PIPP Phase I: Advancing Environmental Surveillance for Pandemic Prediction in Remote and Resource Poor Settings

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Pathogens with pandemic potential such as Ebola, HIV, and avian influenza frequently emerge in low-resources or remote settings with limited healthcare infrastructure. Unfortunately, economic, logistical, behavioral, and technological barriers limit comprehensive clinical testing in these settings. Thus, disease may spread unchecked before a threat to public health is identified. Environmental surveillance has emerged as a versatile and complementary platform to existing clinical tools for monitoring and tracking the spread and transmission of infectious diseases. The basic premise of environmental surveillance is that the detection and quantification of microbial and virial biomarkers extracted from air, soil, water, and wastewater samples can be used to monitor the onset, spread and community transmission of infectious diseases. While environmental surveillance strategies can overcome some of the cost and logistical barriers associated with comprehensive clinical testing, technological hurdles related to environmental sampling and data analysis in low-resource settings and challenges in communicating results to the right people at the right time have limited its implementation in “hotspot” settings where emerging pathogens are likely to occur. The overarching goal of this Predictive Intelligence for Pandemic Prevention (PIPP) Phase I: Development Grants project is to address the challenges associated with the development and deployment of “universal” environmental surveillance strategies for pathogens of pandemic potential. To advance this goal, the project team will leverage funding for this PIPP Phase I planning grant to lay the foundation for the establishment of a Center with globally linked network of environmental surveillance tools that would be deployed in emerging infectious disease hot spots to detect pathogens of concern with timely notification of local, regional, and national health system leaders and stakeholders. The Center activities will include targeted research projects, workshops, and workforce development including the training and mentoring of two post-doctoral fellows and a graduate student at the University of Kentucky and Arizona State University.This project will establish the Center for the Discovery of Emerging Environmental Pathogens (C-DEEP) with a mission to advance the science of environmental surveillance and metagenomics, especially in low-resources or remote settings where pandemics are likely to emerge and where current disease surveillance processes are inadequate. In collaboration with longstanding partners in Sub-Saharan Africa and Southeast Asia, the project team plans to extend existing expertise in environmental surveillance of emerging pathogens by building transdisciplinary collaborations, critically defining knowledge and technology gaps, and conducting preliminary research designed to enrich the capabilities of the C-DEEP. The specific objectives of the project are to: 1) Identify setting-specific obstacles for obtaining and analyzing environmental samples; 2) Efficiently translate “big data” readouts (e.g., metagenomic data) into actionable public health policy changes; 3) Build and grow a diverse alliance of stakeholders who are committed to this challenge; and 4) Conduct targeted pilot experiments to demonstrate concept feasibility and de-risk critical analytic processes. The development of more effective environmental surveillance tools has the potential to identify emerging pathogens, support surveillance of established and sporadic pathogens, such as seasonal influenza and SARS-CoV-2 and monitor antimicrobial resistance markers. The development and deployment of environmental metagenomics technologies and tools through international partnerships could also bolster local disease surveillance capacity and create global networks for pandemic preparedness in low-resource and remote settings thereby enabling surveillance in communities where it is often most needed. This award is supported by the cross-directorate Predictive Intelligence for Pandemic Prevention Phase I (PIPP) program, which is jointly funded by the Directorates for Biological Sciences (BIO), Computer Information Science and Engineering (CISE), Engineering (ENG), and Social, Behavioral and Economic Sciences (SBE).This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.