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Mapping, bayesian geostatistical analysis and spatial prediction of lymphatic filariasis prevalence in Africa.

ABSTRACT: There is increasing interest to control or eradicate the major neglected tropical diseases. Accurate modelling of the geographic distributions of parasitic infections will be crucial to this endeavour. We used 664 community level infection prevalence data collated from the published literature in conjunction with eight environmental variables, altitude and population density, and a multivariate Bayesian generalized linear spatial model that allows explicit accounting for spatial autocorrelation and incorporation of uncertainty in input data and model parameters, to construct the first spatially-explicit map describing LF prevalence distribution in Africa. We also ran the best-fit model against predictions made by the HADCM3 and CCCMA climate models for 2050 to predict the likely distributions of LF under future climate and population changes. We show that LF prevalence is strongly influenced by spatial autocorrelation between locations but is only weakly associated with environmental covariates. Infection prevalence, however, is found to be related to variations in population density. All associations with key environmental/demographic variables appear to be complex and non-linear. LF prevalence is predicted to be highly heterogenous across Africa, with high prevalences (>20%) estimated to occur primarily along coastal West and East Africa, and lowest prevalences predicted for the central part of the continent. Error maps, however, indicate a need for further surveys to overcome problems with data scarcity in the latter and other regions. Analysis of future changes in prevalence indicates that population growth rather than climate change per se will represent the dominant factor in the predicted increase/decrease and spread of LF on the continent. We indicate that these results could play an important role in aiding the development of strategies that are best able to achieve the goals of parasite elimination locally and globally in a manner that may also account for the effects of future climate change on parasitic infection.

SUBMITTER: Slater H

PROVIDER: S-EPMC3741112 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature

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Mapping, bayesian geostatistical analysis and spatial prediction of lymphatic filariasis prevalence in Africa.

Slater Hannah H Michael Edwin E

PloS one 20130812 8

There is increasing interest to control or eradicate the major neglected tropical diseases. Accurate modelling of the geographic distributions of parasitic infections will be crucial to this endeavour. We used 664 community level infection prevalence data collated from the published literature in conjunction with eight environmental variables, altitude and population density, and a multivariate Bayesian generalized linear spatial model that allows explicit accounting for spatial autocorrelation ...[more]

PMID: 23951194

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Project description:BackgroundIn Uganda, malaria and lymphatic filariasis (causative agent Wuchereria bancrofti) are transmitted by the same vector species of Anopheles mosquitoes, and thus are likely to share common environmental risk factors and overlap in geographical space. In a comprehensive nationwide survey in 2000-2003 the geographical distribution of W. bancrofti was assessed by screening school-aged children for circulating filarial antigens (CFA). Concurrently, blood smears were examined for malaria parasites. In this study, the resultant malariological data are analysed for the first time and the CFA data re-analysed in order to identify risk factors, produce age-stratified prevalence maps for each infection, and to define the geographical patterns of Plasmodium sp. and W. bancrofti co-endemicity.MethodsLogistic regression models were fitted separately for Plasmodium sp. and W. bancrofti within a Bayesian framework. Models contained covariates representing individual-level demographic effects, school-level environmental effects and location-based random effects. Several models were fitted assuming different random effects to allow for spatial structuring and to capture potential non-linearity in the malaria- and filariasis-environment relation. Model-based risk predictions at unobserved locations were obtained via Bayesian predictive distributions for the best fitting models. Maps of predicted hyper-endemic malaria and filariasis were furthermore overlaid in order to define areas of co-endemicity.ResultsPlasmodium sp. parasitaemia was found to be highly endemic in most of Uganda, with an overall population adjusted parasitaemia risk of 47.2% in the highest risk age-sex group (boys 5-9 years). High W. bancrofti prevalence was predicted for a much more confined area in northern Uganda, with an overall population adjusted infection risk of 7.2% in the highest risk age-group (14-19 year olds). Observed overall prevalence of individual co-infection was 1.1%, and the two infections overlap geographically with an estimated number of 212,975 children aged 5 - 9 years living in hyper-co-endemic transmission areas.ConclusionsThe empirical map of malaria parasitaemia risk for Uganda presented in this paper is the first based on coherent, national survey data, and can serve as a baseline to guide and evaluate the continuous implementation of control activities. Furthermore, geographical areas of overlap with hyper-endemic W. bancrofti transmission have been identified to help provide a better informed platform for integrated control.

