Are 'cases', 'waves', 'tests' and 'modeling' deceiving indicators to describe the COVID-19 pandemic?

This commentary elaborates on different methodological aspects complicating the interpretation of epidemiological data related to the current COVID-19 pandemic, thus preventing reliable within and across-country estimates. Firstly, an inaccuracy of epidemiological data maybe arguably be attributed to passive surveillance, a relatively long incubation period during which infected individuals can still shed high loads of virus into the surrounding environment and the very high proportion of cases not even developing signs and/or symptoms of COVID-19. The latter is also the major reason for the inappropriateness of the abused “wave” wording, which gives the idea that health system starts from scratch to respond between “peaks”. Clinical data for case-management on the other hand often requires complex technology in order to merge and clean data from health care facilities. Decision-making is often further derailed by the overuse of epidemiological modeling: precise aspects related to transmissibility, clinical course of COVID-19 and effectiveness of the public health and social measures are heavily influenced by unbeknownst and unpredictable human behaviors and modelers try to overcome missing epidemiological information by relying on poorly precise or questionable assumptions. Therefore the COVID-9 pandemic may provide a valuable opportunity to rethink how we are dealing with the very basic principles of epidemiology as well as risk communication issues related to such an unprecedented emergency situation.