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Projecting COVID-19 Cases and Subsequent Hospital Burden in Ohio.

Wasiur R KhudaBukhshCaleb Deen BastianMatthew WascherColin KlausSaumya Yashmohini SahaiMark WeirEben KenahElisabeth RootJoseph H TienGrzegorz A Rempała
Published in: medRxiv : the preprint server for health sciences (2022)
We present a novel statistical approach called Dynamic Survival Analysis (DSA) to model an epidemic curve with incomplete data. The DSA approach is advantageous over standard statistical methods primarily because it does not require prior knowledge of the size of the susceptible population, the overall prevalence of the disease, and also the shape of the epidemic curve.The principal motivation behind the study was to obtain predictions of case counts of COVID-19 and the resulting hospital burden in the state of Ohio during the early phase of the pandemic.The proposed methodology was applied to the COVID-19 incidence data in the state of Ohio to support the Ohio Department of Health (ODH) and the Ohio Hospital Association (OHA) with predictions of hospital burden in each of the Hospital Catchment Areas (HCAs) of the state.
Keyphrases
  • coronavirus disease
  • healthcare
  • sars cov
  • risk factors
  • acute care
  • adverse drug
  • public health
  • machine learning
  • social media
  • climate change
  • data analysis
  • health information
  • drug induced