May I please get some assistance with the following:

We test an individual from the general population and the results of the test are \"positive.\" We now need to know whether or not the positive result has a high probability of being accurate. What we are asking is: What is the probability that the individual has the disease given a positive test result. Assume the test is symmetric (positive and negative) and 95% accurate* and that the disease affects 10% of the general population. Using the binary channel model a. Calculate the probability that a positive test result means that the person has the disease. b. Repeat the calculation for a rare disease that affects 1% of the population. c. Are you surprised by the results? * Be carefai here. The ward 'aeearate \" was defined for inputs with KNO WN attributes, notfor the generai population. Problem 3. Screening the Population for Covid-19 Consider a screening test for a disease such as Covid-19. The test developer calibrates the test by taking samples from individuals that are given that they have the disease. How do we know that? Because they are symptomatic fever, cough, difficulty breathing. Samples from these individuals are then tested, a \"positive\" test meaning that the test worked, and a \"negative\" result is an error called a \"false negative.\" Similarly, individuals are tested that are known to not have the disease. How do we know that? Not only are they symptom-free, but they have been isolated for several weeks. Or there may be samples stored from people who were sampled before the disease existed. These samples, given that there is no disease, are put to the test and a \"negative\" result is expected and good. But because no test is 100% accurate, some of these known negatives will test positive and these are \"false positives.\" Now we wish to use the test in the general population where we do not know whether or not the individual being tested is positive or negative for the disease