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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