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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