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Table 2 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

0.66

0.40

0.91

S 1(r a i n(t))

β 1

4.40

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

-0.12

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

-0.04

-0.21

0.12

Association main effect

α 1

0.12

0.04

0.19

Association interaction

α 2

0.27

0.16

0.37

Hyper-parameters

    

Penalty

λ

0.005

0.002

0.008

Random effect covariance

D 1,1

24.22

22.78

25.77

Random effect covariance

D 2,1

0.94

0.60

1.27

Random effect covariance

D 3,1

-0.73

-0.99

-0.47

Random effect covariance

D 2,2

2.16

2.01

2.31

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

403478.6

  1. D i,j denote the ij-element of the covariance matrix for the random effects. Here rain is the only weather related covariate