Geographical distribution and ecological niche modeling of the etiological agents of human sporotrichosis in Venezuela.
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ABSTRACT:
SUBMITTER: Barreto L
PROVIDER: S-EPMC7966649 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Chagas disease is an endemic zoonosis native to the Americas and is caused by the kinetoplastid protozoan parasite Trypanosoma cruzi. The parasite is also highly genetically diverse, with six discrete typing units (DTUs) reported TcI - TcVI. These DTUs broadly correlate with several epidemiogical, ecological and pathological features of Chagas disease. In this manuscript we report the most comprehensive evaluation to date of the genetic diversity of T. cruzi in Venezuela. The dataset includes 778 samples collected and genotyped over the last twelve years from multiple hosts and vectors, including nine wild and domestic mammalian host species, and seven species of triatomine bug, as well as from human sources. Most isolates (732) can be assigned to the TcI clade (94.1%); 24 to the TcIV group (3.1%) and 22 to TcIII (2.8%). Importantly, among the 95 isolates genotyped from human disease cases, 79% belonged to TcI - a DTU common in the Americas, however, 21% belonged to TcIV- a little known genotype previously thought to be rare in humans. Furthermore, were able to assign multiple oral Chagas diseases cases to TcI in the area around the capital, Caracas. We discuss our findings in the context of T. cruzi DTU distributions elsewhere in the Americas, and evaluate the impact they have on the future of Chagas disease control in Venezuela.
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Project description:BackgroundTanapox virus is a zoonotic infection that causes mild febrile illness and one to several nodular skin lesions. The disease is endemic in parts of Africa. The principal reservoir for the virus that causes Tanapox is unknown, but has been hypothesized to be a non-human primate. This study employs ecological niche modeling (ENM) to determine areas of tropical Africa suitable for the occurrence of human Tanapox and a list of hypothetical reservoirs. The resultant niche model will be a useful tool to guide medical surveillance activities in the region.MethodsThis study uses the Desktop GARP software to predict regions where human Tanapox might be expected to occur based on historical human case locations and environmental data. Additional modeling of primate species, using occurrence data from museum records was performed to determine suitable disease reservoirs.ResultsThe final ENM predicts a potential distribution of Tanapox over much of equatorial Africa, exceeding the borders of Kenya and Democratic Republic of Congo (DRC) where it has been historically reported. Five genera of non-human primates were found to be potential reservoir taxa.ConclusionsValidity testing suggests the model created here is robust (p < 0.04). Several genera of primates were identified as having ENMs overlapping with that of Tanapox and are suggested as potential reservoirs, mainly members of the Genus Cercopithecus. The ENM modeling technique has several limitations and results should be interpreted with caution. This study may increase knowledge and engage further research in this neglected disease.
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Project description:BackgroundThe WHO has established a control strategy for Strongyloides stercoralis in school-aged children as well as targets and to maintain control programs for Ascaris lumbricoides, Trichuris trichiura and hookworms. For an efficient development of control programs, it is necessary to know the target countries around the world, as well as the areas within each country where efforts should be focused. Therefore, maps that provide information on the areas at risk for soil-transmitted helminth (STH) infections on a national and sub-national scale would allow for a better allocation of resources.MethodsWe used the ecological niche models MaxEnt and Kuenm R library to estimate the global distribution of S. stercoralis and hookworms. We used occurrence points of both species extracted from surveys of two literature reviews and from the Global Atlas of Helminth Infection database, together with 14 raster maps of environmental variables.ResultsWe obtained two raster maps with the presence probability of S. stercoralis and hookworm infections at a global level and then estimated the global population at risk to be 2.6 and 3.4 billion, respectively. The population at risk was also estimated at the country level using estimations for areas as small as 25 km2. A relationship was found between the probability of the presence of S. stercoralis and its prevalence, and a raster map was generated. Annual precipitation, annual temperature, soil carbon content and land cover were the main associated environmental variables. The ecological niches of Strongyloides stercoralis and hookworms had an overlap of 68%.ConclusionsHere we provide information that can be used for developing more efficient and integrated control strategies for S. stercoralis and hookworm infections. This information can be annexed to the study of other risk factors or even other diseases to assess the health status of a community. GRAPHICAL ABSTARCT.
