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Download the following article from the link provided below. Read that article and answer the following questions. This article provides an application of Bayesian modelling on COVID-19 datasets. Xiang Gao, Qunfeng Dong, A primer on Bayesian estimation of prevalence of COVID-19 patient outcomes, JAMIA Open, Volume 3, Issue 4, December 2020, Pages 628-631, DOI: https://doi.org/10.1093/jamiaopen/ooaa062 Web Link: https://academic.oup.com/jamiaopen/article/3/4/628/5970479Table 1. Bayesian analysis of two published COVID-19 data sets Study Age groups (years old) Death (y) Infection (N) Prior Beta(a, b) Posterior Beta(a+y, b+N-y) Posterior median (95% credible interval) (%) Infection fatality rates in Iceland 0-70 3 3012 Beta(1) Beta(4, 3010) 0.12 (0.04-0.29) 70-80 3 128 Beta(1) Beta(4, 126) 2.84 (0.85-6.65) >80 4 38 Beta (1) Beta(5, 35) 11.87 (4.30-24.22) Study Regions ASX (y) Infection (N) Prior Beta(a, b) Posterior Beta(aty, b+N-y) Posterior median (95% credible interval), % Asymptomatic (ASX) children in U.S. West 120 15311 Beta(1) Beta(121, 15192) 0.79 (0.66 - 0.94) Midwest 40 5217 Beta(1) Beta(41, 5178) 0.78 (0.56 - 1.04) South 49 8354 Beta(1) Beta (50, 8306) 0.59 (0.44 - 0.78) Northeast 41 4159 Beta(1) Beta(42, 4119) 1.00 (0.73 - 1.33)Total cases by location Location Active Recovered Deceased Total New cases in the last week Auckland 901 217111 179 218191 900 Bay of Plenty 592 101877 130 102599 592 Canterbury 2668 298942 386 301996 2672 Capital and Coast 823 160208 122 161153 824 Counties Manukau 1121 260672 209 262002 1126 Hawke's Bay 361 77308 127 77796 365 Hutt Valley 506 78419 77 79002 505 Lakes 270 45766 81 46117 271 Mid Central 565 83698 148 84411 572 Nelson Marlborough 533 68338 94 68965 538 Northland 475 70307 91 70873 475 South Canterbury 330 29620 33 29983 331 Southern 1456 166674 224 168354 1465 Tairawhiti 117 23265 31 23413 123 Taranaki 449 57092 106 57647 452 Unknown 17 1824 3 1844 12 Waikato 1214 180400 266 181880 1211 Wairarapa 169 22012 50 22231 168 Waitemata 1366 269233 280 270879 1370 West Coast 116 13897 15 14028 117 Whanganui 153 29826 58 30037 153 At the Border* 0 27289 6 27295 NA Total 14202 2283778 2716 2300696 14242Let a, y, and N denote the unknown prevalence, the observed number of medical outcomes of interest I(eg, the number of death or asymptomatic infection), and the total sample size, respectively. The mathematical relationship among 6, y, and N can be described with the following binomial likelihood function:6 y m Binomial {9, N) [1) In Eq. (1), only a? is the unknown parameter, whose possible values are typically modeled using a beta probability distribution:6 9N Beta (o,b} [2) The beta distribution in Eq. (2) has two shape parameters, a and b, whose values represent different degrees of prior knowledge or belief on the likely values of a. In (SQUID19 studiesJ researchers are typically faced with no prior data to derive informative prior probability distributions. In that case, both a and b can be set to 1 as a flat noninforrnative prior distribution for 6, which essentially means that a has an equal chance to be any value between 0 and 100%