ABSTRACT

In recent decades, new infectious diseases have emerged at a rapid rate around the world?driven by climate change, urbanization, and conflict. To prepare for future infectious disease crises, there is an urgent need to devise new data-driven tools for pandemic surveillance, prediction, and mitigation. The COVID-19 pandemic has demonstrated that such tools must incorporate not only our understanding of the science that underpins new infectious diseases, but also how society?s responses to them can affect their propagation. To address this urgent need, the PIPP Phase I PILOT (Predictive Intelligence for Limiting Outbreak Threats) planning project will bring together the expertise from a wide range of relevant disciplines, including public health, clinical biomedicine, computer science, artificial intelligence, and social science. PILOT Investigators will collaborate with academic, practitioner, and decision-maker communities through multiple roundtable workshops to determine existing knowledge gaps and establish best practices in pandemic surveillance, prediction, and mitigation. This project will also engage the next generation of pandemic scholars by educating and training graduate students and postdoctoral fellows, with an emphasis on communicating science for societal impact. Students and fellows will assist the Investigator team in distilling workshop outcomes into white papers and policy briefs, which will be shared with the public via an open access online knowledge portal and virtual town hall meetings. Moreover, to enable experiential learning, members of the public will be invited to participate in a globally-broadcast, community-wide infectious disease crisis simulation. Success in operationalizing this PIPP Phase I planning project will lead to the development and deployment of a new data-driven modeling pipeline for future pandemic threats during PIPP Phase II.

The PILOT modeling pipeline will combine novel digital data sources with methods from the aforementioned disciplines to address three interconnected scientific challenges: (1) understanding and modeling pandemic potential for disease surveillance, (2) understanding and modeling the impact of interventions for disease prediction, and (3) understanding and modeling intervention acceptance (and refusal) for disease mitigation. During the PIPP Phase I planning project, progress towards the first challenge will involve determining which information sources and computational approaches should be preferentially leveraged when assessing a given pathogen?s pandemic potential at a single point in time (i.e., immediately following its emergence or re-emergence in a given context). Likewise, progress towards the second challenge will involve exploring existing knowledge gaps in simulating interventions via agent-based and game-theoretic models of multi-agent decision-making, particularly under conditions with limited information (i.e., wherein simulation-based scenario analyses may be necessary). Finally, progress towards the third challenge will involve establishing best practices for social contagion models that aim to encourage intervention uptake (i.e., with a focus on complex contagion and identification of influencers across social networks). Thus, the overarching goal of the PIPP Phase I PILOT project will be to plan for a center-scale effort by ascertaining which data types and methodological choices are most appropriate for the development and deployment of the PILOT modeling pipeline (i.e., given existing knowledge gaps and best practices). Implementation of the pipeline will be pursued in a future center-scale effort.

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.

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