Table 3 Results of Bayesian spatial binomial model fitted to the DCO data
Source of variation | Mean | SD | 2.5% | 50% | 97.5% |
|---|---|---|---|---|---|
Prevalence of infection | |||||
Intercept | 1.043 | 1.240 | -1.483 | 1.059 | 3.481 |
ben_ppp | -0.291 | 0.137 | -0.560 | -0.291 | -0.017 |
bio12_wc30s | 0.257 | 0.559 | -0.870 | 0.264 | 1.342 |
bio16_wc30s | -0.476 | 0.682 | -1.801 | -0.484 | 0.900 |
dst_coastlin | 0.318 | 0.432 | -0.552 | 0.323 | 1.158 |
dst_waterway | 0.180 | 0.137 | -0.085 | 0.178 | 0.458 |
landcover | -0.073 | 0.098 | -0.266 | -0.073 | 0.119 |
pet_wc30s | -0.223 | 0.290 | -0.784 | -0.227 | 0.366 |
srtm_slope | 0.062 | 0.090 | -0.116 | 0.062 | 0.238 |
Hyperparameters | |||||
Theta1 for U(s) | -2.557 | 1.742 | -6.075 | -2.515 | 0.785 |
Theta2 for U(s) | 3.573 | 2.457 | -0.974 | 3.450 | 8.703 |
Precision \({\tau }_{v}\) | 5.765 | 1.890 | 2.784 | 5.538 | 10.105 |
Prevalence of cases | |||||
Intercept | -0.614 | 0.137 | -0.873 | -0.618 | -0.330 |
ben_ppp | -0.016 | 0.170 | -0.339 | -0.022 | 0.336 |
bio4_wc30s | 0.287 | 0.164 | -0.035 | 0.285 | 0.615 |
bio12_wc30s | 0.413 | 0.308 | -0.192 | 0.411 | 1.027 |
dst_waterway | 0.301 | 0.214 | -0.120 | 0.299 | 0.729 |
miaq_wc30s | -0.122 | 0.154 | -0.428 | -0.122 | 0.181 |
mimq_wc30s | -0.272 | 0.321 | -0.907 | -0.272 | 0.361 |
srtm_slope | -0.031 | 0.119 | -0.267 | -0.032 | 0.205 |
srtm_topo | 0.148 | 0.215 | -0.282 | 0.150 | 0.570 |
landcover | 0.132 | 0.130 | -0.124 | 0.131 | 0.390 |
Hyperparameters | |||||
Theta1 for U(s) | -5.452 | 2.164 | -9.214 | -5.638 | -0.796 |
Theta2 for U(s) | 4.814 | 0.971 | 2.769 | 4.878 | 6.559 |
Precision \({\tau }_{v}\) | 8.888 | 7.911 | 1.679 | 6.604 | 29.850 |