Table 5. Final multivariable Cox regression model for time to infection of premises in the largest cluster (n = 3153), northwest of Sydney, during the 2007 equine influenza outbreak in Australia.
| Factor | Category | Hazard ratio | (95% CI) | P-valuea |
| Meteorological covariates | ||||
| Rainfall (mm day−1), t− 3 b | Linear | 0.91 | (0.82, 1.00) | 0.055 |
| Relative humidity (%), | Nonlinear spline | — | — | <0.001 |
| measured daily at 3pm, t− 5 b | ||||
| Maximum daily air | Nonlinear spline | — | — | <0.001 |
| temperature (°C), t−3 b | ||||
| Maximum daily wind speed, | Nonlinear spline | — | — | <0.001 |
| (km hour−1), t −3 b | ||||
| directed (k = 3)c | ||||
| Premises attributes | ||||
| Area (acres) | Nonlinear spline | — | — | <0.001 |
| Number of horses | >5 | 3.16 | (2.70, 3.69) | <0.001 |
| 3–5 | 2.19 | (1.89, 2.55) | ||
| 2 | 1.93 | (1.66, 2.26) | ||
| 1 | 1.00 | |||
| Length of shared fence | >300 | 1.30 | (1.15, 1.48) | <0.001 |
| with other horse premises (m) | 1–300 | 1.27 | (1.13, 1.43) | |
| 0 | 1.00 | |||
| Vaccination statusb | Yes | 0.28 | (0.04, 2.09) | 0.134 |
| No | 1.00 | |||
| Spatial covariates | ||||
| log10(Elevation (m)) | Linear | 0.58 | (0.51, 0.67) | <0.001 |
| Human population density | Nonlinear spline | — | — | <0.001 |
| (people km−2) |
Number of events = 1727; Log likelihood = −12,847.4; df = 25; P<0.001; R2 = 25.8%.
a
P-values derived from Likelihood ratio tests (LRT).
b
Time-changing covariate, time-lagged 3 or 5 days as noted.
c
Maximum daily wind speed (‘directed’) based on wind only from within 45° arcs centred on the direction of the three nearest infected premises assuming that premises were infectious for 14 days and one of the three nearest infective premises was the source of infection.