PRedicting Emergence Of Virulent Entities By Novel Technologies (PREVENT) Symposium - Session 1, End-to-End Theme

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Marathe, Madhav
Peters, Debra
Ke, Ruian
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Madhav Marathe - Plenary Talk TITLE: "Real-time End-to-End Pandemic Planning, Prediction and Response". The COVID-19 pandemic has brought forth the need for a sustainable capability for pandemic planning, response, and mitigation at various geographic, temporal and social scales. The social, economic, and health impact of the pandemic has been immense and will continue to be felt for decades to come. Since February 2020, our group has been providing local, state, and federal authorities continuous modeling and analytics support as they work assiduously to contain the pandemic. Based on this experience, I will describe the scientific and engineering challenges and opportunities in developing an end-to-end program to better prepare and respond to future pandemics and epidemic outbreaks.
Debra Peters - Presentation TITLE: "Big Data-Model Integration As A Multi-Scale Approach To Predicting The Spread Of Vector-Borne Diseases: An End-To-End Vision And Operational Framework". Geospatial data are increasingly providing opportunities for application to a predictive disease ecology paradigm provided the data can be synthesized and harmonized with fine-scale, highly-resolved data on vector and host responses to their environment. Here, we provide a vision for this multi-scale, integrated approach, and illustrate its utility in understanding and predicting the spread of vesicular stomatitis (VS), a common viral vector-borne vesicular disease affecting livestock throughout the Americas. Our results from two outbreaks (2004-05, 2014-15) show that VS occurrence at a local scale was related to conditions that can be monitored (rainfall, temperatures, streamflow) or modified (vegetation). At landscape to regional scales, conditions that favor different insect vectors were indicated, either black flies in incursion years or biting midges in expansion years. The recent 2019-2020 outbreak with a different viral serotype provided new challenges to prediction capabilities. Application of this approach to other diseases or to VS predictions is limited by data availability on biotic processes across scales, and skilled personnel needed to conduct spatio-temporal machine or deep learning analyses, and process-based modeling.
Ruian Ke - Presentation TITLE: "Estimating Key Parameters For Infectious Disease Outbreaks And Implications For Control". Two epidemiological parameters that are of paramount importance in assessing the epidemic potential of a novel pathogen are the early epidemic growth rate and the reproductive number R0. In this talk, I will present our efforts to estimate these two parameter values during early SARS-CoV-2 outbreaks and how we reached the conclusion in early Feb. 2020 that early, strong social distancing efforts are necessary to stop the spread of the virus. More specifically, we estimated that the early epidemic grew exponentially at rates between 0.19-0.29/day (epidemic doubling times between 2.4-3.7 days) and the reproductive numbers are likely between 3.9 and 7.1. This means that a large fraction of the population (between 74% and 86%) needs to be immune to achieve herd immunity. I will discuss lessons learned for understanding and controlling future outbreaks of novel pathogens.
National Science Foundation (U.S.)
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69:47 minutes
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