Table 1

Algorithms used to predict the occurrence of Canadian census sub-divisions containing resident I. scapularis populations.


Algorithm
 AUC
 SE
 95% CI
 AIC
1
 No. of ticks at model equilibrium (T)
 0.921
 0.030
 0.863 – 0.979
 214
2
 No. of ticks at model equilibrium categorised (Tc)
 0.780
 0.052
 0.678 – 0.881
 206
3
 Percent forest area (F)
 0.387
 0.038
 0.313 – 0.461
 232
4
 Index of larval tick immigration (range 255 km: IL)
 0.816
 0.052
 0.713 – 0.919
 211
5
 Index of nymphal tick immigration (range 425 km: IN)
 0.896
 0.025
 0.848 – 0.949
 216
6
 T * IN
 0.926
 0.029
 0.869 – 0.983
 180
7
 Tc * IN
 0.807
 0.055
 0.699 – 0.914
 183
8
 T * IL
 0.845
 0.052
 0.743 – 0.947
 207
9
 Tc * IL
 0.723
 0.056
 0.614 – 0.832
 207
10
 T * IN * (0.05* IL)†
 0.926
 0.029
 0.869 – 0.983
 1851
11
 Tc * IN * (0.05* IL)†
 0.807
 0.055
 0.699 – 0.914
 1861
12
 T * IN* F
 0.821
 0.050
 0.723 – 0.919
 1852
13
 Tc * IN* F
 0.752
 0.054
 0.646 – 0.858
 1862
14
 T * IN* Log10 F
 0.832
 0.051
 0.732 – 0.933
 1852
15
 Tc * IN* Log10 F
 0.762
 0.056
 0.652 – 0.871
 1872

†Larva-to-nymph survival of I. scapularis is approximately one twentieth of nymph-to-adult survival (Ogden et al., 2005). 1 In all logistic regression models containing variables relating to forest cover, these variables were not significant. 2 In neither of these models was the variable (0.05* IL) significant.

The performance of different risk algorithms in ROC analysis is shown: AUC = area under the ROC curve, SE = standard error, 95% CI = 95% confidence interval for AUC. AIC = Aikeke's Information criterion of a logistic regression model for each algorithm in which the outcome was the occurrence of a known I. scapularis population, and the explanatory variables were the algorithm component(s).

Ogden et al. International Journal of Health Geographics 2008 7:24   doi:10.1186/1476-072X-7-24

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