Global distribution maps of the leishmaniases

1) This work on mapping of leishmaniasis is unique and impressive in its scope and depth, and makes for the most comprehensive overview of the leishmaniasis burden worldwide to date. Rightly so, climatic as well as socio-economic factors were taken into account to predict the risk of leishmaniasis. Indeed, this work will be able to guide health authorities in future surveillance activities. However, to serve this purpose it would be extremely helpful if the maps were presented in a format where it would be possible to 'zoom in' so that the geographical locations can be more easily identified. Specifically, the detail of the global prediction maps in Figures 3 and 4 are difficult to see. The authors could consider including larger insert maps for the major endemic areas, e.g. east Africa and Indian subcontinent for VL.

We agree with the reviewers and have therefore supplied 4 figure supplements detailing a more close up view of CL in northeast Africa and across the Near East, and of VL in northeast Africa and the Indian subcontinent. These panels depict areas of the highest incidence of both CL and VL. In addition, maps can be provided to individuals upon request to the authors.

2) In Asia as well as in Africa, VL caused by L. donovani typically presents as epidemics, with the case load rising and falling over a period of 5-10 years, probably dependent on climatic factors as rainfall, and thus presenting as a varying burden to countries. Similarly, CL caused by L. major and L. tropica are prone to epidemics. Please address in the Discussion.

Whilst this is a crucial component of burden estimation, we believe this doesn’t directly impact on the BRT models used, since these are reliant on presence/absence data, not total numbers of cases in a given year. In parts of the evidence consensus generation process where temporal data is important, such as contemporariness score for peer-review data and the scoring of case data series, the temporal divisions used to analyse these data is sufficiently broad to accommodate most inter-annual variation. However, we have added a section in the Discussion highlighting that this characteristic will further complicate potential burden estimation:

“A further complication with burden estimation is the epidemic nature of the disease, as evidenced by the national case time series in Alvar et al. (2012), leading to significant interannual variation in burden. Therefore, any burden estimation would have to account for this and the temporal spread of data would therefore be critical.”

3) A complete data review was used for establishing the evidence consensus for presence of leishmaniasis. However, in any country where the appropriate vector for transmission has not been confirmed according to the criteria set in 'Control of the Leishmaniasis' (WHO, TRS 949, 2010) it cannot be assumed either that leishmaniasis is endemic, or that the area is suitable for leishmaniasis transmission. It is unclear whether this has been taken into account; if not, please refer to 'Control of the Leishmaniasis' where expert consensus on vector presence in each country is compiled. An example is Taiwan: according to map 3A there is an area of confirmed CL presence, yet the vector for transmitting L. tropica has not been confirmed.

From the outset we set out to model reported cases of leishmaniasis infection and disease in humans, therefore the evidence consensus was primarily driven by evidence of local autochthonous transmission of the disease. Whilst in some cases the vector species is unknown or unproven, this may just as equally reflect the rarity of the disease in this area (and hence little knowledge available on vector species) rather than necessarily the suggestion of local transmission being incorrect. As a result, we chose to prioritise evidence of autochthonous cases of disease. Where there was insufficient evidence pertaining to human cases, information concerning vector and reservoir distributions was also considered, and this was taken from reports in the literature. In the regions where this was considered, the findings were consistent with the WHO Technical Report, apart from the presence of sandflies (not proven to transmit disease locally) in two regions of Tanzania. In the specific case of Taiwan, several cases have been reported as being locally derived (as outlined in the evidence consensus tables in the Dryad dataset) and therefore the evidence consensus scores Taiwan as likely to have the disease present. The region scores +53.33%, therefore indicating that this is not unanimously agreed upon by the various sources consulted, however this is supported by GIDEON and the Alvar et al. (2012) paper. The WHO technical report also indicates that P. kiangsuensis could act as a potential vector. In order to better clarify this situation, we have clarified the text in three places:

a) “(ii) peer-reviewed evidence of local autochthonous transmission”

b) “Cases were included if there was sufficient evidence to suggest that local autochthonous transmission had occurred”

c) “In some locations, cases have been reported as locally transmitted without the presence of proven vector species, which could indicate a false positive. However, the overall consensus score will reflect any uncertainty associated with the validity of these reports; if multiple independent sources report autochthonous cases, this increased certainty will be reflected in a higher consensus score.”

