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

ABSTRACT:

Background

Mathematical models are increasingly being used to evaluate strategies aiming to achieve the control or elimination of parasitic diseases. Recently, owing to growing realization that process-oriented models are useful for ecological forecasts only if the biological processes are well defined, attention has focused on data assimilation as a means to improve the predictive performance of these models.

Methodology and principal findings

We report on the development of an analytical framework to quantify the relative values of various longitudinal infection surveillance data collected in field sites undergoing mass drug administrations (MDAs) for calibrating three lymphatic filariasis (LF) models (EPIFIL, LYMFASIM, and TRANSFIL), and for improving their predictions of

SUBMITTER: Michael E 

PROVIDER: S-EPMC6175292 | biostudies-literature | 2018 Oct

REPOSITORIES: biostudies-literature