Modelling the distribution and transmission intensity of lymphatic filariasis in sub-Saharan Africa prior to scaling up interventions: integrated use of geostatistical and mathematical modelling.

Paula Moraga,T Déirdre Hollingsworth,Maria P Rebollo,Moses J Bockarie,Sammy M Njenga,John O Gyapong,Rachel L Pullan,Emmanuel Davies,Simon J Brooker,Rebecca F Baggaley,Birgit Nikolay,Jorge Cano,Manoj Gambhir

doi:10.1186/s13071-015-1166-x

Abstract

BackgroundLymphatic filariasis (LF) is one of the neglected tropical diseases targeted for global elimination. The ability to interrupt transmission is, partly, influenced by the underlying intensity of transmission and its geographical variation. This information can also help guide the design of targeted surveillance activities. The present study uses a combination of geostatistical and mathematical modelling to predict the prevalence and transmission intensity of LF prior to the implementation of large-scale control in sub-Saharan Africa.MethodsA systematic search of the literature was undertaken to identify surveys on the prevalence of Wuchereria bancrofti microfilaraemia (mf), based on blood smears, and on the prevalence of antigenaemia, based on the use of an immuno-chromatographic card test (ICT). Using a suite of environmental and demographic data, spatiotemporal multivariate models were fitted separately for mf prevalence and ICT-based prevalence within a Bayesian framework and used to make predictions for non-sampled areas. Maps of the dominant vector species of LF were also developed. The maps of predicted prevalence and vector distribution were linked to mathematical models of the transmission dynamics of LF to infer the intensity of transmission, quantified by the basic reproductive number (R0).ResultsThe literature search identified 1267 surveys that provide suitable data on the prevalence of mf and 2817 surveys that report the prevalence of antigenaemia. Distinct spatial predictions arose from the models for mf prevalence and ICT-based prevalence, with a wider geographical distribution when using ICT-based data. The vector distribution maps demonstrated the spatial variation of LF vector species. Mathematical modelling showed that the reproduction number (R0) estimates vary from 2.7 to 30, with large variations between and within regions.ConclusionsLF transmission is highly heterogeneous, and the developed maps can help guide intervention, monitoring and surveillance strategies as countries progress towards LF elimination.Electronic supplementary materialThe online version of this article (doi:10.1186/s13071-015-1166-x) contains supplementary material, which is available to authorized users.

Highlights

Lymphatic filariasis (LF) is one of the neglected tropical diseases targeted for global elimination
M. africana and Culex mosquitoes show a more limited and distinct distribution: Culex mosquitoes occur in eastern Africa, east coast of Madagascar and in restricted areas of west Africa (Fig. 3b), whereas M. africana occurs in west and middle Africa, and coastal areas of east Africa and Madagascar (Fig. 3c)
The transmission of LF is spatially heterogeneous and it is essential that intervention, monitoring and surveillance strategies take this variation into account

Summary

Introduction

Lymphatic filariasis (LF) is one of the neglected tropical diseases targeted for global elimination. The ability to interrupt transmission is, partly, influenced by the underlying intensity of transmission and its geographical variation This information can help guide the design of targeted surveillance activities. After stopping MDA, programmes will still need to conduct surveillance to ensure transmission has not re-emerged (i.e. there has been no recrudescence). Surveillance can be stratified according to the risk of recrudescence This risk may be predicted from analysis of the historical, pre-intervention transmission levels, vector type and capacity and environmental and demographic factors known to influence the intrinsic sensitivity (receptivity) of transmission [4,5,6]. Recent work at the country level highlights the environmental, socio-demographic, and intervention drivers of LF and how this information can be used to stratify areas according to likelihood of transmission being interrupted or persisting [7,8,9]. Other work has used Bayesian geostatistical modelling to predict the distribution of LF at country [10] and continental [11] scales

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