Award Abstract #2027072

RAPID: Infer and Control Global Spread of Corona-Virus with Graphical Models

See grant description on NSF site

Program Manager:

Sylvia Spengler

Active Dates:

Awarded Amount:



Michael Chertkov

Awardee Organization:

University of Arizona


Computer and Information Science and Engineering (CISE)


Graphs are ubiquitous in modeling statistical data-driven phenomena in physical, biological and societal systems. The spread of COVID-19, and other corona-viruses, between people, communities, cities, states and countries can be described in terms of a graph, which we refer to as the Corona Graph. Generally, the Corona Graphs we aim to utilize will be able to describe a specific region, level of spatio-temporal resolution, related geographical, transportation and social interaction details - for example, the city of Tucson, Arizona, during the summer of 2020, with a house-hold as an elementary unit, and accounting for businesses and their current state of isolation. This project goal is to build novel methodology which allows, given spatio-temporal specification and data, to (machine) learn the Corona Graph and underlying Corona Graphical Model, which will then be capable of interpolating, i.e. making societally important inference predictions about the virus spread. The essence of the RAPID proposal is in developing and transferring technology from the state-of-the-art in the foundational computer science, applied mathematics and statistics to the network modelling of COVID-19 spread. The technology developed under auspices of the project will allow to mix epidemiological inputs, such as these expressed in terms of the compartmental models of the epidemiology, with the state of the art approaches in AI and Data Science, such as Graphical Models and Deep Learning. The Corona Graphical Models will be flexible in dealing with heterogenous data sources, becoming available as the World recovers from the “hammer” stage of global self-isolation and transitions to the multi-month “dance” of balancing the conflicting objectives of keeping the reproduction rate of the virus under control while also minimizing the economic and societal costs. The technical aims of the project are divided into three thrusts, focused on the formulation, inference and learning, respectively, of the Corona Graphical Models. The formulation thrust pivots construction of a class of Graphical Models analyzed in the two other follow up thrusts by integrating epidemiology, transportation and other relevant considerations, variables and constraints. The, second, inference thrust strives to combine existing methodologies with novel methods and algorithms to answer questions such as, what is the complexity of computing marginal probabilities of observing a geographical area, including many nodes of the Corona Graph, to have density of immune population to be 10%? Selection of the Corona Graph and epidemiology-meaningful factors, such as frequency of inter-node interactions and efficiency of a node quarantine, will be learned in the third trust from available data, such as samples of the virus exposure collected at multiple nodes of the Corona Graph throughout a period of interest. On the technical level the project approach to modeling, learning and inference with Graphical Models will open doors to combining under one umbrella location- and time-specific data and information from epidemiology, transportation and social sciences. The project will also result in the dissemination of Graphical Model ideas, algorithms, data and benchmarks to the broader foundational AI community and also to multiple other research communities interested in adapting the novel application-informed Graphical Model methodology.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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