Coarse-resolution Ecology of Etiological Agent, Vector, and Reservoirs of Zoonotic Cutaneous Leishmaniasis in Libya

Abstract

Cutaneous leishmaniasis ranks among the tropical diseases least known and most neglected in Libya. World Health Organization reports recognized associations of Phlebotomus papatasi, Psammomys obesus, and Meriones spp., with transmission of zoonotic cutaneous leishmaniasis (ZCL; caused by Leishmania major) across Libya. Here, we map risk of ZCL infection based on occurrence records of L. major, P. papatasi, and four potential animal reservoirs (Meriones libycus, Meriones shawi, Psammomys obesus, and Gerbillus gerbillus). Ecological niche models identified limited risk areas for ZCL across the northern coast of the country; most species associated with ZCL transmission were confined to this same region, but some had ranges extending to central Libya. All ENM predictions were significant based on partial ROC tests. As a further evaluation of L. major ENM predictions, we compared predictions with 98 additional independent records provided by the Libyan National Centre for Disease Control (NCDC); all of these records fell inside the belt predicted as suitable for ZCL. We tested ecological niche similarity among vector, parasite, and reservoir species and could not reject any null hypotheses of niche similarity. Finally, we tested among possible combinations of vector and reservoir that could predict all recent human ZCL cases reported by NCDC; only three combinations could anticipate the distribution of human cases across the country.

Can You Judge a Disease Host by the Company It Keeps? Predicting Disease Hosts and Their Relative Importance: A Case Study for Leishmaniasis

“…As such, it is both subjective and subject to model bias. More recently, other methods have appeared: In [ 33 ] a small group of four suspected hosts was used as a starting point for including biotic effects indirectly by calculating the fundamental niche of these four mammal species and considering the geographical correspondence with the niche distributions of the vectors. This paper was more concerned with including information about a particular set of potential hosts into corresponding risk maps rather than identifying new hosts per se.…”

“…The methodology differs from those of [ 32 – 35 ] by using as model inputs only purely spatial data, using point collection data to proxy spatial distributions of taxa and co-occurrences to infer potential biotic interactions. In particular, it uses no auxiliary information, such as expert knowledge, as in the case of [ 32 ]; fundamental niche distributions of taxa, as in the case of [ 33 ]; or specific categorical data associated with the relevant taxa, as in the case of [ 34 ].…”

Natural Infection of Phlebotomus sergenti by Leishmania tropica in Libya

“…sergenti in Libya. 18 Indeed, from the literature, only Ph. papatasi was identified as a potential vector of Leishmania in Libya.…”

“…In addition, climate change scenarios could eventually affect sandfly vectors distribution and CL dispersion in Libya. 18 Herein, the first natural infection of Ph. sergenti by L. tropica is reported.…”

Epidemiology of Cutaneous Leishmaniasis in Tunisia

“…Therefore, the high awareness among the community, health decision and policy makers were key elements for sustainability of surveillance and control measures in Tunisia. Constructing a risk map of geographical spread of ZCL across Tunisian regions is also important to guide such programs [39]. Indeed finding areas with high probability of presence for both vectors and reservoirs of ZCL will be beneficial to prevent human infection by planning relevant activities [28].…”