A pandemic risk assessment of middle east respiratory syndrome coronavirus (MERS-CoV) in Saudi Arabia

Eifan, Saleh A.; Nour, Islam; Hanif, Atif; Zamzam, Abdelrahman M.M.; AlJohani, Sameera Mohammed

the exponential growth rate occurrence during the early phase of an outbreak can be concomitant to the initial reproduction ratio. The incidence data is integer estimated and Poisson regression is specified to evaluate this parameter rather than linear regression of the logged incidence (Boelle et al., 2009; Hens et al., 2011) . ML method depends on the postulation that the number of secondary cases triggered by an index case is Poisson distributed with probable value R. In sequential bayesian method, the former distribution for R utilized on each new day is the subsequent distribution from the previous day. At each time, the mode of the posterior may be estimated along with the highest probability density interval. Similar to the previous methods, this method necessitates that the epidemic is occurring in a period of exponential growth (Obadia et al., 2012) . On the other hand, overestimation of the initial reproduction number may be obtained in a case when epidemic is not observed from the initial case, because some index cases are not present in the epidemic curve. In ML method this problem was solved by a correction made for absent generations at the beginning of the epidemic curve and similar is recommended in the Bayesian setting (McBryde et al., 2009 ) by assuming a constant reproduction number. However, it is not possible for the TD method due to variation of reproduction ratio with the time. Thus, the current study relied on Bayesian method to follow up the epidemic progress in Saudi Arabia and particularly in the absence of information around the index cases from two year data of MERS cases by assuming a random mixing in a fully susceptible population.