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
Project Summary

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

  1. Time Series as Images: Vision Transformer for Irregularly Sampled Time Series
    by Z. Li, S. Li, X. Yan, 2023 [arxiv]
  2. Improving Medical Predictions by Irregular Multimodal Electronic Health Records Modeling
    by X. Zhang, S. Li, Z. Chen, X. Yan, L. Petzold, 2022 [arxiv]
    (The Fortieth International Conference on Machine Learning)
  3. Modern Machine Learning in Time Series Forecasting
    by Xiaoyong Jin.
    Dissertation 2022. [pdf]
  4. An Optimal Reduction of TV-Denoising to Adaptive Online Learning
    by D. Baby, X. Zhao, Y.-X. Wang.
    AISTATS 2021. [arxiv]
  5. Inter-Series Attention Model for COVID-19 Forecasting
    by X. Jin, Y-X Wang, X. Yan,
    SIAM Data Mining 2021
  6. Adaptive Online Estimation of Piecewise Polynomial Trends
    by D. Baby, Y.-X. Wang.
    NeurIPS 2020. [arxiv]
  7. 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]
  8. 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.
  9. 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
  10. COVID-19 Trend Forecasting Using Neural Models
    by X. Yan,
    Covid-19 Seminar Talk at UCSB [link
    ] [slides][youtube]
  11. Coping with Heterogeneity and Uncertainty of COVID-19 Datasets
    by Y.-X. Wang,
    Covid-19 Seminar Talk at UCSB [link][slides]


  1. 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)
  2. 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)
  3. "History Repeats Itself: COVID-19 Forecasting," Weekly CDC/MIDAS conference call, Nov 17, 2020  [30 minutes talk pdf]
  4. 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,
  5. "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 2020
  6. South Coast Researchers Develop New Forecast Model Using Artificial Intelligence,
    KCLU | NPR for the California coast
    by Andy Vasoyan @ KCLU/NPR [News Link][mp3], Aug 2020
  7. COVID-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
  8. Localizing COVID-19 Forecasts with Artificial Intelligence
    UC Santa Barbara Engineering
    by Andrew Masuda ,
    [News Link] July 2020