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Project description:Sporotrichosis is a cutaneous and subcutaneous fungal disease of humans and other mammals, known to be caused by the Sporothrix schenckii species complex, which comprises four species of clinical importance: S. brasiliensis, S. globosa, S. luriei, and S. schenckii sensu stricto. Of them, S. globosa and S. schenckii s. str. show global distribution and differences in global frequency as causal agents of the disease. In the Americas, only three species are present: S. schenckii s. str., S. brasiliensis (so far, only reported in Brazil), and S. globosa. In Venezuela, since the first case of sporotrichosis reported in 1935, S. schenckii have been considered its unique etiological agent. In the present work, the presence of more than one species in the country was evaluated.By phenotypic key features and molecular phylogeny analyses, we re-examined 30 isolates from diverse Venezuelan regions belonging to the fungi collection of Instituto de Biomedicina, Caracas, Venezuela, and national reference center for skin diseases. All isolates were collected between 1973 and 2013, and maintained in distilled water.Sporotrichosis in Venezuela is mainly caused by S. schenckii s. str. (70%). However, a significant proportion (30%) of sporotrichosis cases in the country can be attributable to S. globosa. A correlation between intraspecific genotypes and clinical presentation is proposed.Our data suggest that sporotrichosis various clinical forms might be related to genetic diversity of isolates, and possibly, to diverse virulence profiles previously reported in the S. schenckii species complex. Sporothrix globosa was found to be the causative agent of 30% of sporotrichosis for the Venezuelan cases re-examined, the highest frequency of this species so far reported in the Americas. The high genetic variability presented by S. schenckii s. str. indicates that species distinction based on phenotypic key features could be a challenging and uncertain task; molecular identification should be always employed.
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Project description:Cutaneous leishmaniasis is a very complex disease involving multiple factors that limit its emergence and spatial distribution. Prediction of cutaneous leishmaniasis epidemics in Tunisia remains difficult because most of the epidemiological tools used so far are descriptive in nature and mainly focus on a time dimension. The purpose of this work is to predict the potential geographic distribution of Phlebotomus papatasi and zoonotic cutaneous leishmaniasis caused by Leishmania major in Tunisia using Grinnellian ecological niche modeling. We attempted to assess the importance of environmental factors influencing the potential distribution of P. papatasi and cutaneous leishmaniasis caused by L. major. Vectors were trapped in central Tunisia during the transmission season using CDC light traps (John W. Hock Co., Gainesville, FL). A global positioning system was used to record the geographical coordinates of vector occurrence points and households tested positive for cutaneous leishmaniasis caused by L. major. Nine environmental layers were used as predictor variables to model the P. papatasi geographical distribution and five variables were used to model the L. major potential distribution. Ecological niche modeling was used to relate known species' occurrence points to values of environmental factors for these same points to predict the presence of the species in unsampled regions based on the value of the predictor variables. Rainfall and temperature contributed the most as predictors for sand flies and human case distributions. Ecological niche modeling anticipated the current distribution of P. papatasi with the highest suitability for species occurrence in the central and southeastern part of Tunisian. Furthermore, our study demonstrated that governorates of Gafsa, Sidi Bouzid, and Kairouan are at highest epidemic risk.
