Table 2.

Results for Bayesian hierarchical spatial negative binomial models for SA2-weeks (n = 178,464) and SA3-weeks (n = 28,392)

	Model 1: SA2-weeks			Model 2: SA3-weeks
	IRR	(95%	CrI)	IRR	(95%	CrI)
Local checkpoints per 500 roadway kms
Lag 0 *	0.975	0.973	0.978	0.945	0.937	0.953
Lag 1 *	0.996	0.992	1.000	0.995	0.984	1.006
Lag 2 *	0.995	0.991	0.999	0.981	0.970	0.992
Adjacent checkpoints per 500 roadway kms
Lag 0 *	0.981	0.971	0.991	0.942	0.912	0.973
Lag 1 *	0.994	0.983	1.005	0.969	0.937	1.003
Lag 2 *	0.981	0.970	0.992	0.949	0.917	0.982
Temporal structure
Timepoint *	0.690	0.671	0.710	0.714	0.692	0.736
Timepoint squared *	1.458	1.417	1.501	1.409	1.365	1.454
Cosine (annual)	1.008	0.998	1.018	1.006	0.996	1.017
Sine (annual)	0.989	0.979	0.999	0.994	0.983	1.005
Space-time varying covariates
Total rainfall (per 100mm increase)	1.112	1.094	1.131	1.106	1.085	1.126
Maximal temperature (per 10°C increase)	1.090	1.068	1.112	1.100	1.076	1.124
Overdispersion	0.490	0.427	0.563	0.190	0.184	0.197
ICAR variance	0.576	0.563	0.590	0.193	0.143	0.270

^*centered and scaled before model fitting

CI: credible interval