- Research
- Open Access
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^{1},
- Jorge Cano^{1}Email author,
- Rebecca F. Baggaley^{2},
- John O. Gyapong^{3},
- Sammy M. Njenga^{4},
- Birgit Nikolay^{1},
- Emmanuel Davies^{5},
- Maria P. Rebollo^{6},
- Rachel L. Pullan^{1},
- Moses J. Bockarie^{7},
- T. Déirdre Hollingsworth^{8, 9},
- Manoj Gambhir^{10} and
- Simon J. Brooker^{1}
- Received: 15 June 2015
- Accepted: 14 October 2015
- Published: 24 October 2015
Abstract
Background
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 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.
Methods
A 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 (R_{0}).
Results
The 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 (R_{0}) estimates vary from 2.7 to 30, with large variations between and within regions.
Conclusions
LF transmission is highly heterogeneous, and the developed maps can help guide intervention, monitoring and surveillance strategies as countries progress towards LF elimination.
Keywords
- Lymphatic filariasis
- Wuchereria bancrofti
- Bayesian geostatistical modelling
- Mathematical modelling
- Basic reproductive number
- Sub-Saharan Africa
Background
Lymphatic filariasis (LF) is a mosquito-borne disease caused by the filarial worms, Wuchereria bancrofti, Brugia malayi and B. timori. Since the launch of the Global Programme to Eliminate Lymphatic Filariasis in 2000 an estimated total of 975 million people, including 198 million in sub-Saharan Africa, have benefitted from mass drug administration (MDA) programmes that deliver antifilarial drugs [1]. As a result of these efforts, it is estimated that the global prevalence of infection has decreased by 30 % and 18.2 million cases of LF morbidity have been averted [1]. At a country level, an increasing number of national LF control programmes have completed five or more rounds of MDA and, as a consequence, are conducting transmission assessment surveys (TAS) to determine whether prevalence of LF is <1 % and MDA can stop [2]. After stopping MDA, programmes will still need to conduct surveillance to ensure transmission has not re-emerged (i.e. there has been no recrudescence). This can either be achieved by periodic surveys, for example, by repeating a TAS 2–3 years after stopping MDA, or through screening of routine blood samples [3]. For the process of verification, countries will also need to conduct surveillance in areas judged to be non-endemic at the start of the programme. To help reduce costs, 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 [4–6]. 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 [7–9]. Other work has used Bayesian geostatistical modelling to predict the distribution of LF at country [10] and continental [11] scales.
In this paper, we use a combination of Bayesian geostatistical and mathematical modelling to develop maps of the prevalence and transmission intensity of bancroftian filariasis in sub-Saharan Africa (SSA) prior to large-scale control. We use these maps to inform a stratified approach to LF surveillance. The work builds on recent work to develop a global atlas of LF infection [12], conducted as part of the Global Atlas of Helminth Infections project (www.thiswormyworld.org) [13].
Methods
Data sources and data selection
Data on the prevalence of LF infection were identified from searches of the formal and informal literature and direct communication with LF control programmes. Details of the search strategies, inclusion criteria, data abstraction and geolocation procedures are provided by Cano et al. [12]. In brief, only population-based survey data based on random sampling were included, whereas data from non-random data, including surveys from hospitals, prisons, mental institutions or military facilities, were excluded. In the current analysis, only surveys conducted prior to the implementation of countrywide, population-based MDA were included. Infection prevalence was defined as either (i) the proportion of surveyed individuals with detectable microfilaraemia or (ii) the proportion of surveyed individuals with detectable antigenaemia. A table with a brief description of the surveys compiled and used in this work is provided as supplemental information (see Additional file 1).
Microfilaraemia (mf) was estimated from examination of thick blood films collected during night blood surveys to detect the presence of microfilariae using microscopy [14, 15]. It is generally assumed that the sensitivity of methods for detecting microfilaraemia is influenced by the volume of blood sampled and previous authors have adjusted prevalence estimates according to blood volume [16]. We therefore investigated this issue further but found that although estimates of infection prevalence varied according to blood volume, we were unable to derive consistent adjustment factors and therefore did not adjust prevalence estimates according to blood volume (see Additional file 1). It is also known that concentration and counting chamber methods have greater sensitivity [17] and therefore where estimates were derived using both concentration methods and thick blood smears (n = 10 surveys), we used only the thick smear data.
