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

by Morgan E. Smith, Emily Griswold, Brajendra K. Singh, Emmanuel Miri, Abel Eigege, Solomon Adelamo, John Umaru, Kenrick Nwodu, Yohanna Sambo, Jonathan Kadimbo, Jacob Danyobi, Frank O. Richards, Edwin Michael 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 oppose d 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 filar iasis (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. Usi ng available data from eight sites from Nigeria and elsewhere, we demonstrate that our data-model 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 de tails of historical interventions. Forecasts of the impacts of interventions carried out...
Source: PLoS Computational Biology - Category: Biology Authors: Source Type: research