Use of soil moisture active passive satellite data and WorldClim 2.0 data to predict the potential distribution of visceral leishmaniasis and its vector <em>Lutzomyia longipalpis</em> in Sao Paulo and Bahia states, Brazil

Abstract

Visceral leishmaniasis (VL) is a neglected tropical disease transmitted by Lutzomyia longipalpis, a sand fly widely distributed in Brazil. Despite efforts to strengthen national control programs reduction in incidence and geographical distribution of VL in Brazil has not yet been successful; VL is in fact expanding its range in newly urbanized areas. Ecological niche models (ENM) for use in surveillance and response systems may enable more effective operational VL control by mapping risk areas and elucidation of eco-epidemiologic risk factors. ENMs for VL and Lu. longipalpis were generated using monthly WorldClim 2.0 data (30-year climate normal, 1-km spatial resolution) and monthly soil moisture active passive (SMAP) satellite L4 soil moisture data. SMAP L4 Global 3-hourly 9-km EASE-Grid Surface and Root Zone Soil Moisture Geophysical Data V004 were obtained for the first image of day 1 and day 15 (0:00-3:00 hour) of each month. ENM were developed using MaxEnt software to generate risk maps based on an algorithm for maximum entropy. The jack-knife procedure was used to identify the contribution of each variable to model performance. The three most meaningful components were used to generate ENM distribution maps by ArcGIS 10.6. Similar patterns of VL and vector distribution were observed using SMAP as compared to WorldClim 2.0 models based on temperature and precipitation data or water budget. Results indicate that direct Earth-observing satellite measurement of soil moisture by SMAP can be used in lieu of models calculated from classical temperature and precipitation climate station data to assess VL risk.

Ecological Niche Modelling Approaches: Challenges and Applications in Vector-Borne Diseases

“…Thus, whenever possible, the databases to be used must be filtered and reinforced with a molecular identification (i.e., [69,[80][81][82]). Despite the aforementioned, the issue is usually neglected, and most of the studies rely solely on morphological identification (e.g., [83][84][85][86][87][88][89][90][91][92][93][94][95][96], but see [97] for a mixed approach), or do not even describe the identification method [70,82,[98][99][100][101][102][103][104][105][106][107][108].…”

“…As far as we know, this is what occurs with a large amount of the studies dealing with VBDs. As these kinds of studies are usually performed with public health purposes at regional or local scales, they often rely on regional/national datasets collected by administrations or NGOs whose range of action is defined by restricted geographical or political borders (i.e., country or even provinces' administrations) (e.g., [67][68][69]80,[83][84][85][86][87][88][89][90][91][92][93][94][95][96]98,99,101,[104][105][106][107][108][112][113][114][115][116][117]). The concern resides in the fact that ENM built with occurrence data restricted to artificial boundaries might consider only a subset of the environmental conditions experienced by a species across its entire range (i.e., "spatial niche truncation" [118]); therefore, providing an incomplete description of the environmental limits [109] and underestimating the environmental conditions that the species can withstand [111] (see an example in Figure 2).…”

“…Despite the sampling bias and the consequent non-independence of occurrences needing to be accounted for, a good number of the VBDs studies published in the last three years omitted declaring any procedure for dealing with the sampling bias [67,68,[80][81][82][83][84][85][86]88,89,91,[94][95][96]98,101,104,108,113,124,142].…”

“…Despite the choice of algorithms or estimation frameworks, model calibration is a key process in which variable selection and model settings are fine-tuned to obtain the best model performance [64,155]. Unfortunately, in spite of the recommendations for maximizing reproducibility of ENMs [64,156], the information concerning model calibration is lacking in a high number of studies dealing with VBDs [80,[82][83][84][85][86]88,89,91,92,96,98,107,112,113,124].…”

Impact of climatic factors on temporal variability of sand fly abundance in Sri Lanka: Longitudinal study (2018 to 2020) with two-stage hierarchical analysis.

“…Laboratory studies have shown that extreme temperatures below 15°C and above 32°C have a negative impact on the fecundity and longevity of the species [7]. The in uence of weather variables such as rainfall, relative humidity, soil water stress, evaporation rate, wind speed and El Nino Southern Oscillation on the transmission of leishmaniasis had been evaluated in the past across different endemic settings but the reported associations were inconsistent [8][9][10][11][12][13][14][15][16]. This heterogeneity depends, in part, on the data and methods used for analysis and the location-speci c in uences of the climate on vector bionomics of the sand y species and transmission dynamics of the respective disease entities.…”