Because there are no false negatives, the negative predictive value is 100%
This isn’t as good as 99.8% and 100%, but it’s still pretty good. We can be pretty confident in both positive and negative tests. Most important in this case are positive tests since they might be used to decide if someone is safe to return to work or school, or to come out of shielding. However, around 4% (1 in 25) of positive tests may be false positives. It is important that people receiving the tests are aware of this.
How does this change for a different prevalence? If the prevalence is 10%, rather than 5%, then the situation is somewhat better. Because a higher proportion of the population is now expected to be positive, the number of true positives increases, while the number of false positives goes down a bit.
Our positive predictive value goes up to 98.2%, while our negative predictive value is still 100%
Even if we don’t trust the 100% sensitivity value, it’s likely to be quite high. Because there are so many more true negatives than the occasional false negative, even if we drop the sensitivity to 99%, there is no real effect on the negative predictive value – at worst it drops to 99.9%.