Project description:BackgroundThe persistent spread of Rhodesian human African trypanosomiasis (HAT) in Uganda in recent years has increased concerns of a potential overlap with the Gambian form of the disease. Recent research has aimed to increase the evidence base for targeting control measures by focusing on the environmental and climatic factors that control the spatial distribution of the disease.ObjectivesOne recent study used simple logistic regression methods to explore the relationship between prevalence of Rhodesian HAT and several social, environmental and climatic variables in two of the most recently affected districts of Uganda, and suggested the disease had spread into the study area due to the movement of infected, untreated livestock. Here we extend this study to account for spatial autocorrelation, incorporate uncertainty in input data and model parameters and undertake predictive mapping for risk of high HAT prevalence in future.Materials and methodsUsing a spatial analysis in which a generalised linear geostatistical model is used in a Bayesian framework to account explicitly for spatial autocorrelation and incorporate uncertainty in input data and model parameters we are able to demonstrate a more rigorous analytical approach, potentially resulting in more accurate parameter and significance estimates and increased predictive accuracy, thereby allowing an assessment of the validity of the livestock movement hypothesis given more robust parameter estimation and appropriate assessment of covariate effects.ResultsAnalysis strongly supports the theory that Rhodesian HAT was imported to the study area via the movement of untreated, infected livestock from endemic areas. The confounding effect of health care accessibility on the spatial distribution of Rhodesian HAT and the linkages between the disease's distribution and minimum land surface temperature have also been confirmed via the application of these methods.ConclusionsPredictive mapping indicates an increased risk of high HAT prevalence in the future in areas surrounding livestock markets, demonstrating the importance of livestock trading for continuing disease spread. Adherence to government policy to treat livestock at the point of sale is essential to prevent the spread of sleeping sickness in Uganda.

Project description:BACKGROUND:The initial endemicity (pre-control prevalence) of onchocerciasis has been shown to be an important determinant of the feasibility of elimination by mass ivermectin distribution. We present the first geostatistical map of microfilarial prevalence in the former Onchocerciasis Control Programme in West Africa (OCP) before commencement of antivectorial and antiparasitic interventions. METHODS AND FINDINGS:Pre-control microfilarial prevalence data from 737 villages across the 11 constituent countries in the OCP epidemiological database were used as ground-truth data. These 737 data points, plus a set of statistically selected environmental covariates, were used in a Bayesian model-based geostatistical (B-MBG) approach to generate a continuous surface (at pixel resolution of 5 km x 5km) of microfilarial prevalence in West Africa prior to the commencement of the OCP. Uncertainty in model predictions was measured using a suite of validation statistics, performed on bootstrap samples of held-out validation data. The mean Pearson's correlation between observed and estimated prevalence at validation locations was 0.693; the mean prediction error (average difference between observed and estimated values) was 0.77%, and the mean absolute prediction error (average magnitude of difference between observed and estimated values) was 12.2%. Within OCP boundaries, 17.8 million people were deemed to have been at risk, 7.55 million to have been infected, and mean microfilarial prevalence to have been 45% (range: 2-90%) in 1975. CONCLUSIONS AND SIGNIFICANCE:This is the first map of initial onchocerciasis prevalence in West Africa using B-MBG. Important environmental predictors of infection prevalence were identified and used in a model out-performing those without spatial random effects or environmental covariates. Results may be compared with recent epidemiological mapping efforts to find areas of persisting transmission. These methods may be extended to areas where data are sparse, and may be used to help inform the feasibility of elimination with current and novel tools.

Project description:The world has now facing a health crisis due to outbreak of novel coronavirus 2019 (COVID-19). The numbers of infection and death have been rapidly increasing which result in a serious threat to the social and economic crisis. India as the second most populous nation of the world has also running with a serious health crisis, where more than 8,300,500 people have been infected and 123,500 deaths due to this deadly pandemic. Therefore, it is urgent to highlight the spatial vulnerability to identify the area under risk. Taking India as a study area, a geospatial analysis was conducted to identify the hotspot areas of the COVID-19. In the present study, four factors naming total population, population density, foreign tourist arrivals to India and reported confirmed cases of the COVID-19 were taken as responsible factors for detecting hotspot of the novel coronavirus. The result of spatial autocorrelation showed that all four factors considered for hotspot analysis were clustered and the results were statistically significant (p value < 0.01). The result of Getis-Ord Gi* statistics revealed that the total population and reported COVID-19 cases have got high priority for considering hotspot with greater z-score (> 3 and > 0.7295 respectively). The present analysis reveals that the reported cases of COVID-19 are higher in Maharashtra, followed by Tamil Nadu, Gujarat, Delhi, Uttar Pradesh, and West Bengal. The spatial result and geospatial methodology adopted for detecting COVID-19 hotspot in the Indian subcontinent can help implement strategies both at the macro and micro level. In this regard, social distancing, avoiding social meet, staying at home, avoiding public transport, self-quarantine and isolation are suggested in hotspot zones; together with, the international support is also required in the country to work jointly for mitigating the spread of COVID-19. Electronic supplementary material The online version of this article (10.1007/s41324-020-00375-1) contains supplementary material, which is available to authorized users.

