Abstract
The increased cases of cutaneous leishmaniasis vectored by Phlebotomus papatasi (Scopoli) in Libya have driven considerable effort to develop a predictive model for the potential geographical distribution of this disease. We collected adult P. papatasi from 17 sites in Musrata and Yefern regions of Libya using four different attraction traps. Our trap results and literature records describing the distribution of P. papatasi were incorporated into a MaxEnt algorithm prediction model that used 22 environmental variables. The model showed a high performance (AUC = 0.992 and 0.990 for training and test data, respectively). High suitability for P. papatasi was predicted to be largely confined to the coast at altitudes < 600 m. Regions south of 30 degrees N latitude were calculated as unsuitable for this species. Jackknife analysis identified precipitation as having the most significant predictive power, while temperature and elevation variables were less influential. The National Leishmaniasis Control Program in Libya may find this information useful in their efforts to control zoonotic cutaneous leishmaniasis. Existing records are strongly biased toward a few geographical regions, and therefore, further sand fly collections are warranted that should include documentation of such factors as soil texture and humidity, land cover, and normalized difference vegetation index (NDVI) data to increase the model's predictive power.