Table 2.
Summary of validation statistics for the geostatistical models described in Table 1.
| Model | AUC | Mean Error# (% prevalence) | Mean Absolute Error* (% prevalence) |
|---|---|---|---|
| Model of P. vivax with distance to coastline fixed effect | 0.867 | 5.07 | 1.30 |
| Model of P. vivax with elevation fixed effect | 0.857 | 5.46 | 1.33 |
| Model of P. falciparum with distance to coastline fixed effect | 0.821 | 0.39 | 0.55 |
| Model of P. falciparum with elevation fixed effect | 0.856 | 0.34 | 0.50 |
AUC between 0.5 and 0.7 indicates a poor discriminative capacity; 0.7-0.9 indicate a reasonable capacity; and >0.9 indicate a very good capacity.
# Mean error is a measure of the bias of predictions (the overall tendency to over or under predict).
* Mean absolute error is a measure of overall precision (the average magnitude of error in individual predictions).