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Project description:African horse sickness is a vector-borne, non-contagious and highly infectious disease of equines caused by African horse sickness viruses (AHSv) that mainly affect horses. The occurrence of the disease causes huge economic impacts because of its high fatality rate, trade ban and disease control costs. In the planning of vectors and vector-borne diseases like AHS, the application of Ecological niche models (ENM) used an enormous contribution in precisely delineating the suitable habitats of the vector. We developed an ENM to delineate the global suitability of AHSv based on retrospective outbreak data records from 2005 to 2019. The model was developed in an R software program using the Biomod2 package with an Ensemble modeling technique. Predictive environmental variables like mean diurnal range, mean precipitation of driest month(mm), precipitation seasonality (cv), mean annual maximum temperature (oc), mean annual minimum temperature (oc), mean precipitation of warmest quarter(mm), mean precipitation of coldest quarter (mm), mean annual precipitation (mm), solar radiation (kj /day), elevation/altitude (m), wind speed (m/s) were used to develop the model. From these variables, solar radiation, mean maximum temperature, average annual precipitation, altitude and precipitation seasonality contributed 36.83%, 17.1%, 14.34%, 7.61%, and 6.4%, respectively. The model depicted the sub-Sahara African continent as the most suitable area for the virus. Mainly Senegal, Burkina Faso, Niger, Nigeria, Ethiopia, Sudan, Somalia, South Africa, Zimbabwe, Madagascar and Malawi are African countries identified as highly suitable countries for the virus. Besides, OIE-listed disease-free countries like India, Australia, Brazil, Paraguay and Bolivia have been found suitable for the virus. This model can be used as an epidemiological tool in planning control and surveillance of diseases nationally or internationally.
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Project description:BackgroundDespite the increasing number of cases of scrub typhus and its expanding geographical distribution in China, its potential distribution in Fujian Province, which is endemic for the disease, has yet to be investigated.MethodsA negative binomial regression model for panel data mainly comprising meteorological, socioeconomic and land cover variables was used to determine the risk factors for the occurrence of scrub typhus. Maximum entropy modeling was used to identify the key predictive variables of scrub typhus and their ranges, map the suitability of different environments for the disease, and estimate the proportion of the population at different levels of infection risk.ResultsThe final multivariate negative binomial regression model for panel data showed that the annual mean normalized difference vegetation index had the strongest correlation with the number of scrub typhus cases. With each 0.1% rise in shrubland and 1% rise in barren land there was a 75.0% and 37.0% increase in monthly scrub typhus cases, respectively. In contrast, each unit rise in mean wind speed in the previous 2 months and each 1% increase in water bodies corresponded to a decrease of 40.0% and 4.0% in monthly scrub typhus cases, respectively. The predictions of the maximum entropy model were robust, and the average area under the curve value was as high as 0.864. The best predictive variables for scrub typhus occurrence were population density, annual mean normalized difference vegetation index, and land cover types. The projected potentially most suitable areas for scrub typhus were widely distributed across the eastern coastal area of Fujian Province, with highly suitable and moderately suitable areas accounting for 16.14% and 9.42%, respectively. Of the total human population of the province, 81.63% reside in highly suitable areas for scrub typhus.ConclusionsThese findings could help deepen our understanding of the risk factors of scrub typhus, and provide information for public health authorities in Fujian Province to develop more effective surveillance and control strategies in identified high risk areas in Fujian Province.
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Project description:Ecological niche models are widely used in ecology and biogeography. Maxent is one of the most frequently used niche modeling tools, and many studies have aimed to optimize its performance. However, scholars have conflicting views on the treatment of predictor collinearity in Maxent modeling. Despite this lack of consensus, quantitative examinations of the effects of collinearity on Maxent modeling, especially in model transfer scenarios, are lacking. To address this knowledge gap, here we quantify the effects of collinearity under different scenarios of Maxent model training and projection. We separately examine the effects of predictor collinearity, collinearity shifts between training and testing data, and environmental novelty on model performance. We demonstrate that excluding highly correlated predictor variables does not significantly influence model performance. However, we find that collinearity shift and environmental novelty have significant negative effects on the performance of model transfer. We thus conclude that (a) Maxent is robust to predictor collinearity in model training; (b) the strategy of excluding highly correlated variables has little impact because Maxent accounts for redundant variables; and (c) collinearity shift and environmental novelty can negatively affect Maxent model transferability. We therefore recommend to quantify and report collinearity shift and environmental novelty to better infer model accuracy when models are spatially and/or temporally transferred.
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