Minor comments:

4) Differences in sandfly ecology. Different sandfly species have distinct ecologies and habitat preference (for example Phlebotomus orientalis and P. martini in east Africa) and the authors should explain how such differences are taken into account.

We have reinforced the relevant section in the Discussion relating to the flexibility of BRT and how it can deal with complexity:

“The complexity and diversity of transmission cycles involving not just humans, but also a multitude of vectors and reservoirs, necessitated a modelling approach which can account for highly non-linear effects of covariates on probability of disease presence. The BRT modelling approach employed is able to do this and has previously been shown to produce highly accurate predictions across a wide range of species. This ecological niche modelling approach is therefore able to deal with not only the variation in parasites causing infection, but also the various life-histories and habitat preferences associated with the different vector species.”

5) Classification of contemporariness. Provide a justification as to the year bins used.

We have added the following in light of this comment:

“Contemporariness bins were based upon the potentially lengthy intrinsic incubation periods present with some Leishmania spp. as well as to accommodate the potential for epidemic cycles, where cases may only be detected in peak years and missed in the intervening baseline periods.”

6) Pseudo-presence data. The generation of such data was not clear and the authors should provide further details.

We have added some more details to those already listed in the document to help clarify. The manner in which the pseudo-presence data was incorporated as a numerical parameter in the BRT process can be found in the paragraph entitled Ensemble analysis:

“As in Bhatt et al. (2013) points were randomly located in regions above an evidence consensus threshold of -25, with regional placement probability weighted by evidence consensus scores, so that regions with higher evidence consensus contained more pseudo-presences than lower scoring areas.”

7) “We provide estimates of the populations at risk in 90 countries for which no human cases of CL or VL were reported.” This is interesting information but we did not find it presented obviously in the article.

We have provided via the Dryad dataset associated with this output, tables detailing national estimates of populations living in areas of environmental risk of leishmaniasis. We have also inserted the following section as a synoptic overview of these countries:

“A full table of this information is presented in the associated Dryad database (doi:10.5061/dryad.05f5h). For many of these countries, Alvar et al. (2012) reported a handful of sporadic cases over the years indicating very rare occurrence of infection, whilst the remainder were countries with inconclusive evidence of disease presence or absence.”

8) Furthermore, significant anthroponotic transmission of both L. infantum and L. donovani occurs across much of the Old World with zoonotic cycles of VL primarily tied to canine hosts. While transmission of L. donovani is anthroponotic, there is no anthroponotic transmission of L. infantum where transmission is entirely zoonotic via canine hosts.

We have changed this section to reflect this comment:

“Furthermore, whilst significant anthroponotic transmission of L. donovani occurs across parts of the Old World, zoonotic cycles of VL, primarily tied to canine hosts, dominate L. infantum transmission (Chamaille et al., 2010; Ready, 2013), with infection in dogs shown to be closely associated with human population density.”

9) “In the Old World the main endemic CL areas are due to anthroponotically-transmitted L. tropica”. True, but a significant case load is also caused by zoonotic L. major in Old World CL.

We have changed this section to reflect the fact that climatic factors have differing relative influences between the Old World and New World. Table 2 demonstrates that whilst periurban extents are the most important predictor of Old World CL, temperature and to a lesser extent, precipitation, have a non-negligible influence, reflecting the two core epidemiologies present with L. tropica and L. major:

“This difference in the relative importance of climatic drivers reflects the fact that in the Old World the main endemic CL areas are due to both anthroponotically-transmitted L. tropica as well as zoonotic cycles of L. major, whereas in the New World the disease is primarily associated with sylvatic and zoonotic cycles with a variety of different Leishmania spp. and wild reservoir hosts implicated (Ashford, 1996; Lima et al., 2013; Ready, 2013; Reithinger et al., 2007; WHO, 2010).”