Background Since December 2019, the novel coronavirus disease (COVID-19) has rapidly spread around the world leading to a pandemic with more than 3,000,000 infected people and more than 200,000 death. Several case definitions have been released and revised by countries and organizations. However, collectivization of case definitions has not been fully investigated.
Methods In this study, we rapidly reviewed existing COVID-19 case definitions, finally a dynamic case definition algorithm was provided by using Bayesian theorem models of diagnosis.
Results Our results showed categorization as suspected, probable, and confirmed cases, is used in majority of case definitions. Furthermore, the criteria for suspected cases and laboratory testing priority was a point of argument. Due to pandemic situation and resource limitation, diagnostic tests were rationed and mainly administered to a selected population, thus it was shown that the fraction of positive test results does not reflect the total infection rate of the population. Case definitions for COVID-19 is 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 helps us in 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.