| Model1 | Model2 | Model3 | Model4 | ModelC |
---|
Auto-regression
| Â | Â | Â | Â | Â |
1st order | 0.64* | Â | Â | Â | 0.31* |
2nd order | Â | Â | 0.61* | Â | Â |
Precipitation
| Â | Â | Â | Â | Â |
prcp3wk (3 wk lag) | Â | 0.35* | Â | Â | Â |
prcp3wk (4 wk lag) | Â | Â | Â | 0.43* | Â |
prcp5wk (11 wk lag) | Â | Â | Â | Â | Â |
prcp_annual (prior year) | -0.78** | -1.57* | -1.30* | -1.89* | Â |
Temperature
| Â | Â | Â | Â | Â |
DW (1 wk lag) | 0.16* | 0.42* | Â | Â | Â |
DW (4 wk lag) | Â | Â | 0.21* | 0.59* | Â |
DWC (1 wk lag) | Â | Â | Â | Â | -0.08* |
R
2
| 0.80 | 0.70 | 0.65 | 0.58 | 0.42 |
- Model 1 and Model 2 measured the effect of weather on mosquito WNv Minimum Infection Rate (MIR) and the best statistical models first with and then without an autoregressive term (AR) for MIR. Models 3 and 4 are less robust statistically but estimate MIR using weather conditions at earlier points in time to provide forecasting. The Model C models the cooling period, after amplification and includes only one option of variables (Additional File 2: Temporal, Part C includes the full equation for each model).
- *p-value < 0.05
- ** p-value < 0.1