Assessing geographic and climatic variables to predict the potential distribution of the visceral leishmaniasis vector Lutzomyia longipalpis in the state of Espírito Santo, Brazil

Abstract

Visceral leishmaniasis (VL) is an infectious disease caused by the protozoa Leishmania chagasi, whose main vector in South America is Lutzomyia longipalpis. The disease was diagnosed in the Brazilian state of Espírito Santo (ES) for the first time in 1968. Currently, this disease has been considered endemic in 10 municipalities. Furthermore, the presence of L. longipalpis has been detected in eight other municipalities where the transmission has not been reported thus far. In this study, we performed species distribution modeling (SDM) to identify new and most likely receptive areas for VL transmission in ES. The sandflies were both actively and passively collected in various rural area of ES between 1986 and 2017. The collection points were georeferenced using a global positioning system device. Climatic data were retrieved from the WorldClim database, whereas geographic data were obtained from the National Institute for Space Research and the Integrated System of Geospatial Bases of the State of Espírito Santo. The maximum entropy algorithm was used through the MIAmaxent R package to train and test the distribution models for L. longipalpis. The major contributor to model generation was rocky outcrops, followed by temperature seasonality. The SDM predicted the expansion of the L. longipalpis-prone area in the Doce River Valley and limited the probability of expanding outside its watershed. Once the areas predicted suitable for L. longipalpis occurrence are determined, we can avoid the inefficient use of public resources in conducting canine serological surveys where the vector insect does not occur.

Prediction of silicon content in the hot metal using Bayesian networks and probabilistic reasoning

“…Complementarily, the Pearson correlation coefficient (R) was calculated for each neural network and it was found that the results presented are better than those predicted in the literature [2][3][4][5][6][7][8], and [22][23][24][25][26][27][28][29][30][31][32][33][34]. The analysis of Fig.…”

“…The usual method for evaluating a neural network model is to use the MSE (mean square error) results, because the lower the values found, the better the predictive capacity. Equation (1) illustrates the MSE between the actual and predicted values [32].…”

Influence of climatic variables on the number of cases of visceral leishmaniasis in an endemic urban area

“…[4][5][6] There is growing evidence of the climate change impact on vector-borne diseases. [7][8][9][10][11] For example, changes in temperature and humidity, especially in endemic areas of the disease, can aggravate leishmaniasis, as these changes are directly linked to the natural cycle of the disease and the vector's ability to survive. 12,13 The present study investigates the hypothesis that climatic factors are responsible for the variation of VL cases, with the prediction that warmer and wetter climates may have contributed to increased cases over the last decades.…”

Aspectos epidemiológicos da leishmaniose visceral no Estado da Bahia, Brasil

“…Classicamente, a LV é transmitida por flebotomíneos fêmeas infectadas, popularmente conhecidos como: birigui, anjinho, cangalhinha, tatuquira ou mosquito palha, durante o repasto sanguíneo (Oliveira et al, 2018). No Brasil, os casos de LV estão relacionados a protozoários do gênero Leishmania, sendo a espécie Leishmania chagasi comumente envolvida e o principal vetor é a espécie Lutzomyia longipalpis (Silva et al, 2015;Del Carro et al, 2020;Silva et al, 2021). A região nordeste do Brasil é considerada uma área endêmica de LV nas Américas, devido ao clima favorável à reprodução do inseto vetor (Alves et al, 2016;Rocha et al, 2018;Azevedo et al, 2019;Ribeiro et al, 2021).…”