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Table 3 Summary of Pandemic and Non-pandemic Model

From: Characterizing Influenza surveillance systems performance: application of a Bayesian hierarchical statistical model to Hong Kong surveillance data

Data Model
❖ Data as counts Y j,t Pois(λ j,t )
❖ Data as a proportion Log Y j , t N μ j , t , σ j 2
Process Model
❖ Pandemic Model
     ▪ Data as counts μ j,t  = θ j,t X t  + φ j,t
     ▪ Data as a proportion Log(λ j,t ) = θ j,t X t  + φ j,t
     ▪ “Completeness” θ j , t = β j , t , 1 + l = 2 M β j , t , l k l - 1 , θ , t p
     ▪ “Excess” φ j , t = a j , t , 1 + r = 2 N a j , t , r k r - 1 , φ , t p
❖ Non-pandemic Model
     ▪ Data as counts and as a proportion Log Y j , t N μ j , t , σ j 2
  μ j , t = P s , j , t , 1 + P s , j , t , 2 k 1 , t np + P s , j , t , 3 k 2 , t np + P s , j , t , 4 k 3 , t np
Parameter Model: non-informative priors
❖ Pandemic Model a j , t , r N μ a , σ a 2 ;r=1,....,N
  β j , t , l N μ β , σ β 2 ;r=1,....,M
τ1/ σ j 2 Gamma a , b
❖ Non-pandemic Model P s , j , t , n N μ p , σ p 2 ;n=1,2,3
τGamma(a, b)