Sequential Modelling of the Effects of Mass Drug Treatments on Anopheline-Mediated Lymphatic Filariasis Infection in Papua New Guinea

Abstract

BackgroundLymphatic filariasis (LF) has been targeted by the WHO for global eradication leading to the implementation of large scale intervention programs based on annual mass drug administrations (MDA) worldwide. Recent work has indicated that locality-specific bio-ecological complexities affecting parasite transmission may complicate the prediction of LF extinction endpoints, casting uncertainty on the achievement of this initiative. One source of difficulty is the limited quantity and quality of data used to parameterize models of parasite transmission, implying the important need to update initially-derived parameter values. Sequential analysis of longitudinal data following annual MDAs will also be important to gaining new understanding of the persistence dynamics of LF. Here, we apply a Bayesian statistical-dynamical modelling framework that enables assimilation of information in human infection data recorded from communities in Papua New Guinea that underwent annual MDAs, into our previously developed model of parasite transmission, in order to examine these questions in LF ecology and control.ResultsBiological parameters underlying transmission obtained by fitting the model to longitudinal data remained stable throughout the study period. This enabled us to reliably reconstruct the observed baseline data in each community. Endpoint estimates also showed little variation. However, the updating procedure showed a shift towards higher and less variable values for worm kill but not for any other drug-related parameters. An intriguing finding is that the stability in key biological parameters could be disrupted by a significant reduction in the vector biting rate prevailing in a locality.ConclusionsTemporal invariance of biological parameters in the face of intervention perturbations indicates a robust adaptation of LF transmission to local ecological conditions. The results imply that understanding the mechanisms that underlie locally adapted transmission dynamics will be integral to identifying points of system fragility, and thus countermeasures to reliably facilitate LF extinction both locally and globally.

Continental-Scale, Data-Driven Predictive Assessment of Eliminating the Vector-Borne Disease, Lymphatic Filariasis, in Sub-Saharan Africa by 2020

“…The technical details of the LF transmission models used and the Bayesian melding approach employed to calibrate these models to local data, as well as specifics of how LF MDA and VC interventions are simulated, have been described extensively previously [21,22,50,51] and are outlined in Additional file 1. Here, our focus is on the coupling of this data-driven modeling framework to input data assembled at local settings (here at the village level) as a means for better capturing the effects of local spatial heterogeneity in LF transmission dynamics when making predictions of the impacts of applied interventions at higher spatial scales.…”

“…For those countries which had not started MDA as of 2015, MDA and VC were assumed to have begun in 2016. We considered a country to have achieved LF elimination if the mf prevalence from each model parameter vector selected (as per step 4(b)) for modeling from that country crossed its own site-specific 95% elimination probability threshold [22,50,52].…”

“…3d) in order to make inferences regarding the expected distribution of mf elimination thresholds within a country. Figure 4 portrays the values of mf breakpoints estimated for a selection of study countries using these best-fitting site-specific parameter vectors following the numerical method outlined in our previous work [21,22,50,53]. Two features of the results depicted in Fig.…”

“…We instead used model fits to mf prevalence in a site to estimate ABR input values (see Methods) considerable variability in LF mf breakpoint values may occur within each country, and second, that both the distribution and mean values of these breakpoints will vary markedly between countries. Table 3 provides the mean and the 5th and 95th percentile values of the mf breakpoints obtained for each study country at both the threshold biting rate (TBR) (important as prevalence targets for assessing the impact of supplementary VC in breaking LF transmission [22,50]) and at the prevailing ABR values in a site (important for serving as mf threshold targets for evaluating the impact of MDA [22]). The data support the impression gained from Fig.…”

The Allee Effect and Elimination of Neglected Tropical Diseases: A Mathematical Modelling Study

“…We refer here to the component of the function R eff made up of the density-dependent functions of parasite intensity as R(W): Each of the parameters is defined and its value is presented in Appendix Table 1. These parameter values were obtained by fitting the LF model to human Mf prevalence data from several communities in PNG (Gambhir et al, 2010b;Singh et al, 2013). In the main text, in Eqn (1) and subsequently, the fully expressed model above has been simplified so that the parameter l represents the set of parameters above ABR.j 1 .j 2 s 2 .h(a), and b represents the set of parameters h.k.d.…”