Incorporating variant frequencies data into short-term forecasting for COVID-19 cases and deaths in the USA: a deep learning approach.

Journal: EBioMedicine
Published Date: Feb 21, 2023

Abstract

BACKGROUND: Since the US reported its first COVID-19 case on January 21, 2020, the science community has been applying various techniques to forecast incident cases and deaths. To date, providing an accurate and robust forecast at a high spatial resolution has proved challenging, even in the short term.

Authors

  • Hongru Du
    Center for Systems Science and Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA; Department of Civil and Systems Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA.
  • Ensheng Dong
    Center for Systems Science and Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA; Department of Civil and Systems Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA.
  • Hamada S Badr
    Center for Systems Science and Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA; Department of Civil and Systems Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA; Department of Earth and Planetary Sciences, Johns Hopkins University, Baltimore, MD, 21218, USA.
  • Mary E Petrone
    Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, CT, 06510, USA.
  • Nathan D Grubaugh
    Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, CT, 06510, USA; Department of Ecology and Evolutionary Biology, Yale University, New Haven, CT, 06510, USA.
  • Lauren M Gardner
    School of Civil and Environment Engineering, UNSW Sydney, Sydney, NSW, Australia. l.gardner@jhu.edu.

Keywords

External Resources

Popular Topics
Recent Journals