronavirus disease-19 virus; hCoV19; nCoV2; machine learning method; machine learning based method; machine learning methodologies; COVID-19 pandemic; COVID crisis; COVID epidemic; COVID pandemic; COVID-19 crisis; COVID-19 epidemic; COVID-19 global health crisis; COVID-19 global pandemic; COVID-19 health crisis; COVID-19 public health crisis; COVID19 crisis; COVID19 epidemic; COVID19 global health crisis; COVID19 global pandemic; COVID19 health crisis; COVID19 pandemic; COVID19 public health crisis; SARS-CoV-2 epidemic; SARS-CoV-2 global health crisis; SARS-CoV-2 global pandemic; SARS-CoV-2 pandemic; SARS-CoV2 epidemic; SARS-CoV2 pandemic; SARS-coronavirus-2 epidemic; SARS-coronavirus-2 pandemic; Severe Acute Respiratory Syndrome CoV 2 epidemic; Severe Acute Respiratory Syndrome CoV 2 pandemic; Severe acute respiratory syndrome coronavirus 2 epidemic; Severe acute respiratory syndrome coronavirus 2 pandemic; corona virus disease 2019 epidemic; corona virus disease 2019 pandemic; coronavirus disease 2019 crisis; coronavirus disease 2019 epidemic; coronavirus disease 2019 global health crisis; coronavirus disease 2019 global pandemic; coronavirus disease 2019 health crisis; coronavirus disease 2019 pandemic; coronavirus disease 2019 public health crisis; coronavirus disease crisis; coronavirus disease epidemic; coronavirus disease pandemic; coronavirus disease-19 global pandemic; coronavirus disease-19 pandemic; severe acute respiratory syndrome coronavirus 2 global health crisis; severe acute respiratory syndrome coronavirus 2 global pandemic; COVID-19 diagnosis; COVID19 diagnosis; SARS-CoV-2 diagnosis; coronavirus disease 2019 diagnosis; diagnosed with COVID-19; diagnosed with COVID19; diagnosed with SARS-CoV-2; diagnosed with coronavirus 2019; diagnosed with coronavirus disease 2019; severe acute respiratory syndrome coronavirus 2 diagnosis; COVID-19 monitoring; COVID19 monitoring; SARS-CoV-2 monitoring; coronavirus disease 2019 monitoring; severe acute respiratory syndrome coronavirus 2 monitoring; wastewater testing; waste water based testing; waste water testing; wastewater based testing; wastewater monitoring; waste water based monitoring; waste water monitoring; wastewater based monitoring; wastewater sampling; waste water sampling; poor communities; impoverished communities; poverty communities; SARS-CoV-2 variant; 2019-nCoV variant; 2019-nCoV variant forms; 2019-nCoV variant strains; COVID-19 variant; COVID-19 variant forms; COVID-19 variant strains; SARS-CoV-2 variant forms; SARS-CoV-2 variant strains; coronavirus disease 2019 variant; coronavirus disease 2019 variant forms; coronavirus disease 2019 variant strains; severe acute respiratory syndrome coronavirus 2 variant; severe acute respiratory syndrome coronavirus 2 variant forms; severe acute respiratory syndrome coronavirus 2 variant strains; COVID-19 outbreak; COVID19 outbreak; SARS-CoV-2 outbreak; SARS-CoV2 outbreak; Severe acute respiratory syndrome coronavirus 2 outbreak; coronavirus disease 2019 outbreak; coronavirus disease-19 outbreak; SARS-CoV-2 infection; COVID-19 infection; COVID19 infection; SARS-CoV2 infection; Severe acute respiratory syndrome coronavirus 2 infection; coronavirus disease 2019 infection; infected with COVID-19; infected with COVID19; infected with SARS-CoV-2; infected with SARS-CoV2; infected with coronavirus disease 2019; infected with severe acute respiratory syndrome coronavirus 2; policy recommendation; recommendation for policy; machine learning model; machine learning based model

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.

