Table 1 

Algorithms used to predict the occurrence of Canadian census subdivisions 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: I_{L}) 
0.816 
0.052 
0.713 – 0.919 
211 
5 
Index of nymphal tick immigration (range 425 km: I_{N}) 
0.896 
0.025 
0.848 – 0.949 
216 
6 
T * I_{N} 
0.926 
0.029 
0.869 – 0.983 
180 
7 
Tc * I_{N} 
0.807 
0.055 
0.699 – 0.914 
183 
8 
T * I_{L} 
0.845 
0.052 
0.743 – 0.947 
207 
9 
Tc * I_{L} 
0.723 
0.056 
0.614 – 0.832 
207 
10 
T * I_{N }* (0.05* I_{L})^{†} 
0.926 
0.029 
0.869 – 0.983 
185^{1} 
11 
Tc * I_{N }* (0.05* I_{L})^{†} 
0.807 
0.055 
0.699 – 0.914 
186^{1} 
12 
T * I_{N}* F 
0.821 
0.050 
0.723 – 0.919 
185^{2} 
13 
Tc * I_{N}* F 
0.752 
0.054 
0.646 – 0.858 
186^{2} 
14 
T * I_{N}* Log_{10 }F 
0.832 
0.051 
0.732 – 0.933 
185^{2} 
15 
Tc * I_{N}* Log_{10 }F 
0.762 
0.056 
0.652 – 0.871 
187^{2} 


^{†}Larvatonymph survival of I. scapularis is approximately one twentieth of nymphtoadult 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* I_{L}) 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:10.1186/1476072X724 