If you use a test with 95% sensitivity and 95% specificity, and it is positive then you are surly more likely sick than not, right? Well, ... it depends. On the prior probability to be sick (or prevalence) to be precise. And humans are really bad assessing the meaning of a test result.

Here is a bayes calculator to make this easer:

Prevalence Sensitivity Specificity Test positive? Likelihood to be infected:

Disclaimers

In the case of Covid-19 the incidence is often reported instead of the prevalence. While similar, that is a different measure. The prior (prevalence) is also modified if there is a specific reason for doing the test. I.e. the prevalence of Covid-19 in the total population is lower than the prevalence in the population of people with a cough.

Also keep in mind that some of the specificity and sensitivity labels provided by the tests themselves turned out to be false in independent testing. It is therefore advisable to use independent sensitivity estimates like the ones measured by the Paul-Ehrlich Institute. Lastly the sensitivity varies depending on the virus load.