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Table 1 Parameter estimates and 95% credible intervals for the joint model

From: Joint Bayesian modeling of time to malaria and mosquito abundance in Ethiopia

  Parameter Posterior mean 2.5% 97.5%
Abundance model     
Intercept β 0 3.12 2.89 3.36
S 1(r a i n(t)) β 1 5.99 5.72 6.26
S 2(r a i n(t)) β 2 0.67 0.59 0.74
Distance β 3 -0.13 -0.17 -0.11
Temperature β 4 -0.15 -0.16 -0.15
Relative humidity β 5 0.0004 0.0001 0.0006
Corrugate roof β 6 0.05 -0.07 0.17
Measurement error σ 0.69 0.69 0.70
Time to event model     
Age θ 1 0.01 -0.03 0.05
Gender θ 2 -0.05 -0.21 0.12
Association main effect α 1 0.14 0.06 0.21
Association interaction α 2 0.31 0.20 0.41
Hyper-parameters     
Penalty λ 0.0031 0.0014 0.0055
Random effect covariance D 1,1 26.96 25.37 28.70
Random effect covariance D 2,1 0.75 0.35 1.16
Random effect covariance D 3,1 -0.80 -1.07 -0.53
Random effect covariance D 2,2 3.05 2.84 3.27
Random effect covariance D 3,2 0.82 0.71 0.93
Random effect covariance D 3,3 1.40 1.31 1.50
DIC 398866.1
  1. D i,j denotes the ij-element of the covariance matrix for the random effects. We use a three week window to define the incidence I k(i)(t)