Suppose we have a test for a disease. Assume that

(1) the sensitivity of the test is 99%, that is, the probability that the test is positive for a person with the disease is 0.99;

(2) the specificity of the test is 98%, that is, the probability that the test is negative for a person without the disease is 0.98;

(3) the prevalence of the disease is one in two hundred, that is, the probability of a random person carrying the disease is 0.005.

The probability that someone with a positive test actually has the disease is called the positive predictive value of the test. Express the positive and negative predictive value of the test using conditional probabilities and compute them using Bayes formula.