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Table 4 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 3.28 3.04 3.52
S 1(r a i n(t)) β 1 6 5.73 6.27
S 2(r a i n(t)) β 2 0.67 0.59 0.74
Distance β 3 -0.19 -0.20 -0.17
Temperature β 4 -0.15 -0.16 -0.15
Relative humidity β 5 0.0004 0.0002 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 -0.04 -0.21 0.12
Association main effect α 1 0.14 0.07 0.21
Association interaction α 2 0.31 0.20 0.41
Hyper-parameters     
Penalty λ 0.0038 0.0017 0.0069
Random effect covariance D 1,1 27.10 25.46 28.82
Random effect covariance D 2,1 0.92 0.51 1.33
Random effect covariance D 3,1 -0.82 -1.10 -0.55
Random effect covariance D 2,2 3.05 2.85 3.27
Random effect covariance D 3,2 0.81 0.71 0.92
Random effect covariance D 3,3 1.40 1.31 1.50
DIC 398876
  1. D i,j denotes the ij-element of the covariance matrix for the random effects. We use a two week window to define the incidence I k(i)(t)