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

Abstract

Although there is increasing importance placed on the use of mathematical models for the effective design and management of long-term parasite elimination, it is becoming clear that transmission models are most useful when they reflect the processes pertaining to local infection dynamics as opposed to generalized dynamics. Such localized models must also be developed even when the data required for characterizing local transmission processes 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 both data and models concurrently in areas where we have insufficient observational data. Using available data from eight sites from Nigeria and elsewhere, we demonstrate that our datamodel discovery system is able to estimate local transmission models and missing pre-control infection information using generalized knowledge of filarial transmission dynamics, monitoring survey data, and details of historical interventions. Forecasts of the impacts of interventions carried out in each site made by the models estimated using the reconstructed baseline data matched temporal infection observations and provided useful information regarding when transmission interruption is likely to have occurred. Assessments of elimination and resurgence probabilities based on the models also suggest a protective effect of vector control against the reemergence of LF 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 support the effective management of disease programs in the face of critical data gaps.

Modelling the Impact of Vector Control on Lymphatic Filariasis Programs: Current Approaches and Limitations

“…We found a number of areas of agreement between modelling and field evidence for vector control. In particular, reports of vector control being successfully used to interrupt transmission in the field [ 12 , 13 ] are supported by model predictions that reducing the biting rate could greatly increase the probability of elimination and decrease the probability of resurgence [ 2 , 15 , 17 ]. Our results also demonstrate the utility of enhancing vector control coverage after MDA cessation and that poorly maintained vector control could undermine hard-won gains.…”

“…To review the recent modelling literature, we searched PubMed using the search term “vector control” and “lymphatic filariasis” and either “model,” “modelling” or “dynamics” on 22 October 2020, for articles published in the last 10 years (since 2010). The search returned 30 publications, from which we identified 10 that used mathematical models of lymphatic filariasis transmission to assess the impact of vector control on program outcomes [ 2 , 5 , 6 , 12 , 15–20 ]. Of these studies, 4 used reported vector control coverage data from specific settings and 6 considered a theoretical introduction of vector control at specified coverage levels, most commonly 50% or 80% population coverage.…”

“…There were 3 models that appeared multiple times across these studies [ 5–7 , 15–18 , 20 ]. Two of these were stochastic individual-based models that track the unique infection status of individuals within a population, and the third was a deterministic age-structured model that tracks the mean population burden of worms and infective mf stages.…”

“…One study showed that using 80% coverage vector control to increase the mf breakpoint could reduce the number of MDA rounds required to ensure a 95% probability of elimination by 6 to 15 rounds, with a median decrease of 9 rounds, and could reduce the variance from 3.52 to 2.66 in Anopheles settings [ 16 ]. A recent study using vector control and infection data from a range of settings demonstrated that maintaining vector control at current levels (25%–80%) after achieving 1% mf prevalence gave an elimination probability of 91%–100%, whereas the one setting investigated that had no vector control had a much lower elimination probability of 24% [ 15 ].…”

Review state-of-the-art of output-based methodological approaches for substantiating freedom from infection

“…When substantiating freedom from disease, the question is not to quantify the frequency of an event, but to estimate the likelihood of its absence, when there is no evidence of its presence. This is akin to problems addressed in other areas, such as demonstrating eradication of an invasive species ( 26–28 ) or confirming interruption in the transmission of human parasitic ( 29 , 30 ) or infectious diseases ( 31 , 32 ). In veterinary epidemiology, initial work on this question focused on determining an appropriate sample size to ensure some predefined level of confidence in disease freedom assuming a homogeneous population and a perfect biological test ( 33 ).…”