Wuchereria bancrofti antigenaemia was typically estimated using an immuno-chromatographic card test (ICT) [14, 18]. These tests are more sensitive than mf detection and can be conducted on blood collected at any time of the day and therefore since 2000 have been the diagnostic method of choice for mapping the distribution of LF caused by W. bancrofti. Recent work has highlighted potential cross-reactivity of the ICT test with Loa loa [19], therefore we excluded ICT-based surveys conducted in areas of L. loa transmission (n = 314), as defined by Zouré et al. [20].
Covariate variables
There are a number of climatic, environmental and demographic factors that influence the transmission and epidemiology of LF, including temperature, humidity, elevation and population density [12, 23–25]. We generated a suite of climatic and environmental covariates from surfaces interpolated from meteorological stations or remote sensing imagery, including estimates of mean, minimum and maximum temperature and precipitation at 1 km^{2} resolution [26], averaged long-term estimates of land surface temperature [27], elevation at 1 km^{2} resolution [28], and aridity index [28], averaged enhanced vegetation index for the period 2000 to 2012 [27], and land cover data [29]. Estimates of population density were obtained from the Gridded Population of the World [30] and the United Nations Environment Programme [31], which we used to classify areas as urban, peri-urban or rural areas, based on the assumption that urban extents (UE) have a population densities ≥1000 persons/km^{2}, peri-urban >250 persons/km^{2} within a 15 km distance from the UE edge, and rural <250 persons/km^{2} and/or >15 km from the UE edge. From these datasets of population density, population growth rate for the period 1960 to 2010, as a measure of the change rate in population over this period, was also calculated. A detailed description of each covariate and source is provided in the supplemental file (Additional file 2: Table S3). Each covariate grid was resampled to a 10 km spatial resolution using bilinear interpolation for continuous surface and a majority approach for categorical data [32]. Covariate data were extracted to survey locations using ArcGIS Desktop v10.2 (Environmental Systems Research Institute Inc., Redlands CA, USA).
Vector distribution maps
LF is transmitted mainly by mosquito species belonging to the Anopheles, Culex, and Mansonia genera and to lesser extent Aedes, Coquillettidia and Ochlerotatus genera [33, 34]. Anopheles mosquitoes are the main vectors of LF through much of west and central Africa and inland east Africa, whereas Culex species are the main vectors in coastal east and southern Africa [33]. Studies have shown that the survival, parasite uptake and development of infective L3 stages and overall transmission potential varies by vector species [35–37]. In order to capture these species differences, we developed maps of the distribution of each dominant LF vector species: Anopheles, Culex and Mansonia.
Geostatistical modelling approach
The prevalence of mf and prevalence of antigenaemia were modelled separately due to a poor correlation between outcomes and the inability to predict one from another, as detailed elsewhere [43]. Bayesian geostatistical models, which included both fixed and random effects, were used to predict the spatial distribution of each outcome. Fixed effects quantify the effects of the covariates on LF infections, whereas random effects account for unexplained spatial variation whose structure can eventually help identify anomalous areas of high or low risk that can be further investigated.
where Κ _{ υ } is the modified Bessel function of second kind and order υ > 0. The integer value of υ determines the mean square differentiability of the underlying process. υ is usually fixed since typically it is poorly identified in applications. σ ^{2} is the marginal variance and k > 0 is a scaling parameter related to the range ρ, the distance at which the S(x _{ i }) and S(x _{ j }) are almost independent. In particular, we used the empirically derived definition \( \rho = \sqrt{8\upupsilon}/k \), with ρ corresponding to the distance at which the spatial correlation is approximately 0.1. We also include in the model a spatially unstructured random effect, u _{ i }, that captures the effects of unmeasured characteristics that affectall members in the survey. u _{ i } are assumed to be independent zero-mean Gaussian distributed with precision τ _{ u }.
where |a| <1, and ξ(x _{ i }, 1) follows the stationary distribution of a first-order autoregressive process, namely N(0, σ _{w} ^{2} /(1 − a ^{2})). W(x _{ i }, t) is a spatial correlation term. Each w(x _{ i }, t) follows a zero-mean Gaussian distribution. The w(x _{ i }, t) is temporally independent but spatially dependent at each time t, with Matérn covariance function. u _{ it } denotes the unstructured random effect and are assumed to be independent zero-mean Gaussian distributed with precision τ _{ u }.
Variable selection and model development
where \( l\left(\widehat{\theta}\right) \) is the maximum log-likelihood function and k is the number of parameters. After that, we explored further simplification of the model by backward elimination of selected variables until it was no longer possible to reduce AIC by elimination of any of the remaining variables.
