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IJHMD 2024, 1(2): 4-10 |
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Yazdani S, Foroughi Z, Karimian Rad E, Haghdoost A, Jabali H, Hajikhani A et al . A Bayesian approach model to COVID-19 case definitions. IJHMD 2024; 1 (2) :4-10
URL: http://jhd.goums.ac.ir/article-1-44-en.html
URL: http://jhd.goums.ac.ir/article-1-44-en.html
Shahram Yazdani1 
, Zeynab Foroughi2 , Emad Karimian Rad *3 , Ali-Akbar Haghdoost4 , Hadi Jabali2 , Alireza Hajikhani5 , Maryam Hoseini Abardeh6
, Zeynab Foroughi2 , Emad Karimian Rad *3 , Ali-Akbar Haghdoost4 , Hadi Jabali2 , Alireza Hajikhani5 , Maryam Hoseini Abardeh6
1- Virtual School of Medical Education and Management, Shahid Beheshti University of Medical Sciences, Tehran, Iran
2- Department of Health Service Management, School of Health Management and Information Sciences, Iran University of Medical Sciences, Tehran, Iran
3- Department of Health Service Management, School of Health Management and Information Sciences, Iran University of Medical Sciences, Tehran, Iran ,emadkrad@gmail.com
4- Social Determinants of Health Research Center, Institute for Futures Studies in Health, Kerman, Iran
5- Faculty of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
6- National Agency for Strategic Research in Medical Education, Tehran. Iran
2- Department of Health Service Management, School of Health Management and Information Sciences, Iran University of Medical Sciences, Tehran, Iran
3- Department of Health Service Management, School of Health Management and Information Sciences, Iran University of Medical Sciences, Tehran, Iran ,
4- Social Determinants of Health Research Center, Institute for Futures Studies in Health, Kerman, Iran
5- Faculty of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
6- National Agency for Strategic Research in Medical Education, Tehran. Iran
Abstract: (566 Views)
Background: Since December 2019, the novel coronavirus disease (COVID-19) has rapidly spread worldwide and caused a pandemic. Several case definitions have been released and revised by countries and organizations. However, the collectivization of case definitions has not been investigated.
Methods: The study was conducted in two phases. In the first phase, two review were conducted to detect current COVID-19 case definitions and their influential epidemiological features in the case definition. In the second phase, a dynamic case definition algorithm was applied using the Bayesian theorem models of diagnosis to represent case definitions.
Results: Our results showed categorization as suspected, probable, and confirmed cases, which is used in the majority of case definitions. Furthermore, the criterion for suspected cases and laboratory testing priority was a point of argument. Due to the pandemic situation and resource limitations, diagnostic tests were rationed and mainly administered to a selected population, thus it was shown that the fraction of positive test results do not reflect the total infection rate of the population. Case definitions for COVID-19 are changing as we learn more about the disease. RT-PCR and CT scan of lung seem to be beneficial in COVID-19 diagnosis and combing them with epidemiological criteria help us a better understanding of the disease.
Conclusion: Based on our results, in the current case definitions, only symptomatic patients categorized and tested as a susceptible case. While the majority of COVID-19 cases are asymptomatic carriers of the disease, thus making the prevention more challenging. Dynamic statistical models can provide new insights into surveillance systems.
Methods: The study was conducted in two phases. In the first phase, two review were conducted to detect current COVID-19 case definitions and their influential epidemiological features in the case definition. In the second phase, a dynamic case definition algorithm was applied using the Bayesian theorem models of diagnosis to represent case definitions.
Results: Our results showed categorization as suspected, probable, and confirmed cases, which is used in the majority of case definitions. Furthermore, the criterion for suspected cases and laboratory testing priority was a point of argument. Due to the pandemic situation and resource limitations, diagnostic tests were rationed and mainly administered to a selected population, thus it was shown that the fraction of positive test results do not reflect the total infection rate of the population. Case definitions for COVID-19 are changing as we learn more about the disease. RT-PCR and CT scan of lung seem to be beneficial in COVID-19 diagnosis and combing them with epidemiological criteria help us a better understanding of the disease.
Conclusion: Based on our results, in the current case definitions, only symptomatic patients categorized and tested as a susceptible case. While the majority of COVID-19 cases are asymptomatic carriers of the disease, thus making the prevention more challenging. Dynamic statistical models can provide new insights into surveillance systems.
Keywords: Surveillance, COVID-19, Coronavirus, SARS-CoV-2, 2019-nCoV, Severe acute respiratory syndrome
Type of Study: Original Article |
Subject:
Others
Received: 2022/03/12 | Accepted: 2022/05/29 | Published: 2024/07/9
Received: 2022/03/12 | Accepted: 2022/05/29 | Published: 2024/07/9
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