Fig 1.
Map of Jordan’s Governorates and locations of Syrian refugee camps (red dots).
Table 1.
List of model predictor variables.
A description of variables identified by previous studies and publication sources as influencing the distribution of Phlebotomus papatasi and their relationship with its occurrence.
Table 2.
Georeferenced data sources and manipulations for predictor variables.
Table 3.
Climate data validation variables.
Description of climate variables, number of weather stations, and years for which data were acquired from the Jordan Meteorological Department.
Table 4.
Predictor variables’ fuzzy membership functions.
Inclusion of the associated rationale used to convert the predictor variables into fuzzy sets for inclusion in the multicriteria decision analysis model.
Table 5.
Rating scale used for pairwise comparisons between predictor variables.
Table 6.
Pairwise comparison matrix of the analytical hierarchy process (AHP) for the predictors associated with the occurrence of Phlebotomus papatasi in Jordan.
Based on the first author’s subjective judgment constructed from the literature review in Section 2.2.2.
Fig 2.
Multicriteria decision analysis output for the suitability distribution of Anabasis articulata.
This was used as a proxy for the distribution of Psammomys obesus; a predictor variable for the occurrence of Phlebotomus papatasi.
Table 7.
Pairwise comparison matrix of the analytical hierarchy process (AHP) for the predictors associated with the occurrence of Psammomys obesus in Jordan.
Based on the first author’s subjective judgement constructed from published literature.
Fig 3.
Data validation regression plots.
Results from simple linear regression analysis between weather station climate data recordings and modelled climate data using historical recordings for (A) mean monthly temperature, (B) mean monthly relative humidity, (C) mean monthly wind speed, and (D) mean annual precipitation.
Table 8.
Climate data validation results.
R2 values derived from the simple linear regression analysis between weather station climate data recordings and modelled climate data using historical recordings. Overall, R2 values were higher for the warmer months of the year (April-September).
Fig 4.
Fuzzy membership maps for each predictor variable.
(A) Annual precipitation, (B) Anabasis articulata, (C) human settlement, (D) temperature, (E) relative humidity, (F) vegetation and (G) wind.
Fig 5.
MCDA outputs for the predicted suitability distribution of Phlebotomus papatasi in Jordan from April to September.
* show the location of Syrian refugee camps.
Fig 6.
Geographic areas suitable for Phlebotomus papatasi occurrence using suitability cut-offs of 0.9 to 0.5.
Table 9.
Mean change in values (n = 96) between original multicriteria decision analysis outputs compared to new maps with either equal weights for all predictor variables, or assuming linear membership functions for all predictor variables.
Fig 7.
Population at risk of Phlebotomus papatasi exposure between April and September using different suitability cut-off points from moderate (0.5) to very high (0.9) suitability calculated using (top) the Global Population of the World (GPW) grid and (bottom) the European Commission’s Global Human Settlement (GHS) population grid.
Table 10.
Quantification of (i) areas suitable for Phlebotomus papatasi occurrence in relation to the whole country; (ii) the population at risk of cutaneous leishmaniasis using different population grids; and (iii) the population at risk in relation to the total population, using different suitability cut-off values.