Dataset Information

Distribution of tungiasis in latin America: Identification of areas for potential disease transmission using an ecological niche model.

ABSTRACT:

Background

Tungiasis is a neglected tropical disease (NTD) found in Sub-Saharan Africa and Latin America. Despite the high frequency in marginalized populations, little information is available on the geography and estimates of the population at risk in endemic regions. Here we used a geostatistical model to map the potential geographic distribution of areas suitable for tungiasis transmission in Latin America and estimated the at-risk population.

Methods

We developed an ecological niche model (ENM) using tungiasis occurrence records and remotely sensed environmental and socioeconomic data. The potential geographic distribution was then compared to the current population distribution of the region to derive the total population living in urban and rural areas.

Findings

We identified a total of 138 records of occurrences of tungiasis in Latin America, ranging from Mexico to Argentina; 27 reports were not included in the modeling, due to missing detailed geographic information. The occurrences with detailed geographic information (n = 112) included 17 countries in Latin America and the Caribbean. The locations were in environments that primarily consisted of forests (29%), croplands (16•5%), and shrublands (10•9%). We predicted environmentally suitable areas for tungiasis transmission in 45 countries. The estimated human population living in these areas is 450,546,547 with urban centers accounting for 347,007,103 and rural areas 103,539,444. Countries with significant ecological suitability and documented occurrences include Brazil, Colombia, Mexico, Argentina, Bolivia, Ecuador, French Guyana, Guatemala, Haiti, Paraguay, Peru, Trinidad and Tobago, and Venezuela.

Interpretation

This is the first study mapping the potential distribution of tungiasis in Latin America, evidencing the need for population-based studies and elaboration of integrated control measures.

Funding

This project was supported in part by an appointment to the Research Participation Program at the Centers for Disease Control and Prevention administered by the Oak Ridge Institute for Science and Education through an interagency agreement between the U.S. Department of Energy and the Centers for Disease Control and Prevention.

SUBMITTER: Deka MA

PROVIDER: S-EPMC9903635 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Distribution of tungiasis in latin America: Identification of areas for potential disease transmission using an ecological niche model.

Deka Mark A MA Heukelbach Jorg J

Lancet regional health. Americas 20211101

<h4>Background</h4>Tungiasis is a neglected tropical disease (NTD) found in Sub-Saharan Africa and Latin America. Despite the high frequency in marginalized populations, little information is available on the geography and estimates of the population at risk in endemic regions. Here we used a geostatistical model to map the potential geographic distribution of areas suitable for tungiasis transmission in Latin America and estimated the at-risk population.<h4>Methods</h4>We developed an ecologica ...[more]

PMID: 36776459

Similar Datasets

Project description:BackgroundHantavirus Pulmonary Syndrome (HPS) is a rodent-borne zoonosis in the Americas, with up to 50% mortality rates. In Argentina, the Northwestern endemic area presents half of the annually notified HPS cases in the country, transmitted by at least three rodent species recognized as reservoirs of Orthohantavirus. The potential distribution of reservoir species based on ecological niche models (ENM) can be a useful tool to establish risk areas for zoonotic diseases. Our main aim was to generate an Orthohantavirus risk transmission map based on ENM of the reservoir species in northwest Argentina (NWA), to compare this map with the distribution of HPS cases; and to explore the possible effect of climatic and environmental variables on the spatial variation of the infection risk.MethodsUsing the reservoir geographic occurrence data, climatic/environmental variables, and the maximum entropy method, we created models of potential geographic distribution for each reservoir in NWA. We explored the overlap of the HPS cases with the reservoir-based risk map and a deforestation map. Then, we calculated the human population at risk using a census radius layer and a comparison of the environmental variables' latitudinal variation with the distribution of HPS risk.ResultsWe obtained a single best model for each reservoir. The temperature, rainfall, and vegetation cover contributed the most to the models. In total, 945 HPS cases were recorded, of which 97,85% were in the highest risk areas. We estimated that 18% of the NWA population was at risk and 78% of the cases occurred less than 10 km from deforestation. The highest niche overlap was between Calomys fecundus and Oligoryzomys chacoensis.ConclusionsThis study identifies potential risk areas for HPS transmission based on climatic and environmental factors that determine the distribution of the reservoirs and Orthohantavirus transmission in NWA. This can be used by public health authorities as a tool to generate preventive and control measures for HPS in NWA.

Project description:In some transmission foci of Leishmania infantum in Brazil, Lutzomyia cruzi could be considered the main vector of this pathogen. In addition, L. cruzi is a permissive vector of L. amazonensis. Its geographical distribution seems to be restricted and limited to Cerrado and Pantanal biomes, which includes some areas in Brazil and Bolivia. Considering that predicting the distribution of the species involved in transmission cycles is an effective approach for assessing human disease risk, this study aims to predict the spatial distribution of L. cruzi using a multiscale ecological niche model based in both climate and habitat variables. Ecological niche modelling was used to identify areas in South America that are environmentally suitable for this particular vector species, but its presence is not recorded. Vector occurrence records were compiled from the literature, museum collections and Brazilian Health Departments. Bioclimatic variables, altitude, and land use and cover were used as predictors in five ecological niche model algorithms: BIOCLIM, generalised linear model (logistic regression), maximum entropy, random forests, and support vector machines. The vector occurs in areas where annual mean temperature values range from 21.76°C to 26.58°C, and annual total precipitation varies from 1005 mm and 2048 mm. Urban areas were most present around capture locations. The potential distribution area of L. cruzi according to the final ecological niche model spans Brazil and Bolivia in patches of suitable habitats inside a larger climatically favourable area. The bigger portion of this suitable area is located at Brazilian States of Mato Grosso do Sul and Mato Grosso. Our findings identified environmentally suitable areas for L. cruzi in regions without its known occurrence, so further field sampling of sand flies is recommended, especially in southern Goiás State, Mato Grosso do Sul (borders with Mato Grosso, São Paulo and Minas Gerais); and in Bolivian departments Santa Cruz and El Beni.

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.

Dataset Information

Distribution of tungiasis in latin America: Identification of areas for potential disease transmission using an ecological niche model.

Background

Methods

Findings

Interpretation

Funding

Publications

Distribution of tungiasis in latin America: Identification of areas for potential disease transmission using an ecological niche model.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets