Additional file 1: Figure S1. of Mapping the spatial distribution of the Japanese encephalitis vector, Culex tritaeniorhynchus Giles, 1901 (Diptera: Culicidae) within areas of Japanese encephalitis risk

Temporal distribution of Culex tritaeniorhynchus occurrence data. Histogram showing the number of spatially unique Cx. tritaeniorhynchus occurrence records per year in our dataset (1928–2014). 73.43% of occurrence records were obtained during the years for which we have annual land cover class layers (2001–2012), as indicated by orange x-axis breaks. (.docx) (DOCX 85 kb

Additional file 5: Table S2. of Mapping the spatial distribution of the Japanese encephalitis vector, Culex tritaeniorhynchus Giles, 1901 (Diptera: Culicidae) within areas of Japanese encephalitis risk

The relative influence of each covariate on the model. The relative influence of each covariate in rank order, including mean (%), 2.5% quantile and 97.5% quantile values. (.docx) (DOCX 14 kb

Additional file 1: of Integrating vector control across diseases

Details of: selection of major vector-borne diseases and vector control interventions for evaluation, compilation of risk maps for these diseases, and calculation of joint population at risk estimates. (DOCX 2809 kb

Geographical variation in selected (a) climate and (b) landscape predictors of leishmaniasis distribution in the recent past period (2005).

<p>Geographical variation in selected (a) climate and (b) landscape predictors of leishmaniasis distribution in the recent past period (2005).</p

Perfusion flows during 46 hours of normothermic perfusion.

<p>(A) portal flow ± SD, (B) hepatic artery flow ± SD, (C) IVC flow ± SD. Perfusate flows were measured in-line using ultrasonic flow probes.</p

The predicted extent of (a) cutaneous and (b) visceral leishmaniasis under recent past conditions and under alternative climate change pathways and socio-economic storylines.

<p>The heavy blacklines across the middle of the box indicate median predicted extent across the 20 model runs; the box indicates the interquartile range of the data whilst the whiskers indicates the extremes. Note that the threshold that best predicted disease presence in cross-validation across model runs was used to assign pixels to presence or absence classes (values in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0183583#pone.0183583.t003" target="_blank">Table 3</a>).</p

Percentage contribution of predictors to models of visceral leishmaniasis where different sets of predictors were considered (averaged across 20 sub-models).

<p>Percentage contribution of predictors to models of visceral leishmaniasis where different sets of predictors were considered (averaged across 20 sub-models).</p

Mean accuracy statistics for models of cutaneous and visceral leishmaniasis for models considering only abiotic predictors versus those considering mammal richness alongside abiotic predictors.

<p>A. cutaneous leishmaniasis. B. visceral leishmaniasis.</p

Map of study area indicating the distributional data for the leishmaniases (black circles) used to parameterise environmental models (a) cutaneous leishmaniasis (n = 803 squares) (b) visceral leishmaniasis (n = 201 squares).

<p>Grey squares indicate the locations of pseudo-absence data points for one iteration of the model. Tick marks on the x and y axes indicate degrees latitude and longitude respectively and the study grain is 5 arc minute squares.</p

Change in the predicted presence of cutaneous leishmaniasis between the recent past and future (2050) conditions under alternative climate change and socio-economic scenarios.

<p>This was calculated by subtracting the number of times a pixel was predicted as present in the recent past (across 20 model runs) from the number of times a pixel was predicted as present in the future. Areas in pink show areas that are likely more favourable for disease and areas in blue show areas that are less favourable for the disease in the future.</p