WISE: Wastewater-based Infectious Disease Surveillance

MOTIVATION Infectious disease dynamics and drivers are primarily inferred from clinical case data. When there are insufficiencies in clinical data collection (i.e., limited testing), disease dynamics remain incomplete or unobserved. Examples include dynamics of disease with mild-to-moderate health impacts or with high rates of asymptomatic infection. Wastewater-based epidemiology (WBE) offers a complementary approach to track infectious disease dynamics independent from clinical surveillance. By tracking viral loads shed into wastewater by large populations within a catchment over time, we can infer and potentially predict previously unobserved epidemiological dynamics and their drivers.GOALS Here, we propose to advance the methods and analyses underlying wastewater-based infectious disease surveillance such that we obtain insights into the epidemiological dynamics of viruses beyond those achievable through clinical case surveillance alone. Specifically, our proposal will allow us to address the following THREE MAIN RESEARCH QUESTIONS:1) What drives the epidemiological dynamics of viruses? How early can viral outbreaks be detected and their future fate modelled based on wastewater analyses?2) Can we rapidly determine the genetic variants founding a wave? And can we determine the future fate of the wave based on wastewater data? 3) How does a pandemic virus transform into an endemic virus? To what extent can we predict its endemic properties?These research questions advance foundational, policy-relevant insights into disease trajectory that can help inform intervention strategies, and can not be addressed using clinical surveillance alone.RESEARCH APPROACH To tackle these research questions, we will advance and adapt our methodological approaches obtained during the COVID-19 pandemic to address SARS-CoV-2 disease trajectory. Specifically, we will extend our work to cover other priority pathogens (human coronaviruses, influenza, norovirus and other enteropathogens); improve methodologies for virus detection, quantification, and sequencing; advance methodologies for modeling and analyzing data to address epidemiology, and disseminate findings and methodologies to relevant stakeholders.IMPLEMENTATION The project will be led by Prof. Dr. T. Stadler, with expertise in infectious disease epidemiology, through collaboration with three other applicants with diverse expertise in the fields of environmental engineering (Dr. C. Ort), pathogen detection in the environment (Dr. T. Julian), and computational biology (Prof. Dr. N. Beerenwinkel). The project is organized by four distinct research work packages (WPs) that integrate to provide infectious disease data that is unattainable without the proposed interdisciplinary approach. Specifically, WP1 (Characterizing catchment properties) and WP2 (Methods for virus detection) advance methods to characterize virus shed in wastewater. WP3 (Computational data analysis) and WP4 (Linking to epidemiology) advance modeling and statistical approaches to translate viral loads in wastewater to meaningful, policy-relevant indicators of disease trajectory that directly address the project Goals. Finally, WP5 (Capacity building and dissemination) collates and disseminates methods and findings to relevant stakeholders, with the goal of transitioning WBE from research to surveillance.