Award Abstract #2200052

PIPP Phase I: Predicting and Preventing Epidemic to Pandemic Transitions

See grant description on NSF site

Program Manager:

Mitra Basu

Active Dates:

Awarded Amount:



Ioannis Paschalidis

Jonathan H Epstein

Eric D Kolaczyk

Diane Joseph-McCarthy

Nahid Bhadelia

Awardee Organization:

Trustees of Boston University


Computer and Information Science and Engineering (CISE)


The COVID-19 pandemic and its effects, both in terms of the millions of lives lost and the trillions in estimated costs, are a recent example of the devastation pandemics can cause. Any discernible progress in the prediction, early detection, and rapid response would have significant impacts on human welfare. The overarching goal of this project is to develop a comprehensive strategy and the required science for predicting and preventing future pandemics. Predicting a pandemic at its pre-emergence, zoonotic stage requires considering millions of undescribed viruses thought to exist in mammals and birds, which could lead to many false alarms. On the other hand, detecting a pandemic after it has spread widely is too late. Instead, this project will develop methods for detecting when an emerging pathogen has spilled over from its natural animal reservoir into humans, causing a small, localized disease cluster, and will seek to develop a suite of rapid response and mitigation strategies. The research agenda will form the basis of a research Center to undertake a longer-term effort. This project offers educational and training opportunities for graduate students and post-docs. The research is organized around four tasks that map to a natural progression from prediction and detection to prevention. Task 1 seeks to identify location hotspots of pathogen emergence and to compile ranked lists of the most likely zoonotic pathogens that could cause an initial outbreak. Task 2 will focus on detecting disease anomalies in healthcare settings with methods applicable to resource-limited settings, leveraging alternative data sources from social media, web search, cell phone mobility patterns, local case reports, and death reports. Task 3 will consider the more detailed characterization of a pathogen causing a local disease cluster. It will also develop network-based disease spread models to predict if, and under what conditions, the local disease cluster is likely to evolve into a pandemic. Task 4 will focus on mitigation and response strategies, including individual therapeutics and vaccines, issues of global governance, and decision-making tools to deploy control mechanisms in the form of travel restrictions, lockdowns, social distancing and mask-wearing directives, and drug/vaccine resource allocation. To evaluate the developed framework, the team will apply it to recent historical epidemics and pandemics, considering COVID-19, H1N1, and Ebola. The research team spans a large multidisciplinary space, including biology, ecology, epidemiology, medicine (infectious diseases, virology and microbiology), computer & information science & engineering, and social sciences (behavioral sciences, health policy, and emerging media). 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), Social, Behavioral and Economic Sciences (SBE) and Engineering (ENG). 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.

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