Dataset Information

Prediction of high-risk areas for visceral leishmaniasis using socioeconomic indicators and remote sensing data.

ABSTRACT: Spatial heterogeneity in the incidence of visceral leishmaniasis (VL) is an important aspect to be considered in planning control actions for the disease. The objective of this study was to predict areas at high risk for visceral leishmaniasis (VL) based on socioeconomic indicators and remote sensing data. We applied classification and regression trees to develop and validate prediction models. Performance of the models was assessed by means of sensitivity, specificity and area under the ROC curve. The model developed was able to discriminate 15 subsets of census tracts (CT) with different probabilities of containing CT with high risk of VL occurrence. The model presented, respectively, in the validation and learning samples, sensitivity of 79% and 52%, specificity of 75% and 66%, and area under the ROC curve of 83% and 66%. Considering the complex network of factors involved in the occurrence of VL in urban areas, the results of this study showed that the development of a predictive model for VL might be feasible and useful for guiding interventions against the disease, but it is still a challenge as demonstrated by the unsatisfactory predictive performance of the model developed.

SUBMITTER: Almeida AS

PROVIDER: S-EPMC4046095 | biostudies-literature | 2014 May

REPOSITORIES: biostudies-literature

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Prediction of high-risk areas for visceral leishmaniasis using socioeconomic indicators and remote sensing data.

Almeida Andréa S AS Werneck Guilherme L GL

International journal of health geographics 20140520

Spatial heterogeneity in the incidence of visceral leishmaniasis (VL) is an important aspect to be considered in planning control actions for the disease. The objective of this study was to predict areas at high risk for visceral leishmaniasis (VL) based on socioeconomic indicators and remote sensing data. We applied classification and regression trees to develop and validate prediction models. Performance of the models was assessed by means of sensitivity, specificity and area under the ROC cur ...[more]

PMID: 24885128

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Project description:BackgroundParasites' evolution in response to parasite-targeted control strategies, such as vaccines and drugs, is known to be influenced by their population genetic structure. The aim of this study was to describe the population structure of Ethiopian strains of Leishmania donovani derived from different areas endemic for visceral leishmaniasis (VL) as a prerequisite for the design of effective control strategies against the disease.Methodology/principal findingsSixty-three strains of L. donovani newly isolated from VL cases in the two main Ethiopian foci, in the north Ethiopia (NE) and south Ethiopia (SE) of the country were investigated by using 14 highly polymorphic microsatellite markers. The microsatellite profiles of 60 previously analysed L. donovani strains from Sudan, Kenya and India were included for comparison. Multilocus microsatellite typing placed strains from SE and Kenya (n = 30) in one population and strains from NE and Sudan (n = 65) in another. These two East African populations corresponded to the areas of distribution of two different sand fly vectors. In NE and Sudan Phlebotomus orientalis has been implicated to transmit the parasites and in SE and Kenya P. martini. The genetic differences between parasites from NE and SE are also congruent with some phenotypic differences. Each of these populations was further divided into two subpopulations. Interestingly, in one of the subpopulations of the population NE we observed predominance of strains isolated from HIV-VL co-infected patients and of strains with putative hybrid genotypes. Furthermore, high inbreeding irreconcilable from strict clonal reproduction was found for strains from SE and Kenya indicating a mixed-mating system.Conclusions/significanceThis study identified a hierarchical population structure of L. donovani in East Africa. The existence of two main, genetically and geographically separated, populations could reflect different parasite-vector associations, different ecologies and varying host backgrounds and should be further investigated.

Project description:BackgroundMalaria is a significant public health concern in Afghanistan. Currently, approximately 60% of the population, or nearly 14 million people, live in a malaria-endemic area. Afghanistan's diverse landscape and terrain contributes to the heterogeneous malaria prevalence across the country. Understanding the role of environmental variables on malaria transmission can further the effort for malaria control programme.MethodsProvincial malaria epidemiological data (2004-2007) collected by the health posts in 23 provinces were used in conjunction with space-borne observations from NASA satellites. Specifically, the environmental variables, including precipitation, temperature and vegetation index measured by the Tropical Rainfall Measuring Mission and the Moderate Resolution Imaging Spectoradiometer, were used. Regression techniques were employed to model malaria cases as a function of environmental predictors. The resulting model was used for predicting malaria risks in Afghanistan. The entire time series except the last 6 months is used for training, and the last 6-month data is used for prediction and validation.ResultsVegetation index, in general, is the strongest predictor, reflecting the fact that irrigation is the main factor that promotes malaria transmission in Afghanistan. Surface temperature is the second strongest predictor. Precipitation is not shown as a significant predictor, as it may not directly lead to higher larval population. Autoregressiveness of the malaria epidemiological data is apparent from the analysis. The malaria time series are modelled well, with provincial average R2 of 0.845. Although the R2 for prediction has larger variation, the total 6-month cases prediction is only 8.9% higher than the actual cases.ConclusionsThe provincial monthly malaria cases can be modelled and predicted using satellite-measured environmental parameters with reasonable accuracy. The Third Strategic Approach of the WHO EMRO Malaria Control and Elimination Plan is aimed to develop a cost-effective surveillance system that includes forecasting, early warning and detection. The predictive and early warning capabilities shown in this paper support this strategy.

