NSF RAPID: Interventional COVID-19 Response Forecasting in Local Communities Using Neural Domain Adaptation Models
Xifeng Yan, University of California at Santa Barbara
Yu-Xiang Wang, University of California at Santa Barbara
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
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.