NSF RAPID: Interventional COVID-19 Response Forecasting in Local Communities Using Neural Domain Adaptation Models |
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PIs Xifeng Yan, University of California at Santa Barbara Yu-Xiang Wang, University of California at Santa Barbara |
Project Summary Products |
Funded by NSF IIS 2029626.
This material is based upon work supported by the National
Science Foundation under Grant No. 2029626. Any opinions, findings, and
conclusions or recommendations expressed in this material are those of the
author(s) and do not necessarily reflect the views of the National Science
Foundation.
There is still much we do not understand about the spread of COVID-19, and how
our mitigation strategies are affecting the spread. Demography, population
density, business structure, and social culture differ across regions.
Correlating these local factors with the number of infections and the
availability of hospital resources can provide precious scientific and
data-driven guidance to local policy makers. Different from existing, classic
epidemic models, in this project we aim to build novel forecasting models based
on cutting-edge AI techniques. The goal is to provide timely, localized
information needed by administrators for strategic allocation of resources and
planning towards reopening business. One key advantage of our approach is that
it is able to combine the data from regions with more COVID-19 cases with the US
Census microdata that characterize each local community, hence helping us to
make fine-grained predictions of the localized effects of a policy decision.
Existing simulation models for COVID-19 cases forecasting either ignore the
fine-grained demographical, social and cultural difference at local communities,
or often require complicated, manual parameter setting for estimating the effect
of interventions. Existing statistical models, on the other hand, require
substantial amount of data to be available, hence are not able to obtain
sufficiently confident predictions on each local level. We propose a
fundamentally different approach that is built on the newest neural network
models like Transformers to overcome these weaknesses. The proposed approach
performs domain adaption and few shot learning, so that knowledge learned from
other regions can be adapted to local communities even when only a few data
points are available. Specifically, our approach will creatively draw
information from the US Census American Community Survey data, COVID-19 related
data from other regions at home and abroad, as well as other related kinds of
epidemics under the clinical guidance of our collaborators from the Santa
Barbara Cottage Hospital.
Talks