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.
Project Summary
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.
Research Results
-
Time Series as Images: Vision Transformer for Irregularly Sampled Time
Series
by Z. Li, S. Li, X. Yan, 2023 [arxiv] -
Improving Medical Predictions by Irregular Multimodal Electronic
Health Records Modeling
by X. Zhang, S. Li, Z. Chen, X. Yan, L. Petzold,
2022
[arxiv]
ICML'23
(The Fortieth International Conference on Machine Learning) -
Modern Machine Learning
in Time Series Forecasting
by Xiaoyong Jin.
Dissertation 2022. [pdf] -
An Optimal Reduction
of TV-Denoising to Adaptive Online Learning
by D. Baby, X.
Zhao, Y.-X. Wang.
AISTATS 2021. [arxiv] -
Inter-Series Attention Model
for COVID-19 Forecasting
by X. Jin,
Y-X Wang, X. Yan,
SIAM Data Mining 2021 [arxiv]
- Adaptive Online Estimation of Piecewise Polynomial Trends
by
D. Baby, Y.-X. Wang.
NeurIPS 2020. [arxiv] -
Domain Adaptation with Conditional Distribution Matching and
Generalized Label Shift
by Remi Tachet des Combes, Han Zhao, Yu-Xiang
Wang, Geoff Gordon.
NeurIPS 2020. [arxiv] - COVID-19 Trend Forecasting Using Neural Models
by X. Yan, selected as a lightning talk to the
CRA Virtual Conference [url][mp4].
About the CRA Conference: It is for chairs of departments of Computer
Science, Computer Engineering, and Information Technology, and leaders from
U.S. industrial and government computing research laboratories and centers
interested in computing research issues. - Tracking and
forecasting the
COVID-19 cases in Santa Barbara county to increase the public awareness of
the virus.
by H. Zha,
X. Yan,
[Website
Link] 2020- COVID-19 Trend Forecasting Using Neural Models
by X. Yan,
Covid-19 Seminar Talk at UCSB
[link ]
[slides][youtube]- Coping with Heterogeneity and Uncertainty of COVID-19 Datasets
by Y.-X.
Wang,
Covid-19 Seminar Talk
at UCSB
[link][slides] [youtube]
Talks
-
COVID-19 Research Lightning Talks: "Interventional COVID-19 Response
Forecasting in Local Communities Using Neural Domain Adaptation Models." Webinar and Q&A
English DOI: https://doi.org/10.7916/98me-fq27
,
Spanish DOI: https://doi.org/10.7916/pd0p-bt51
(Spanish version)Daily COVID-19 Hospitalization Forecasting for
all the counties in California, requested by California Department of
Public Health
by X. Jin, Y.-X.
Wang, X. Yan [forecasting]
[source code]
Dec 2020 (updated every Sunday, Tuesday and Thursday night)
"History Repeats Itself: COVID-19 Forecasting,"
Weekly CDC/MIDAS
conference call, Nov 17, 2020 [30
minutes talk pdf]
CDC Release: Daily New Cases, Hospitalizations,
and Deaths Forecasts
by X. Jin,
X. Yan, [github] [arxiv]
Oct 2020
CDC Daily New Cases Forecasts,
CDC Hospitalizations Forecasts,
CDC
Deaths Forecasts,
"Achieving State-of-the-Art Performance in
COVID-19 Hospitalization Forecasting,"
2020 Responsible Machine Learning Summit | Center of
Responsible Machine Learning, UCSB
by Xifeng Yan [Abstract][Talk
Link],
Oct 2020South Coast Researchers Develop
New Forecast Model Using Artificial Intelligence,
KCLU | NPR for
the California coast
by Andy Vasoyan @ KCLU/NPR [News
Link][mp3],
Aug 2020COVID-19 Forecasts by AI: Project provides a deep
learning tool to forecast COVID-19 trends by community
UC
Santa Barbara The Current News
by Andrew Masuda ,
[News
Link] July 2020
Localizing COVID-19 Forecasts with Artificial
Intelligence
UC Santa Barbara Engineering
by Andrew Masuda ,
[News
Link] July 2020