The age group (either below or above 15 years of age) of individuals studied on the prevalence surveys was considered at every step of the modelling process. In addition, population density was considered for the periods at which the surveys were undertaken. Since ICT-based data were available for surveys conducted from 1990 onwards, estimates of population density at 2000 were used. For mf data, in order to take into account temporal changes in population density, gridded maps of population density at 1960, 1980 and 2000 were used accordingly with the period of survey (1950–1969, 1970–1989, 1990–onwards).
The selection of covariates at this stage in the overall model-fitting process ignores spatial and temporal correlation, and as a result is likely to over-state the statistical significance of covariate effects. In the final stage of the model-fitting process we re-assess the covariate effects and their significance within a spatial mixed model for the ICT data and a spatiotemporal mixed model for the Mf data that take into account the spatial and spatiotemporal correlation, respectively.
Model validation
We assessed the predictive ability of the model using a leave-one-out cross-validation procedure. In this approach, a single observation is retained as the validation data, and the spatial model is fitted to the remaining data. Then, the observation in the validation data is predicted using the fitted model. The validation data needs to spatially represent the whole region where the prevalence is predicted. Therefore, instead of repeating the cross-validation procedure using each observation once as the validation data, we used each of the locations of a spatially representative sample of the prediction surface. To obtain a valid data set, 20 % of the observations were sampled without replacement where each observation had a probability of selection proportional to the area of the Thiessen polygon surrounding its location, that is, the area closest to the location relative to the surrounding points.
where m is the number of observations in the validation data, p*(x_{i}) is the predicted prevalence, and p(x_{i}) is the observed prevalence at location x_{i}, i = 1, …, m.
Implementation and spatial prediction
where G is the number of vertices in the triangulation, ψ _{ g }(⋅) are piecewise polynomial basis functions on each triangle, and {S _{ g }} are zero-mean Gaussian distributed weights. The INLA and SPDE approaches are easily applied thanks to their implementation in the R-INLA software package [5].
We assigned a flat improper prior for the intercept, \( {\beta}_0\sim N\left(0,{\tau}_{\beta_0}^{-1}\right) \) with \( {\tau}_{\beta_0}=0 \), and independent vague Gaussian priors with fixed precision for all other components of the fixed effects, β _{ i } ∼ N(0, 1/0.001), i = 1, … p. The smoothness parameter ν was considered fixed to 1 implying a continuous domain Markov field. In the spatial model, the vector of weights S = (S _{1,.}.., S _{ G })′ is assigned a Gaussian distribution, S ∼ N(0, Q ^{− 1}) where Q is a sparse precision matrix depending on the Matérn covariance function parameter κ and variance σ ^{2}. In the spatiotemporal model, for ξ = (ξ _{1}, …, ξ _{ n }) ', we use a Gaussian prior with zero mean and precision matrix depending on the autocorrelation hyperparameter a, k and σ _{w} ^{2} . To complete the models, we assign τ _{ u } a vague Gamma prior.
The model output provided the posterior distribution for each of the model parameters. We summarized these distributions to obtain the posterior mean and 95 % CI of the fixed effects and hyperparameters. Predictions of infection prevalence were provided at a spatial resolution of 10 km. Maps showing the predicted LF prevalence as well as its uncertainty were generated by summarizing the posterior distributions of the prevalence obtained at each of the prediction locations. Specifically, the predicted prevalence was represented as the posterior mean, and its uncertainty was represented using the 95 % CI. Predictions were standardized to provide estimates for entire communities (children and adults combined) and for the period 2000-onwards.
Mathematical modelling of the intensity of LF transmission
The prevalence of LF infection provides a useful metric to guide the planning of control, but provides limited insight to the dynamics of transmission. Instead the intensity of infection transmission is best quantified by the basic reproduction number, R_{0}, which for macroparasites such as LF can be defined as the average number of female offspring per adult female worm surviving to reproduction (in the absence of density-dependence i.e. phenomena such as host immunity or worm mating probability, which accelerate or curtail the production of parasite life stages) [48]. Existing analytic expressions for R_{0} for LF, and helminths with similar natural history such as onchocerciasis, are defined in terms of average worm burden or microfilarial load in a community [49–51], yet the majority of LF studies only present data on the prevalence of infection. In the framework published by Gambhir et al. a simple, differential equation transmission model of LF is used to estimate R_{0} for a given prevalence value [6]. This model assumes various density-dependent functions which alter the rates of transition between state variables in the model (for example, the rate of parasite establishment and rate of parasite fecundity). The model parameter prior distributions were defined using data from the literature and parameter posteriors were found by fitting to baseline mf prevalence data from low, moderate and high transmission settings in Tanzania [52] – settings which are representative of much of SSA.
