Predicting lymphatic filariasis elimination in data-limited settings: a reconstructive computational framework for combining data generation and model discovery

AbstractAlthough there is increasing recognition of the importance of mathematical models in the effective design and management of long-term parasite elimination, it is also becoming clear that to be most useful parasite transmission models must accurately reflect the processes pertaining to local infection dynamics. These models must also be identified even when the data required for characterizing the local transmission process are limited or incomplete, as is often the case for neglected tropical diseases, including the disease system studied in this work, viz. lymphatic filariasis (LF). Here, we draw on progress made in the field of computational knowledge discovery to present a reconstructive simulation framework that addresses these challenges by facilitating the discovery of data and models concurrently in order to deliver reliable location-specific predictions pertaining to LF elimination in areas where we have insufficient observational data. Using available data from eight sites from Nigeria and elsewhere, we demonstrate that our data-model discovery system is able to identify local transmission models using a generalized knowledge of filarial transmission dynamics, monitoring survey data, and details of historical interventions, which in turn can also generate information regarding the pre-control endemic status of LF in each study site. Forecasts of the impacts of interventions made by the models discovered using the reconstructed baseline data not only matched temporal infection observations, but also provided critical information regarding when transmission interruption is likely to have occurred. Assessments of elimination and recrudescence probabilities based on these models also suggested a protective effect of vector control against the reemergence of transmission after stopping drug treatments. The reconstructive computational framework for model and data discovery developed here highlights how coupling models with available data can generate new knowledge about complex, data-limited systems, and ultimately support the effective management of disease programs in the face of critical data gaps.Author summaryAs modelling becomes commonly used in the design and evaluation of parasite elimination programs, the need for well-defined models and datasets describing the nature of transmission processes in endemic settings is becoming pronounced. For many neglected tropical diseases, including the disease investigated in this study, lymphatic filariasis (LF), however, data for model identification are typically sparse or incomplete. In this study, we present a new data-model computational discovery system that couples data-assimilation methods based on existing monitoring survey data with model-generated data about baseline conditions, in order to discover the local LF transmission models required for simulating the impacts of interventions for achieving parasite elimination in typical endemic locations. Using data from eight study sites in Nigeria and elsewhere, we show that our reconstructive computational knowledge discovery system is able to combine information contained within partially-available site-specific monitoring data with knowledge of parasite transmission dynamics embedded in process-based models to generate the missing data required for inducing reliable locally applicable LF models. We also show that the models so discovered are able to generate the intervention forecasts required for supporting management-relevant decisions in parasite elimination.