Mapping, Bayesian Geostatistical Analysis and Spatial Prediction of Lymphatic Filariasis Prevalence in Africa
Open Access
- 12 August 2013
- journal article
- research article
- Published by Public Library of Science (PLoS) in PLOS ONE
- Vol. 8 (8), e71574
- https://doi.org/10.1371/journal.pone.0071574
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.Keywords
This publication has 82 references indexed in Scilit:
- Potential Influence of Climate Change on Vector-Borne and Zoonotic Diseases: A Review and Proposed Research PlanEnvironmental Health Perspectives, 2010
- Causal thinking and complex system approaches in epidemiologyInternational Journal of Epidemiology, 2009
- Spatially-explicit risk profiling of Plasmodium falciparum infections at a small scale: a geostatistical modelling approachMalaria Journal, 2008
- Bayesian spatial analysis of a national urinary schistosomiasis questionnaire to assist geographic targeting of schistosomiasis control in Tanzania, East AfricaInternational Journal for Parasitology, 2008
- The Limits and Intensity of Plasmodium falciparum Transmission: Implications for Malaria Control and Elimination WorldwidePLoS Medicine, 2008
- The geographical distribution of lymphatic filariasis infection in MalawiFilaria Journal, 2007
- Spatial epidemiology of human schistosomiasis in Africa: risk models, transmission dynamics and controlTransactions of the Royal Society of Tropical Medicine and Hygiene, 2007
- Developing a spatial-statistical model and map of historical malaria prevalence in Botswana using a staged variable selection procedureInternational Journal of Health Geographics, 2007
- Bayesian Measures of Model Complexity and FitJournal of the Royal Statistical Society Series B: Statistical Methodology, 2002
- A new look at the statistical model identificationIEEE Transactions on Automatic Control, 1974