en Others Original Article <div style="text-align: justify;"><span style="font-size:12px;"><span style="font-family:Times New Roman;"><b>Background:</b> 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.<br> <b>Methods:</b> 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.<br> <b>Results:</b> 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.<br> <b>Conclusion</b>: 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.</span></span><br> &nbsp;</div> Surveillance, COVID-19, Coronavirus, SARS-CoV-2, 2019-nCoV, Severe acute respiratory syndrome 4 10 http://jhd.goums.ac.ir/browse.php?a_code=A-10-61-1&slc_lang=en&sid=1 Shahram Yazdani 1003194753284600284 1003194753284600284 No Virtual School of Medical Education and Management, Shahid Beheshti University of Medical Sciences, Tehran, Iran Zeynab Foroughi 1003194753284600285 1003194753284600285 No Department of Health Service Management, School of Health Management and Information Sciences, Iran University of Medical Sciences, Tehran, Iran Emad Karimian Rad emadkrad@gmail.com 1003194753284600286 1003194753284600286 Yes Department of Health Service Management, School of Health Management and Information Sciences, Iran University of Medical Sciences, Tehran, Iran Ali-Akbar Haghdoost 1003194753284600287 1003194753284600287 No Social Determinants of Health Research Center, Institute for Futures Studies in Health, Kerman, Iran Hadi Jabali 1003194753284600288 1003194753284600288 No Department of Health Service Management, School of Health Management and Information Sciences, Iran University of Medical Sciences, Tehran, Iran Alireza Hajikhani 1003194753284600289 1003194753284600289 No Faculty of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran Maryam Hoseini Abardeh 1003194753284600290 1003194753284600290 No National Agency for Strategic Research in Medical Education, Tehran. Iran