Project description:Visceral leishmaniasis (VL) is an important neglected disease caused by a protozoan parasite, and represents a serious public health problem in many parts of the world. It is zoonotic in Europe and Latin America, where infected dogs constitute the main domestic reservoir for the parasite and play a key role in VL transmission to humans. In Brazil this disease is caused by the protozoan Leishmania infantum chagasi, and is transmitted by the sand fly Lutzomyia longipalpis. Despite programs aimed at eliminating infection sources, the disease continues to spread throughout the Country. VL in São Paulo State, Brazil, first appeared in the northwestern region, spreading in a southeasterly direction over time. We integrate data on the VL vector, infected dogs and infected human dispersion from 1999 to 2013 through an innovative spatial temporal Bayesian model in conjunction with geographic information system. This model is used to infer the drivers of the invasion process and predict the future progression of VL through the State. We found that vector dispersion was influenced by vector presence in nearby municipalities at the previous time step, proximity to the Bolívia-Brazil gas pipeline, and high temperatures (i.e., annual average between 20 and 23°C). Key factors affecting infected dog dispersion included proximity to the Marechal Rondon Highway, high temperatures, and presence of the competent vector within the same municipality. Finally, vector presence, presence of infected dogs, and rainfall (approx. 270 to 540mm/year) drove the dispersion of human VL cases. Surprisingly, economic factors exhibited no noticeable influence on disease dispersion. Based on these drivers and stochastic simulations, we identified which municipalities are most likely to be invaded by vectors and infected hosts in the future. Prioritizing prevention and control strategies within the identified municipalities may help halt the spread of VL while reducing monitoring costs. Our results contribute important knowledge to public and animal health policy planning, and suggest that prevention and control strategies should focus on vector control and on blocking contact between vectors and hosts in the priority areas identified to be at risk.

Project description:IntroductionTo sustain the achievement of kala-azar elimination program (KEP), early detection and treatment of the visceral leishmaniasis (VL) cases and associated modalities such as treatment failure (TF), relapse VL (RVL), and Post-kala-azar dermal leishmaniasis (PKDL) is the cornerstone. A predictive biomarker for VL development and related complications could also play a crucial role in curtailing disease incidence and transmission. Investigations to find a biomarker with prospective capabilities are, however, scarce. Using samples and known clinical outcomes generated within two previous longitudinal cohort studies, we aimed to determine if fluctuations in serum anti-rK39 antibody levels could provide such predictive value.Materials and methodsSerum samples collected at four different time points (Baseline, 12, 18, and 24 months) from 16 patients who had developed VL within the monitoring period and 15 of their asymptomatic healthy controls counterparts were investigated. To investigate potential prediction of VL related complications, serum samples of 32 PKDL, 10 RVL, 07 TF, and 38 cured VL from a single dose AmBisome trial were analyzed. Of this second panel, all patients were monitored for 5 years and sera were collected at four time points (Baseline then 1, 6, and 12 months after treatment). The level of anti-rK39 antibodies in archived samples was measured by a semi-quantitative ELISA.ResultsThe mean antibody level was significantly higher in VL patients compared to their asymptomatic healthy counterparts at each time point. Likewise, we observed a trend toward elevations in antibody levels for PKDL, RVL, TF relative to the reducing levels observed in cured VL. Receiver operating characteristic (ROC) analysis found a promising predictive power of rK39 antibody levels to reveal progression from asymptomatic Leishmania donovani infection stage to VL, defined as 87.5% sensitive and 95% specific. Following treatment, rk39 antibody notably showed 100% sensitivity and 95% specificity in predicting TF.ConclusionOur data indicate that the relative quantity of serum anti-rK39 antibody has promise within either a predictive or prognostic algorithm for VL and VL-related modalities. These could enable VL control programs to implement more effective measures to eliminate the disease. Further research is, however, imperative to standardize the rK39 antibody ELISA between sites prior to broader use.

Dataset Information

Prediction of high-risk areas for visceral leishmaniasis using socioeconomic indicators and remote sensing data.

Publications

Prediction of high-risk areas for visceral leishmaniasis using socioeconomic indicators and remote sensing data.

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