For a setting with a particular endemic prevalence, there is an underlying bite rate and force of infection which leads to this endemic equilibrium. This parameter contributes to R_{0} (as explained in detail below), but can only be calculated indirectly through the method described in detail by Gambhir et al. [53] and outlined here.
where λ, α and β are the ‘immigration’ rates of each of the life stages (adult worms, microfilariae, and L3 infective larvae respectively; λ is a composite parameter, incorporating the mosquito-human biting rate which is estimated using the methodology describe above; μ, δ and σ are the death rates of each of the life stages respectively; and f _{1}(I), f _{2}(W), f _{3}(W), f _{4}(M) represent the modifying density-dependent functions acting on each of the respective parasite life-stage intensities (W, the number of adult worms per definitive host, M, the mf load per host, refer to parasite life stages within the definitive host population, whereas the L3 infective larval load per mosquito (larvae develop through L1 and L2 stages but only become infective once they reach the L3 stage), L, refers to the parasite life-stage within the vector host. The host immunity variable, I, increases in magnitude at a rate equal to the adult worm burden but can also decay over time, allowing the model to capture the waning of immunity). Note that for R _{ 0,} host immunity levels and worm burdens are assumed to be zero as this expression calculates the number of offspring in a wholly susceptible population. The above mathematical model was initially fitted separately for settings where either Anopheles or Culex species are the predominant vector, based on the develop vector distribution maps (Fig. 3). However, the relationship between the mf prevalence and R_{0} is practically identical for Anopheles or Culex species, as the model assumes similar mosquito life expectancy for each species, and therefore we present a single relationship between prevalence of mf and R_{0}. Finally, the threshold mf prevalence value above which LF transmission can persist, which is higher than the prevalence at which R _{0} = 1 due to the density dependencies assumed in the modelling framework [53], was calculated.
Results
Collated survey data
Summary of data on the prevalence of microfilaraemia (mf) and prevalence of antigenaemia, based on immuno-chromatographic card test (ICT) by region and time period. Median and inter-quartile range (IQR) are presented
Mf prevalence | ICT prevalence | |||||||
---|---|---|---|---|---|---|---|---|
1950–1969 | 1970–1989 | 1990–onwards | 1990–onwards | |||||
Region | N | Median (IQR) | N | Median (IQR) | N | Median (IQR) | N | Median (IQR) |
Eastern | 46 | 14.1 (2.6–34.6) | 109 | 15.8 (4.6–30.2) | 137 | 11.5 (2.8–21.6) | 2002 | 0 (0–1) |
Middle | 21 | 0 (0–2.7) | 38 | 0 (0–0) | 31 | 1 (0–1.2) | 564 | 1 (0–5.5) |
Northern | 3 | 22 (0–26.8) | 1 | 10 | - | - | - | - |
Western | 304 | 6.8 (1.2–21.7) | 190 | 7.3 (2–15.9) | 265 | 6.3 (1.2–15.1) | 629 | 6 (0–26) |
Total | 374 | 7.1 (1.1–22) | 338 | 8.1 (0.8–18.9) | 433 | 6.5 (1.2–17) | 3195 | 0 (0–4.8) |
Vector distribution maps
Figure 3 presents maps of the predicted distributions of Anopheles, Culex, and Mansonia species. Anopheles mosquitoes of gambiae and funestus complexes are widely distributed across sub-Saharan Africa (Fig. 3a). In contrast, 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). A wider presence of Culex mosquitoes is predicted in Nigeria, north-east of Cameroon and north of Angola. Areas where the distribution of each of the major LF mosquito vectors occur across west Africa and coastal areas of east Africa (Fig. 3d).
Spatial prediction of LF prevalence
The ICT-based model indicate large areas of high prevalence (above 40 %) are predicted along the coast of east Africa, from southern Kenya to central regions of Mozambique, and the east coast of Madagascar. High prevalences of antigenaemia are predicted across large areas of west Africa and pockets at the north-west of the Democratic Republic of Congo (DRC) and central Congo. In general, lower (<5 %) estimates of ICT prevalence are predicted in inland areas of east Africa (Ethiopia, Rwanda, Burundi, central and south of Uganda) and middle Africa (southern and central DRC).
