Study and prediction of the case-fatality rate (CRF) of COVID-19 based on patient’s medical information referred to Dr. Masih Daneshvari Hospital in Tehran

Kazempour Dizaji, Mehdi and Zare, Ali and Tabarsi, Payam (2022) Study and prediction of the case-fatality rate (CRF) of COVID-19 based on patient’s medical information referred to Dr. Masih Daneshvari Hospital in Tehran. Health Science Monitor, 1 (2).

[thumbnail of 8 Kazempour Dizaji A-10-76-4.pdf] Text
8 Kazempour Dizaji A-10-76-4.pdf

Download (538kB)

Abstract

Background & Aims: Coronavirus disease 2019 (COVID-19) is an acute respiratory syndrome that despite global health efforts to
prevent its spread, it still has high fatality rates in many countries.
Materials & Methods: Based on the medical information of 4,372 COVID-19 patients referring to Dr. Masih Daneshvari Hospital in
Tehran, Iran, the case-fatality rate (CFR) for COVID-19 was calculated, and the trend of this index was assessed using the artificial
neural network (ANN) model.
Results: In this study, the CFR for COVID-19 reduced by an average of 0.4% per day and reached 4.43% during 50 days of the
epidemic onset. Predicting the daily trend of this index using ANN model also showed a very gentle downward trend. According to
the prediction of this model, during the first 100 days and also the second 100 days from the COVID-19 epidemic onset, the CFR for
this disease decreased by an average of 0.03% and 0.01% per day, and reached 3.87% and 3.05%, respectively,
Conclusion: The use of CFR for COVID-19 and prediction of the trend of this index for the future can provide valuable information
on the diagnosis of the disease severity and evaluation of the effectiveness of control and treatment strategies, as well as assessment
of the health care

Item Type: Article
Uncontrolled Keywords: Coronavirus, Covide-19, Case-fatality rate (CFR), Artificial neural networks (ANNs), Prediction
Subjects: R Medicine > R Medicine (General)
Depositing User: Unnamed user with email gholipour.s@umsu.ac.ir
Date Deposited: 14 Nov 2023 09:57
Last Modified: 14 Nov 2023 09:57
URI: https://eprints.umsu.ac.ir/id/eprint/7181

Actions (login required)

View Item
View Item