The novel coronavirus, COVID-19, has become one of the biggest pandemics in human history and has generated lasting impacts on public health, society, and economy. The number of cases in the United States has passed 1 million with a total number of deaths over 50 thousand. There is an urgent need for research and development that can bring a predictive understanding of the spread of the virus, thereby enabling mitigation methods to alleviate the negative effects of COVID-19. Traditional epidemiological models usually take into consideration only a small number of features in building a prediction model, which may not be able to capture potential risk factors and effects of various intervention mechanisms of this new pandemic. In this project the investigators develop novel machine learning methods that can simultaneously model and predict the COVID-19 spread, detect and monitor risk factors, and evaluate effectiveness of interventions over time and space.

The new model ingests and integrates heterogeneous and rapidly accumulating data across diverse sources, such as publications, news, census, social media, and outbreak observation trackers. It employs a new contextualized language model to accurately recognize named entities and relations from vast text data and build knowledge graphs to extract potential risk factors. A dynamic graph is constructed. Each location node may have a set of static and time-dependent attributes. Events, individual behaviors, social activities, interventions are mapped to activity nodes with edges connecting to the corresponding location nodes at the time. A novel dynamic graph neural network is trained to perform joint predictions of all locations over time. Activity nodes of significant attention weights represent major risk factors or effective intervention mechanisms. The project will result in public dissemination of the prediction model and all source codes, immediately benefiting the combat against COVID-19.

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.

Project Start
Project End
Budget Start
2020-06-01
Budget End
2021-05-31
Support Year
Fiscal Year
2020
Total Cost
$90,000
Indirect Cost
Name
University of California Los Angeles
Department
Type
DUNS #
City
Los Angeles
State
CA
Country
United States
Zip Code
90095