Validation statistics for spatio-temporal model for mf prevalence and spatial model for ICT-based prevalence
Model | Correlation | Mean Error | Mean Absolute Error | Coverage percentage of 95 % CI |
---|---|---|---|---|
mf | 0.69 | 1.89 % | 4.64 % | 70.93 % |
ICT | 0.86 | 0.06 % | 6.51 % | 52.83 %^{a}, 77.49 %^{b} |
Predictive distribution of R_{0}
Discussion
Here we present maps of the distribution of LF microfilaraemia and antigenaemia across SSA under a pre-intervention scenario, based on Bayesian geostatistical modelling. We also present the first attempt to map geographical variation in R_{0}, which reflects differential risks of recrudescence and can be used to inform the design of post-MDA surveillance activities.
The geostatistical maps presented here indicate lower and more focal patterns of mf compared patterns of antigenaemia. The maps also suggest a more restricted distribution of mf compared to previous mapping work by Slater and Michael [11] which suggested higher levels of infection overall and the occurrence of transmission where LF has never or only occasionally been reported. For example, the Slater and Michael model predict mf prevalences above 75 % in Sudan and South Sudan-Ethiopia border, whereas our predicted prevalence <10 % and recent surveys using ICTs found low levels of antigenaemia [55–57]. Our predicted distribution of antigenaemia is also consistent with previous country-level models [8, 58–60]. Uncertainty in predictions based on mf prevalence data is greatest for central Africa, primarily driven by a scarcity of mf surveys conducted since 1990. This lack of contemporary data is compensated in part by the inclusion of older data and a temporal effect in the final model.
Here we also present the first composite map of the distribution of dominant LF vector species in SSA. Anopheles mosquitoes, mostly those species belonging to the gambiae and funestus complexes, are considered the major LF vector in SSA, particularly in rural settings where LF is more prevalent [33]. Mosquitoes of the Cx. pipiens complex, especially Cx. quinquefasciatus, are suggested to be important vector of LF in eastern Africa, especially in urban and peri-urban settings [61, 62]. In west Africa, however, it is widely accepted that Anopheles species are the main vectors since Culex mosquitoes, despite being ubiquitous and potential vectors in the region [63], are considered to transmit LF poorly [64–66]. Interestingly, the areas where at least two vector genera occur coincide with areas showing the highest estimates of antigenaemia and microfilaraemia. This apparent concordance makes us think that a more efficient and higher intensity of LF transmission is expected where multiple potential LF vectors occur.
Strengths and challenges in LF mapping
The presented work has a number of strengths and limitations. Our use of Bayesian model-based geostatistics provides a flexible and robust approach for spatial modelling that takes into account the spatial dependence structure of the data and provides a formal expression of uncertainty in the prediction estimates [67]. Traditionally, Bayesian predictive inference has been implemented via MCMC methods which make inference tractable for complex models but present problems in terms of convergence and computational time [68]. We overcome these issues by using the INLA approach [46] which is a computationally effective alternative to MCMC designed for latent Gaussian models. By using a combination of analytical approximation and numerical integration, INLA produces fast and accurate approximations to posterior distributions. The analysis of spatial point data is possible by combining INLA and SPDE approaches [47], whereby a continuously indexed Gaussian field is represented as a discretely indexed Gaussian Markov random field (GMRF) using a basis function representation defined on a triangulation of the domain of interest.
Intensity of LF transmission is influenced by a complexity of factors, yet our models incorporated only environmental and demographic drivers of LF transmission in pre-intervention scenarios. The models do not incorporate socioeconomic status [69–72] or the coverage of LF interventions, including MDA and vector control, which will influence transmission, especially once interventions have been scaled up. The role of vector control measures in modifying patterns of LF transmission has recently been highlighted in The Gambia where the large-scale distribution of bed nets for malaria control has, in part, contributed to the elimination of LF in the country [73]. In coastal Kenya, large-scale distribution of ITNs for malaria control has been proposed to sustain reduction of LF infection levels, even after MDA is interrupted [74]. An increasing number of studies have demonstrated the impact of repeated rounds of albendazole and ivermectin alter patterns of transmission [75] and over time dramatically reduce transmission [76, 77]. A methodological challenge for incorporating intervention-related factors into future models is that much of the available data are presented at an area-level. Bayesian hierarchical models have been used to address this problem by providing a natural way to combine data from different sources taking into account the different uncertainties [78–80]. However, such models have three important limitations. First, they rely on MCMC methods for Bayesian inference which are computationally intensive and may become unfeasible for large datasets. Second, few approaches for temporally misaligned data exist and they do not account for correlation in spatial random effects over time. Third, correlation studies use the predictions from several models as covariates in regression models and as such predictions contain error as the predicted values do not equal the true exposures. These important issues indicate that there is considerable scope to develop new model-based methods for the analysis of misaligned data.
