Frontiers in Tropical Diseases (Nov 2023)
A hierarchical model-based framework for evaluating probabilities of area-wide freedom from lymphatic filariasis infection based on sentinel site surveillance data
Abstract
The design of population surveys to substantiate the elimination of disease transmission across large implementation units (IUs) has become important as many parasite control efforts approach their final stages. This is especially true for the global program to eliminate lymphatic filariasis (LF), which has successfully reduced infection prevalence in many endemic countries, such that the focus has shifted to how best to determine that the area-wide elimination of this macroparasitic disease has been achieved. The WHO has recommended a two-stage lot quality assurance sampling (LQAS) framework based on sampling children from selected clusters within an IU, called the Transmission Assessment Survey (TAS), for supporting such decision-making, but questions have emerged regarding the reliability of this strategy for assessing if LF transmission is broken effectively everywhere within an area. In this study, we develop and describe an alternative probabilistic framework that combines infection status information from longitudinal parasitological surveys of whole communities carried out in sentinel sites, imperfect diagnostic tests, and locally-applicable extinction thresholds predicted by transmission models, to overcome the problems associated with TAS. We applied the framework to LF infection and intervention data from the country of Malawi, and demonstrated how our hierarchical coupled model-sentinel site survey tool can be used to estimate the probability that LF transmission has occurred at the individual survey, village, and countrywide scales. We also further demonstrated how the framework can be used in conjunction with zonal or areal design prevalences to estimate the number of sentinel sites and durations of interventions required to acquire sufficiently high confidence that an area is free from infection. Our results indicate that the application of the spatially driven model-data freedom-from-infection tool developed here to follow up data from high-risk sentinel sites in a region may offer a highly cost-effective framework for guiding the making of high-fiducial and defensible area-wide LF intervention stopping decisions.
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