Prediction of high-risk areas for visceral leishmaniasis using socioeconomic indicators and remote sensing data.
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ABSTRACT:
SUBMITTER: Almeida AS
PROVIDER: S-EPMC4046095 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Visceral leishmaniasis (VL) or kala-azar mainly affects children in endemic areas. This study was conducted to determine the seroprevalence of VL using direct agglutination test (DAT) in children living in rural districts of Alborz Province located 30 km from Tehran capital city of Iran. Multi-stage cluster random sampling was applied. Blood samples were randomly collected from 1,007 children under 10 years of age in the clusters. A total of 37 (3.7%) of the studied population showed anti-Leishmania infantum antibodies with titers of ≥1:800. There was a significant association between positive sera and various parts of the rural areas of Alborz Province (P<0.002). Two children with anti-Leishmania infantum antibodies titers of ≥1:3,200 indicated kala-azar clinical features and treated with anti-leishmaniasis drugs in pediatric hospital. The findings of this study indicated that Leishmania infection is prevalent in rural areas of Alborz Province. Therefore, it is necessary to increase the awareness and alertness among physicians and public health managers, particularly in high-risk rural areas of the province in Iran.
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Project description:Introduction: The armamentarium of antileishmanial drugs is small. It is further being threatened by the development of resistance and decreasing sensitivity to the available drugs. The development of newer drugs is sorely needed. Areas covered: The authors have based their review on a literature search performed using PubMed. The article specifically looks at investigational drugs, which have demonstrated, at the very least, in vitro and in vivo activities against the leishmania species that cause visceral leishmaniasis. Specifically, the authors review the nitroimidazole compound fexinidazole, which is one of the few drugs which have reached Phase II trials. The article also discusses the R enantiomer of (S)-PA-824, which has shown good antileishmanial activity. Finally, the article also highlights the many novel delivery systems and oral formulations of amphotericin B, which are both cheap and less toxic and are currently under investigation. Expert opinion: Very few new drugs have reached the clinic for this neglected tropical disease and there is an urgent need for new efficacious therapeutics. The authors believe that support from public-private partnerships would help in enabling the prompt development of drug candidates that could potentially make the clinic.
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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.
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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.
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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.
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Project description:BackgroundControl of visceral leishmaniasis (VL) on the Indian subcontinent has been highly successful. Control efforts such as indoor residual spraying and active case detection will be scaled down or even halted over the coming years. We explored how after scale-down, potential recurrence of VL cases may be predicted based on population-based surveys of antibody or antigenemia prevalence.MethodsUsing a stochastic age-structured transmission model of VL, we predicted trends in case incidence and biomarker prevalence over time after scaling down control efforts when the target of 3 successive years without VL cases has been achieved. Next, we correlated biomarker prevalence with the occurrence of new VL cases within 10 years of scale-down.ResultsOccurrence of at least 1 new VL case in a population of 10 000 was highly correlated with the seroprevalence and antigenemia prevalence at the moment of scale-down, or 1 or 2 years afterward. Receiver operating characteristic curves indicated that biomarker prevalence in adults provided the most predictive information, and seroprevalence was a more informative predictor of new VL cases than antigenemia prevalence. Thresholds for biomarker prevalence to predict occurrence of new VL cases with high certainty were robust to variation in precontrol endemicity.ConclusionsThe risk of recrudescence of VL after scaling down control efforts can be monitored and mitigated by means of population-based surveys. Our findings highlight that rapid point-of-care diagnostic tools to assess (preferably) seroprevalence or (otherwise) antigenemia in the general population could be a key ingredient of sustainable VL control.
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Project description:Forest fires are one of the significant disturbances in forest ecosystems. It is essential to extract burned areas rapidly and accurately to formulate forest restoration strategies and plan restoration plans. In this work, we constructed decision trees and used a combination of differential normalized burn ratio (dNBR) index and OTSU threshold method to extract the heavily and mildly burned areas. The applicability of this method was evaluated with three fires in Muli County, Sichuan, China, and we concluded that the extraction accuracy of this method could reach 97.69% and 96.37% for small area forest fires, while the extraction accuracy was lower for large area fires, only 89.32%. In addition, the remote sensing environment index (RSEI) was used to evaluate the ecological environment changes. It analyzed the change of the RSEI level through the transition matrix, and all three fires showed that the changes in RSEI were stronger for heavily burned areas than for mildly burned areas, after the forest fire the ecological environment (RSEI) was reduced from good to moderate. These results realized the quantitative evaluation and dynamic evaluation of the ecological environment condition, providing an essential basis for the restoration, decision making and management of the affected forests.
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