AUC values from MLR analyses for predictive models using CDC data as gold standard.

<p>Bolded AUC values indicate a positive association.</p><p>*AUC values are significant (p<0.05).</p

Inter-Model Comparison of the Landscape Determinants of Vector-Borne Disease: Implications for Epidemiological and Entomological Risk Modeling

<div><p>Extrapolating landscape regression models for use in assessing vector-borne disease risk and other applications requires thoughtful evaluation of fundamental model choice issues. To examine implications of such choices, an analysis was conducted to explore the extent to which disparate landscape models agree in their epidemiological and entomological risk predictions when extrapolated to new regions. Agreement between six literature-drawn landscape models was examined by comparing predicted county-level distributions of either Lyme disease or <i>Ixodes scapularis</i> vector using Spearman ranked correlation. AUC analyses and multinomial logistic regression were used to assess the ability of these extrapolated landscape models to predict observed national data. Three models based on measures of vegetation, habitat patch characteristics, and herbaceous landcover emerged as effective predictors of observed disease and vector distribution. An ensemble model containing these three models improved precision and predictive ability over individual models. <i>A priori</i> assessment of qualitative model characteristics effectively identified models that subsequently emerged as better predictors in quantitative analysis. Both a methodology for quantitative model comparison and a checklist for qualitative assessment of candidate models for extrapolation are provided; both tools aim to improve collaboration between those producing models and those interested in applying them to new areas and research questions.</p></div

Spatial extent of Eastern United States considered in the analysis, based on 2000 U.S. Census (24.3°N to 45.9°N latitude, 93.0°W to 66.5°W longitude).

<p>Spatial extent of Eastern United States considered in the analysis, based on 2000 U.S. Census (24.3°N to 45.9°N latitude, 93.0°W to 66.5°W longitude).</p

AUC values from MLR analyses for predictive models using CDC data as gold standard – ensemble models.

<p>Bolded AUC values indicate a positive association.</p><p>*AUC values are significant (p<0.05).</p><p>N = none/minimal; L = low; M = moderate; H = high; A = absent/none; R = reported; E = established.</p

County and state level Spearman correlation coefficients (ρ) for pair-wise model comparisons overall and for geographic sub-analyses.

<p>Bolded values indicate a positive association.</p><p>*Values are significantly different from 0 (p<0.05)</p

Habitat models included in inter-model comparison.

<p>*Intercepts with no parameter estimate provided were not included; TD = tick density; PS = patch size; PI = patch isolation;</p>∧<p>Model employed a logistic link function.</p><p>HI = human incidence; OL = odds of Lyme disease; HD = highly developed land; GCH = soil classified as fair-good coniferous-supporting habitat; PHH = soil classified as poor-fair herbaceous-supporting habitat; TC = tick count; POP = population; NDVI = normalized difference vegetation index.</p

Inter-model comparison considerations and questions applied to Lyme disease incidence and tick abundance/presence models and supporting references.

<p>Y =  Yes, N =  No, ND =  Not determined, N/A =  Not applicable.</p

Odds ratios in MLR for predictive models using CDC data as gold standard – original and ensemble models.

<p>AIC  =  Akaike information criterion; considers both model fit and complexity, used to assess goodness-of-fit.</p><p>°For Lyme Disease Risk, 0 =  minimal/no risk, 1 =  low risk/Lyme disease reported, 2 =  medium risk, 3 =  high risk. For Tick Presence, 0 =  absent/none, 1 =  reported, 2 =  established.</p>∧<p>N = 1750: Some counties had no deciduous forest; thus, patch size and patch isolation could not be calculated.</p><p>*Significant positive OR estimate: 95% CI excludes the null (1.0) and OR estimate is >1.0 (p<0.05).</p

Dynamics of the fraction of infected nodes at constant transmission probability.

<p>The networks follow a scale-free degree distribution with for the hosts and a Poisson degree distribution for the vectors. As the number of hosts is increased, the epidemic is sustained for longer times.</p

Occurrence of clusters of lower SES and of higher household density.

<p>Colored areas indicate significant clusters based on the <i>G_i*(d)</i> test (p<0.05) of clusters of high levels of crowding and of low levels of SES.</p