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Quantifying the value of surveillance data for improving model predictions of lymphatic filariasis elimination

Fig 1

Schematic diagram showing the sequential fitting procedure for updating models and predictions by incorporating longitudinal data.

In all scenarios, the initial EPIFIL models were initialized with parameter priors and a chi-square fitting criterion was applied to select those models which represent the baseline mf prevalence data sufficiently well (α = 0.05). The accepted models were then used to simulate the impact of interventions on mf prevalence. The chi-square fitting criterion was sequentially applied to refine the selection of models according to the post-MDA mf prevalence data included in the fitting scenario. The fitted parameters from selection of acceptable models at each data point were used to predict timelines to achieve 1% mf prevalence. The scenarios noted in the blue boxes indicate the final relevant updating step before using the fitted parameters to predict timelines to achieve 1% mf in that data fitting scenario.

Fig 1

doi: https://doi.org/10.1371/journal.pntd.0006674.g001