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Estimation of COVID-19 case fatality ratio based on a bi-directional correction method
Wanling Hu; Xiaoyun Liu; Tao Wang; Changlong Zhou; Dingfu Liu; Yuanming Zhang; Zhongli Hu; Ying Diao.
Affiliation
  • Wanling Hu; Hubei Asset Management Co. Ltd.
  • Xiaoyun Liu; Wuhan Windoor Information Technology Co. Ltd.
  • Tao Wang; Wuhan University
  • Changlong Zhou; Yongchuan Hospital of Chongqing Medical University
  • Dingfu Liu; Hubei Academy of Agricultural Sciences
  • Yuanming Zhang; Huazhong Agricultural University
  • Zhongli Hu; Wuhan University
  • Ying Diao; Chongqing University of Arts and Sciences
Preprint in English | medRxiv | ID: ppmedrxiv-20089144
ABSTRACT
The case fatality rate (CFR) can be used to predict the number of potential deaths in the epidemic and thus can reflect the appropriateness and quality of medical measures developed by public health. When a new disease breaks out, it is particularly important to accurately estimate the CFR. However, while the epidemic is still developing, the crude CFR is often lower than the true value and the hospital CFR is often higher than the true value due to differences in occurrence time, patient number, and treatment plans. Therefore, this study proposes a bi-directional correction method to estimate the CFR. COVID-19 data from China were used to evaluate this method. The results show that this method provides more accurate results than both the crude CFR and hospital CFR. Additionally, this method was used to estimate the CFR of COVID-19 in other countries, with an aim to provide a reference for prevention and control decisions for the COVID-19 epidemic and for the evaluation of medical efforts.
License
cc_by_nc_nd
Full text: Available Collection: Preprints Database: medRxiv Type of study: Experimental_studies / Observational study / Prognostic study Language: English Year: 2020 Document type: Preprint
Full text: Available Collection: Preprints Database: medRxiv Type of study: Experimental_studies / Observational study / Prognostic study Language: English Year: 2020 Document type: Preprint
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