In an effort to support decision making by governments and individuals related to the COVID-19 pandemic, the researchers will develop a set of epidemic forecast models to accurately assess the risk presented by COVID-19 in the United States at county, state, and national levels. The models will build on epidemiological data from the CSSE team’s publicly available COVID-19 tracking map, along with anonymized mobile phone data, demographic and socioeconomic information, climate and seasonality factors, and various health and behavioral metrics. The modeling framework will be flexible, and thus able to provide decision support for various policy needs and mitigation strategies. The team will make concerted efforts to maximize the model’s usefulness to decision-makers and ensure the successful translation of modeling outcomes into useful actions. In the short term, the model outputs generated by the team will contribute to the CDC’s COVID-19 national forecasting efforts through the COVID-19 Forecast Hub. In the long term, the systems engineering approach to this research effort will contribute to the establishment of a robust, vetted set of tools that can be used for epidemic forecasting, prior to and during the next pandemic. This project will also support the training of graduate students.

The forecasting model will utilize an empirical machine learning approach that combines disparate data inputs into a meaningful predictive model using a combination of raw data and novel metrics generated in-house as inputs. The research team will explore, evaluate and compare the performance of different statistical methodologies for answering different proposed modeling objectives, in addition to developing new techniques to further improve predictive capabilities such as ensemble approaches and input clustering. Various combinations of methodologies and research objectives will be considered and optimized to find the best pairing. The team will make a concerted effort to continually validate the model based on observed data, and in response, continue to refine the model to both increase the accuracy of the predictions and infer the most important factors driving the outbreak, thus improving our general understanding of COVID-19 transmission risk. The proposed modeling effort will simultaneously build on the research team’s ongoing data collection effort that supports the JHU CSSE COVID-19 Dashboard and data set, and thus enable the team to further improve the quality of the data, as well as improve the communication, documentation, and management of the dataset, which has become the authoritative source of COVID-19 case and death data globally serves as the foundation for national and local level COVID-19 modeling conducted by dozens of research teams, governmental organizations and public health agencies around the world.

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.

National Science Foundation (NSF)
Division of Environmental Biology (DEB)
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Katharina Dittmar
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Johns Hopkins University
United States
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