You are viewing the site in preview mode

Skip to main content

Advertisement

Table 5 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 0.67 0.42 0.92
S 1(r a i n(t)) β 1 4.39 4.18 4.61
S 2(r a i n(t)) β 2 0.92 0.85 0.99
Distance β 3 -0.19 -0.23 -0.15
Measurement error σ 0.71 0.70 0.71
Time to event model     
Age θ 1 0.01 -0.03 0.05
Gender θ 2 -0.05 -0.21 0.13
Association main effect α 1 0.12 0.04 0.19
Association interaction α 2 0.26 0.16 0.36
Hyper-parameters     
Penalty λ 0.004 0.002 0.008
Random effect covariance D 1,1 24.25 22.81 25.80
Random effect covariance D 2,1 0.95 0.62 1.28
Random effect covariance D 3,1 -0.73 -0.99 -0.47
Random effect covariance D 2,2 2.16 2.01 2.30
Random effect covariance D 3,2 0.81 0.72 0.91
Random effect covariance D 3,3 1.41 1.32 1.51
DIC 403463.5
  1. D i,j denote the ij-element of the covariance matrix for the random effects. Here we use a two week window to define the incidence I k(i)(t). Only rain is used as weather related covariate