Public Health Relevance Statement:
Narrative As the world returns to a new normal, post-COVID19 pandemic, there is a widely accepted need to improve technologies to anticipate future viral outbreaks due to endemic viral pathogens, especially among the most at- risk and vulnerable citizens. Building off our highly successful Phase I project, we will use predictive statistical modelling using wastewater to anticipate the timing and severity of future outbreaks from SARS-CoV-2, Influenza, RSV and Norovirus within high risk, older residents residing in long-term care facilities. This project is focused on establishing the quality and usability of next-generation statistical models to anticipating outbreaks, and modify LTCF infectious disease practices as a practical and cost-saving approach for heading off future viral outbreaks. Terms: <2019 novel corona virus; 2019 novel coronavirus; 2019-nCoV; Aging; Assisted Living Facilities; Behavior; Benchmarking; Best Practice Analysis; COVID crisis; COVID epidemic; COVID monitoring; COVID pandemic; COVID-19; COVID-19 crisis; COVID-19 epidemic; COVID-19 era; COVID-19 global health crisis; COVID-19 global pandemic; COVID-19 health crisis; COVID-19 monitoring; COVID-19 outbreak; COVID-19 pandemic; COVID-19 period; COVID-19 public health crisis; COVID-19 virus; COVID-19 years; COVID19 monitoring; COVID19 outbreak; COVID19 virus; CV-19; Caring; Cell Communication and Signaling; Cell Signaling; Client; CoV-2; CoV2; Communicable Diseases; Communities; Computerized Medical Record; Coronavirus Infectious Disease 2019; Cost Savings; Data; Data Science; Diagnosis; Disease; Disease Outbreaks; Disorder; Economics; Elderly; Electronic Medical Record; Ensure; Epidemic; Extended Care Facilities; Funding; Future; Generalized Growth; Grippe; Growth; Health; Health Care Costs; Health Costs; Health Facilities; Health care facility; Healthcare Costs; Healthcare Facility; Home; Human; I-Corps; Individual; Industry; Infectious Disease Pathway; Infectious Diseases; Infectious Disorder; Influenza; Innovation Corps; Intracellular Communication and Signaling; Investments; Lead; Location; Long-Term Care; Machine Learning; Marketing; Modeling; Modern Man; Monitor; NIH; National Institutes of Health; Norovirus; Norwalk-like Viruses; Outbreaks; Outcome; Pathway interactions; Pb element; Phase; Population; Position; Positioning Attribute; Probabilistic Models; Probability Models; Research; Risk; SARS corona virus 2; SARS-CO-V2; SARS-COVID-2; SARS-CoV-2; SARS-CoV-2 epidemic; SARS-CoV-2 global health crisis; SARS-CoV-2 global pandemic; SARS-CoV-2 monitoring; SARS-CoV-2 outbreak; SARS-CoV-2 pandemic; SARS-CoV2; SARS-associated corona virus 2; SARS-associated coronavirus 2; SARS-coronavirus-2; SARS-coronavirus-2 epidemic; SARS-coronavirus-2 pandemic; SARS-related corona virus 2; SARS-related coronavirus 2; SARSCoV2; SBIR; Sampling; Scientist; Services; Severe Acute Respiratory Coronavirus 2; Severe Acute Respiratory Distress Syndrome CoV 2; Severe Acute Respiratory Distress Syndrome Corona Virus 2; Severe Acute Respiratory Distress Syndrome Coronavirus 2; Severe Acute Respiratory Syndrome CoV 2; Severe Acute Respiratory Syndrome CoV 2 epidemic; Severe Acute Respiratory Syndrome CoV 2 pandemic; Severe Acute Respiratory Syndrome-associated coronavirus 2; Severe Acute Respiratory Syndrome-related coronavirus 2; Severe acute respiratory syndrome associated corona virus 2; Severe acute respiratory syndrome coronavirus 2; Severe acute respiratory syndrome coronavirus 2 epidemic; Severe acute respiratory syndrome coronavirus 2 outbreak; Severe acute respiratory syndrome coronavirus 2 pandemic; Severe acute respiratory syndrome related corona virus 2; Severities; Sewage; Signal Transduction; Signal Transduction Systems; Signaling; Skilled Nursing Facilities; Small Business Innovation Research; Small Business Innovation Research Grant; Societies; Statistical Models; Technology; Testing; Time; Tissue Growth; Training; United States National Institutes of Health; Urbanicity; Validation; Viral; Vulnerable Populations; Work; Wuhan coronavirus; acute COVID-19; acute SARS-CoV-2 infection; acute phase of COVID-19; acute phase of SARS-CoV-2 infection; advanced age; advocacy organizations; assisted living; assistive living; assistive living facilities; benchmark; biological signal transduction; burden of disease; burden of illness; care facilities; catalyst; commercialization; computer based prediction; coronavirus disease 2019; coronavirus disease 2019 crisis; coronavirus disease 2019 epidemic; coronavirus disease 2019 global health crisis; coronavirus disease 2019 global pandemic; coronavirus disease 2019 health crisis; coronavirus disease 2019 monitoring; coronavirus disease 2019 outbreak; coronavirus disease 2019 pandemic; coronavirus disease 2019 public health crisis; coronavirus disease 2019 virus; coronavirus disease crisis; coronavirus disease epidemic; coronavirus disease monitoring; coronavirus disease pandemic; coronavirus disease-19; coronavirus disease-19 global pandemic; coronavirus disease-19 outbreak; coronavirus disease-19 pandemic; coronavirus disease-19 virus; coronavirus infectious disease-19; dashboard; data integration; data modeling; design; designing; detection assay; disease burden; disease control; disease model; disorder control; disorder model; economic; economic cost; economic impact; extended care; flexibility; flexible; future outbreak; geriatric; global health; hCoV19; heavy metal Pb; heavy metal lead; high risk; homes; improved; longterm care; machine based learning; model of data; model the data; modeling of the data; nCoV2; next generation; next outbreak; novel; ontogeny; outbreak in the future; outbreak of SARS-CoV-2; pandemic; pandemic disease; pathogenic virus; pathway; post-COVID; post-COVID-19; post-coronavirus disease 2019; predictive modeling; programs; risk mitigation; senior citizen; severe acute respiratory syndrome coronavirus 2 global health crisis; severe acute respiratory syndrome coronavirus 2 global pandemic; severe acute respiratory syndrome coronavirus 2 monitoring; statistical learning; statistical linear mixed models; statistical linear models; usability; validations; viral disease outbreak; viral outbreak; viral pathogen; virus disease outbreak; virus pathogen; vulnerable group; vulnerable individual; vulnerable people; waste water based epidemiology; waste water based monitoring; waste water epidemiology; waste water monitoring; waste water sampling; wastewater based epidemiology; wastewater based monitoring; wastewater epidemiology; wastewater monitoring; wastewater sampling