Significant grouped climate variables with highest Akaike weight (<i>w_i</i>).

<p>Key to covariate abbreviations: LST, land surface temperature; MIR, middle infrared; NDVI, normalized difference vegetation index; RH, relative humidity. Key to database statistical abbreviations: AICc: Akaike information criterion for small sample sizes; <i>w_i</i>, Akaike weight.</p><p>Univariate logistic regression models were fitted to each of the grouped climate variables to determine the covariates that best discriminated intradomiciliary village prevalence. Model performance was evaluated by the selecting the covariate with the highest Akaike weight (<i>w_i</i>).</p

Summary of environmental and socioeconomic databases used in analyses.

<p>Key to database abbreviations: LST, land surface temperature; MIR, middle infrared; NDVI, normalized difference vegetation index; RH, relative humidity; max, maximum average value; min, minimum average value. Key to database source abbreviations: CGIAR_CSI, Consultative Group for International Agriculture Research – Consortium for Spatial Information; MODIS, moderate resolution imaging spectroradiometer; AVHRR/TFA, advanced very high resolution radiometer transformed by temporal fourier analysis; CRU/UEA, Climate Research Unit,/University of East Anglia; INE, Instituto Nacional de Estadistica de Guatemala.</p

Estimates of effect of significant environmental risk factors on <i>Triatoma dimidiata</i> intradomiciliary prevalence >5%.

<p>Key to risk factor abbreviations: LST, land surface temperature; MIR, middle infrared; NDVI, normalized difference vegetation index; RH, relative humidity.</p><p>Univariate logistic regression models were developed to investigate the effect of each environmental covariate on <i>Triatoma dimidiata</i> intradomiciliary village prevalence >5% by survey and department. Odds ratios (OR) and 95% confidence intervals for significant risk factors are reported. Land cover classes represent the proportion of each land cover type within a 2 km buffer of analyzed villages.</p

Diagnostic statistics for predictive models of <i>Triatoma dimidiata</i> intradomiciliary prevalence >5%.

<p>Key to department and study abbreviations: Dept, department; BV, Baja Verapaz; JU, Jutiapa; UVG, Universidad del Valle de Guatemala; GNMH; Guatemala National Ministry of Health. Key to model abbreviations: ENV, environmental model; DOM, domicile construction material model; ALL, combination of census and environmental models. Key to accuracy measure abbreviations: AUC, area under receiver-operator curve; Max κ, maximum kappa; PPV, positive predictive value; NPV, negative predictive value.</p><p>Multivariate logistic regression models were developed to estimate the predictive probability of <i>Triatoma dimidiata</i> intradomiciliary village prevalence >5%. For each department and study, predictive models of environmental and domicile construction risk factors were developed separately and together. Overall model accuracy was compared using the area under the receiver-operator curve (AUC). Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated using the probability threshold with maximum value of kappa (κ).</p

Schematic representation of the malaria model.

<p>Horizontal solid lines denote transitions between epidemiological states, and dashed lines represent transmission of infection between human hosts and mosquito vectors. Dotted lines denote vectors feeding on livestock. The vector population consists of adult female anopheline mosquitoes.</p

Estimates of effect of significant domicile construction materials on <i>Triatoma dimidiata</i> intradomiciliary prevalence >5%.

<p>Univariate logistic regression models were developed to investigate the effect of each domicile construction material on <i>Triatoma dimidiata</i> intradomiciliary village prevalence >5% by survey and department. Odds ratios (OR) and 95% confidence intervals for significant risk factors are reported. Domicile construction risk factors represent the proportion of domiciles per village constructed with each material as determined by the 2002 national census of the Guatemalan National Institute of Statistics <a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0001035#pntd.0001035-INE1" target="_blank">[40]</a>.</p

Effect of altering the relative livestock to human density, for different vector density scenarios, at the new endemic equilibrium.

<p>Comparing a scenario where the availability of livestock to vectors is the same as that of humans (left, <i>A_l</i> = 0.5) <i>versus</i> where it is 9 times higher than that of humans (right, <i>A_l</i> = 0.9). Along the <i>x</i>-axis, representing  = <i>N_l/N_h</i>, the livestock density <i>N_l</i> is varied relative to a fixed human density <i>N_h</i> = 100. <i>N_v</i>(0) = 1000. Effect of introducing livestock when: <i>N_v</i> remains constant (black line), and when <i>N_v</i> increases until reaching a maximum, which depends on the carrying capacity, <i>K</i> (coloured lines: <i>K</i> increasing from 5,000 (green line) to 100,000 (red line)). The effects of introducing livestock on the human blood index (HBI) and on the vector mortality rate () are independent from the vector density scenarios (A, B). The vertical line in the left panels highlights the new endemic equilibrium that is reached after the introduction of 1 head of livestock per 4 persons ( = 0.25), corresponding to the end of the timeline in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0101699#pone-0101699-g002" target="_blank">Figure 2</a>. Other parameters are as in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0101699#pone-0101699-t001" target="_blank">Table 1</a>.</p

Parameter values for modelling the effects of untreated livestock on malaria.

<p>*For simulations with constant vector population density: ; for variable vector density: and .</p><p>**The relative magnitudes of and were varied in a sensitivity analysis.</p

Critical proportion of ITL as a function of the insecticidal (<i>k</i>), and diversionnary effect ().

<p>The lines show the combination of values of coverage and insecticidal probability required to achieve <i>R</i>₀ = 1, above which <i>R</i>₀ will be decreased below 1, for a given diversion probability (). Black line: , no repellency or attractancy (is the same as the white line in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0101699#pone-0101699-g004" target="_blank">Figure 4</a>); Red lines: , repellency increasing from 0.1 to 0.5 (top), at intervals of 0.1; Green lines: , attractancy increasing from −0.1 to −0.5 (bottom), at intervals of 0.1. Other parameters are as in baseline simulations (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0101699#pone-0101699-t002" target="_blank">Table 2</a>).</p

Predicted impact of Insecticide Treatment of Livestock on malaria prevalence – with repellency () or attractancy () for <i>k</i> = 0.1.

<p>This figure shows how the diversionary properties of the insecticide affect the coverage required to achieve a given prevalence ratio (PR: prevalence with ITL / baseline prevalence). Blue line: PR = 0.46 (like the observed in the Pakistan trial); White line: PR = 0; Red line: PR = 1 (above which treating livestock increases malaria prevalence). Along the y axis, is varying from no diversion () to maximum repellency () or maximum attractancy (). The colour bar shows the scale of PR values, from 0 to ≈11 in Pakistan and up to ≈5 in Ethiopia. Other parameters are as in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0101699#pone-0101699-t002" target="_blank">Table 2</a>.</p