The vector distribution maps are also not without their limitations. First, different methodologies were used for different genera: maps for Culex and Mansonia were constructed by using maximum entropy ecological niche (MaxEnt) modelling [40]; and boosted regression tree (BRT) modelling was used to modelling environmental suitability for Anopheles mosquitoes [38]. Second, there is a lack of data for some areas of central and northeast Africa, introducing imprecision into maps.
Finally, the presented map of R0 is based on a mathematical model of transmission dynamics [53]. The relationship between prevalence and R0 is non-linear and there are very high values of R0 for moderate to high levels of prevalence, suggesting great challenges for control. However, it should be remembered that these values are for a population when there is no immunity, unlike endemic communities, and that we do not yet have a reliable estimate for how long this immunity will last. Therefore the high values of R0 may not represent such a large challenge for control in the short term. For recrudescence or re-emergence however, they may represent a higher risk. The model uses prevalence to estimate transmission intensity because of the availability of prevalence data. Alternative measures of transmission intensity include annual transmission potential and annual biting rates [81], but these are very rarely measured. Ideally, the model would have been parameterised with data from a range of transmission settings, but there are a lack of detailed data and we recognise that transmission dynamics will vary in settings other than Tanzania from where data were used to parameterise the model.
Mapping LF to guide control and surveillance
The developed map of R_{0} can be used to identify areas of higher transmission intensity where additional control efforts may be required to achieve the elimination of LF infection (i.e. implementing vector control or extending MDA rounds). Our model predicts a marked geographical heterogeneity in the intensity of transmission in SSA. Current MDA strategies do not account for this spatial heterogeneity, and therefore coverage targets and implementation schemes are common for all endemic areas [3]. The precise level and duration of treatments to achieve LF elimination in different endemic regions remains unknown [82], such that it is difficult to decide when to stop ongoing MDA interventions [51]. Indeed, although some model predictions suggest that LF transmission can be interrupted by annual MDA alone, it is not clear that this can be achieved everywhere with 4–6 yearly rounds of MDA [83].
In recent years, some experts have suggested that current control programmes based on standard MDA schemes should give rise to more tailored control interventions adapted to the local specificities of LF transmission [6, 14, 84]. Our maps provide programme managers with a thorough picture of the initial level of LF endemicity and potential geographical variation in the intensity of transmission prior to the implementation of large-scale interventions. The R_{0} maps also provide insight into (i) the risk of persistent transmission despite repeated MDA and highlight that pockets of residual transmission may occur in settings where overall transmission has declined [77], and (ii) the possibility of recrudescence, especially where transmission is highly heterogeneous [85]. Careful use of the developed maps can guide decision makers and ultimately programme managers first to define the most appropriate control strategy and adapt it to local conditions of transmission and second to identify areas which may require special attention during monitoring and surveillance stages. How to integrate an understanding of the spatial heterogeneity of LF into the design of optimal LF surveillance is the subject of ongoing work.
Conclusions
The transmission of LF is spatially heterogeneous and it is essential that intervention, monitoring and surveillance strategies take this variation into account. The developed maps of mf, antigenaemia and R_{0} are intended to contribute to this effort. In particular, our maps can help guide control programmes in future LF surveillance activities by identifying areas of higher intensity of transmission that may require enhanced and tailored interventions and by setting a benchmark for evaluating the impact of scaling up of LF interventions on the pathway to LF elimination. Country maps displaying the results of this modelling work will be available at the website of the Global Atlas of Helminth Infection project (www.thiswormyworld.org).
Declarations
Acknowledgements
The work would have not been possible without the generous contribution of data by a large number of people– for a full list of acknowledgements see: www.thiswormyworld.org. We also thank comments from Katherine Gass and Pat Lammie. This work received financial support from the Coalition for Operational Research on the NTDs (COR-NTD) which is funded at the Task Force for Global Health by the Bill & Melinda Gates Foundation through its Neglected Tropical Diseases Program. The Wellcome Trust through a Senior Research Fellowship to SJB (098045), the Bill & Melinda Gates Foundation (OPP1033751), GlaxoSmithKline (JC, SJB, MJB) and the United Kingdom Department for International Development (MPR, MJB) also contributed to this work. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
Authors’ Affiliations
References
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