Diagnosis from an Epidemiological Point of View. The Example of COVID-19

A diagnostic test is anything that provides data about the patient's health or disease. All such tests can be formally evaluated for their accuracy and precision (sensitivity and specificity). Once a test has been performed, whether positive or negative, sensitivity and specificity are not the priority issues; the physician's dilemma is whether or not the patient has the disease, once the test result is known. This is the positive and negative predictive value. However, the positive predictive value depends on the prevalence: when prevalence is high, the positive predictive value of the test increases and, as a consequence, there are fewer false positives and more false negatives. When prevalence is low, the opposite occurs: the positive predictive value of the test decreases, and there will be more false positives and fewer false negatives. Furthermore, diagnoses are generally not determined and labeled in isolation; rather, diagnoses are applied sequentially (Bayesian inference). Thus, prior knowledge facilitates rapid decision-making that is generally correct by increasing the pre-test diagnostic probability. When tests are performed in series (one after the other), specificity and positive predictive value are maximized, but sensitivity and negative predictive value are reduced. Thus, we have the example of COVID-19. For a low prevalence scenario (such as the current one), assuming a specificity of 98%, a positive test ensures the diagnosis in at least 2/3 of patients. And a negative result practically rules it out. When the incidence of COVID-19 is high. For example, with a prevalence of 30% or in practice when we are in some "COVID-19 wave", a positive result ensures the diagnosis in 94% but a negative result can occur in up to 10% of patients. On the other hand, a second test after the first gives a more reliable result if its results go in the same direction.