Covid test

simplistic/explanatory examples in another answer ("Bayes Covid")
*Sensitivity* impacts false negatives and *Specificity* impacts false positives
Sensitivity is 63% for a nasopharyngeal swab and 31% for a throat swab, meaning
a false sense of security can result from you "passing" your Covid test.
With little *precedence* we cant “rule out illness” given a negative test result.
(NPV) is LOW & the probability of being free of disease is UNDETERMINED.

TO UNDERSTAND THE ISSUE AT A HIGH LEVEL, TWO SIMPLISTIC EXAMPLES...
EX1: Of 1000 ASYMPTOMATIC...
if rapidTest *sensitivity* is 80%... 20% will have false negatives
assume 1% of pop are positive ( @ 2% - false positives)
if rapidTest *specificity* is 100% all infection-free would test negative)
from the pre-test probability (1%) 
 10 in 1000 will have it, 990 will not
 considering the 80% sensitivity  
  8 will test positive,  
  2 will test negative (false negatives)
Of the 990,  100% test negative ( *specificity* is 100%) 

 low pre-test probability (10–20%)...
	asymptomatic individuals in a presumed low prevalence environment 
moderate pre-test probability (40–60%)
	cough and fever in a city/jurisdiction with known cases
high pre-test probability (80–90%) 
	may include a patient with fever, cough, shortness of breath, 
	with a known close contact with confirmed COVID19.