One (5 Marks) 1. (5 Marks) During week 1, we used Bayes' Theorem to calculate the probability that someone who tested positive for COVID-19 actually had the virus. Here is the equation and data that we used for that calculation: P(Cov|+) = P(+/Cov)P(Cov) P(+/Cov)P(Cov) + P(+/DF)P(DF) Where P(+/Cov) = 0.95 is the probability of testing positive for COVID-19 given that you test positive. P(Cov) = .01 is the probability that a citizen drawn at random from the population has COVID-19. P(+/DF) = .01 is the probability that someone testing positive is actually disease free (false positive) P(DF) = .99 is the probability that a citizen drawn at random from the population is disease free. Using these values we calculated P(Cov/+) = 0.49. Assumming that this same individual who tested positive the first time, took a second test that also came back positive. What is the revised probability that they actually have COVID-19