PIPP Phase 1 Theme 1 grant
The Predictive Intelligence for Pandemic Prevention (PIPP) Phase I: Development Grants Center for Emerging Pathogen Prediction and Integration (CEPPI) will lead strategic surveillance of wildlife pathogens across changing environments. Understanding and preventing the emergence of new infectious diseases requires interpreting many types of data (e.g., information on the disease-causing pathogens, interactions between the human and wildlife populations, and changes in their associated environments). The team will use the vast historical data and sampling available in biorepositories, such as museums, to systematically investigate pathogen emergence to enable reliable predictions of zoonotic disease to help prevent or mitigate future pandemics. Many aspects of pathogen biology in wildlife remain poorly documented worldwide, particularly in low-income, biodiverse regions. Pandemics pose a challenge that requires international, collaborative, proactive solutions, so the Center will focus on developing best methods in training the next generation of pathogen biologists, improving basic biodiversity infrastructure, databases, and workflows, and then sharing these new approaches globally. Working especially with partner museums in the Americas, the Center will expand wildlife sampling strategies to develop a more comprehensive, decentralized network of specimen repositories and researchers to improve pathogen identification and surveillance.
To improve pathogen detection, surveillance, and mitigation, the Center will promote state-of-the-art biorepositories, genomic screening, and bioinformatic workflows, which are empowered by intuitively interactive visualization web tools, computational and mathematical modeling, and machine learning. Rapid visual and intelligent integration of pathobiology into the vast global, digital infrastructure for wild mammals is the cornerstone of this Center, which will act as an early warning system for pandemic prediction and prevention. In Phase I, the cross-disciplinary team aims to develop internationally scalable multidisciplinary workflows to build: 1) informatics baselines for pathogens and hosts derived from existing archival biorepositories representing decadal sampling of mammalian communities that will be linked directly to targeted strategies for improved monitoring and surveillance; 2) pipelines that streamline sequencing and bioinformatic methods for rapid, affordable, large-scale screening of mammalian host and parasite samples to provide scalable views of diversity and change over space and time; 3) pathogen risk assessments linking novel methods that integrate social and environmental parameters and pathogen diversity to predict the outcomes of accelerating anthropogenic change; 4) collaborations across local biorepositories to develop strategic sampling and digitization protocols for expanded geographic, temporal and taxonomic coverage; and 5) problem-solving networks through educational modules, direct engagement, and collaborative learning. CEPPI helps standardize exploration of pathogens and biodiversity by building new connections with local communities, sequencing and bioinformatic facilities, and public health and natural resource agencies. New capacities for visualization, informatics interpretation, and translation emanate from the development of fast, affordable, and scalable sequencing and bioinformatic technologies. These capacities will stimulate new methods of pathogen detection, discovery, and monitoring that will foster global pathogen surveillance and mitigation.
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).
Uchicago Project:
ABSTRACT
The COVID-19 pandemic has shown how epidemiologic modeling can inform decision making in times of crisis and uncertainty. It has also highlighted significant gaps that must be addressed to create ongoing, interdisciplinary collaborations that can provide more effective predictive intelligence for pandemic prevention (PIPP). The Robust Epidemic Surveillance and Modeling (RESUME) team has experience supporting public health stakeholders during the COVID-19 pandemic and drawing on that has identified critical gaps in three broad areas: 1) communication and collaboration among researchers and public health stakeholders, 2) integration of diverse data streams including surveillance data, and 3) foundational work to predict future pathogens and their evolution. To address these three gaps, the project brings together an interdisciplinary team with expertise in epidemiologic modeling, public health, policy and risk analysis, social sciences, decision modeling, artificial intelligence (AI), high-performance computing (HPC), molecular engineering, structural biology, and large-scale data management and assimilation. The investigators will engage modelers and public health stakeholders to broaden participation in collaborative modeling, carry out pilot projects, and develop training modules for generalizable approaches to collaborative pandemic intelligence. The project will develop curricula for pandemic prevention education and workforce development. The training activities and new curricula will help build a convergent and inclusive PIPP capacity by bringing a diverse workforce into the research pipeline from high school through graduate school and enhancing the expertise of the public health workforce.
The project will convene workshops with stakeholders and researchers and carry out pilot studies to refine the vision for an interdisciplinary PIPP center. Activities will advance the science and practice in the following three foci: 1) Co-design of policy, implementation, and risk analyses: Early and sustained engagement with public health stakeholders for the co-design of pandemic prevention requirements; Development and application of methods for decision making under deep uncertainty, value of information, and adaptive interventions; Development of novel computational approaches exploiting advances in AI, data management, and HPC methods for creating integrated multi-fidelity, multi-method, and multi-spatiotemporal scale modeling analyses. 2) Robust data for modeling: Development of novel real-time sensors and near real-time data streams from sensor-based air, wastewater, and human monitoring (including for novel pathogens); Integration of sensor, public health surveillance and clinical data through model-based, data assimilation approaches for combining data streams and epidemiological model forecasts; Creation of large-scale open-science data storage and indexing capabilities for epidemiologic modelers. 3) Prediction of future pathogens: Fundamental research in experimental and theoretical pathogen structure and evolution; Scenario development for epidemiological and decision support modeling of emerging pathogens. To support these foci, the project will demonstrate a sustainable simulation, data, decision support, and learning collaborative platform, the Open Science Platform for Robust Epidemic analYsis (OSPREY). The OSPREY platform will serve as crucial PIPP cyberinfrastructure built to leverage investments in forthcoming exascale and increasingly ubiquitous HPC and data resources.
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.