EAGER: Epidemic Spread Modeling Using Hard Data

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Data-driven prediction models of the spread of COVID-19 are critical for guiding public health policy. Epidemiological models that use as input data in aggregated form can be used for prediction but the granularity of input can limit model usability. Models that are individual-centric are a lot more flexible but require as input the time series of every person's movement within a population: the exact location of each individual, the duration of the individual's stay at the location, and the transition to the next location. Due to privacy issues, accurate data of such granularity are not publicly available. The focus of this project is on the development of a prediction ecosystem that is individual-centric and can be used to foresee the spread of a highly contagious disease within a population that is active within an urban area. Such a model can be used to develop what-if scenarios to mitigate the spread of the disease and can become an indispensable tool for guiding policy decisions in future pandemics. This project will provide a flexible tool for epidemic modeling of COVID-19 and future pandemics. This project advocates the usage of agent-based models as an alternative to machine-learning for accurate prediction of the spread of contagious diseases. The aim is to create a prediction ecosystem for evaluating detailed scenarios: geographical restrictions of mobility, work from home orders/advisories, school closures (and partial openings under different conditions), points of interest operating under various capacities, time in quarantine, and vaccination priority, among others. The above scenarios can be modeled at various levels of detail with the aim to keep the model input small, compact, and flexible, but without compromising its prediction ability. Analysis of the above within the agent-based model setting identifies the most effective yet feasible input abstractions, similar to identifying the importance of feature selection in machine learning models. This tool, driven by anonymized cell-phone data will provide a robust modeling ecosystem that captures the effect of mitigation measures of contagious diseases using stochastic models that are complementary to machine-learning ones. Through this project, undergraduate and graduate students will be trained in the art of applied data science.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.