Project description:BackgroundThe World Health Organization (WHO), international donors and partners have emphasized the importance of integrated control of neglected tropical diseases (NTDs). Integrated mapping of NTDs is a first step for integrated planning of programmes, proper resource allocation and monitoring progress of control. Integrated mapping has several advantages over disease specific mapping by reducing costs and enabling co-endemic areas to be more precisely identified. We designed and conducted integrated mapping of lymphatic filariasis (LF) and podoconiosis in Ethiopia; here we present the methods, challenges and lessons learnt.MethodsIntegrated mapping of 1315 communities across Ethiopia was accomplished within three months. Within these communities, 129,959 individuals provided blood samples that were tested for circulating Wuchereria bancrofti antigen using immunochromatographic card tests (ICT). Wb123 antibody tests were used to further establish exposure to LF in areas where at least one ICT positive individual was detected. A clinical algorithm was used to reliably diagnose podoconiosis by excluding other potential causes of lymphoedema of the lower limb.ResultsA total of 8110 individuals with leg swelling were interviewed and underwent physical examination. Smartphones linked to a central database were used to collect data, which facilitated real-time data entry and reduced costs compared to traditional paper-based data collection approach; their inbuilt Geographic Positioning System (GPS) function enabled simultaneous capture of geographical coordinates. The integrated approach led to efficient use of resources and rapid mapping of an enormous geographical area and was well received by survey staff and collaborators. Mobile based technology can be used for such large scale studies in resource constrained settings such as Ethiopia, with minimal challenges.ConclusionsThis was the first integrated mapping of podoconiosis and LF globally. Integrated mapping of podoconiosis and LF is feasible and, if properly planned, can be quickly achieved at nationwide scale.

Project description:BACKGROUND:The island of Hispaniola, shared by Haiti and the Dominican Republic (DR), is the only remaining malaria-endemic island in the Caribbean and accounts for 95% of the lymphatic filariasis (LF) burden in the Americas. Both countries aim to eliminate the diseases by 2020. Migration from Haiti, where both diseases are more prevalent, may promote transmission in the DR. Historically, Haitian migrant labourers live in rural Dominican agricultural 'company towns' called bateyes, many of which received mass drug administration (MDA) for LF elimination. This study sought to determine the prevalence of malaria and LF in bateyes of the DR and to describe related risk factors for disease. METHODS:From March to April 2016, a cross-sectional, cluster survey was conducted across Dominican bateyes stratified into three regions: southwest, north and east. A household questionnaire (n = 776), captured demographics, ethnic origin, mobility patterns, malaria intervention coverage, and knowledge, and recent fever and treatment-seeking. Two individuals per household (n = 1418) were tested for malaria parasites by microscopy and rapid diagnostic test (RDT) and LF antigen by filariasis test strip (FTS). Population-level estimates and confidence intervals (CI) were computed adjusting for the survey design. Two-sided t-tests compared differences in knowledge scores. RESULTS:No (0%) blood sample was Plasmodium-positive by microscopy or RDT. Six individuals were FTS-positive (0.5%; 95% CI: 0.2-1.5), but none (0%) of these were microfilariae-positive. Most batey residents were born in the DR (57.8%), documented (85.0%), and permanent residents (85.1%). Very few respondents (9.4%) reported travel to Haiti in the past year. Overall, half (53.8%) of respondents owned a bed net, and 82.3% of net owners reported using it the previous night. Indoor residual spraying (IRS) differed by region (range: 4.7%-61.2%). Most of those with recent fever sought care (56.0%), yet only 30.5% of those seeking care were tested for malaria. Compared to Dominican-born populations, Haitian-born respondents more frequently reported recent fever, did not seek care for the fever, had not heard of malaria, and could not name symptoms or prevention methods. CONCLUSIONS:Malaria and LF transmission appear absent or extremely low in Dominican bateyes, which are a mixture of Haitian and Dominican residents. Travel to Haiti is rare, meaning risk of malaria and LF importation is low. Addressing identified gaps in intervention coverage, malaria knowledge, treatment seeking and service delivery will improve the quality of surveillance for these diseases, particularly among marginalized populations and promote island-wide elimination.

Dataset Information

Mapping, bayesian geostatistical analysis and spatial prediction of lymphatic filariasis prevalence in Africa.

Publications

Mapping, bayesian geostatistical analysis and spatial prediction of lymphatic filariasis prevalence in Africa.

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