Introducing a drift and diffusion framework for childhood growth research

Benjamin J.J. McCormick*, Fraser I. Lewis, Godfrey Guga, Paschal Mdoe, Esto Mduma, Cloupas Mahopo, Pascal Bessong, Stephanie A. Richard

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

Background: Growth trajectories are highly variable between children, making epidemiological analyses challenging both to the identification of malnutrition interventions at the population level and also risk assessment at individual level. We introduce stochastic differential equation (SDE) models into child growth research. SDEs describe flexible dynamic processes comprising: drift - gradual smooth changes - such as physiology or gut microbiome, and diffusion - sudden perturbations, such as illness or infection. Methods: We present a case study applying SDE models to child growth trajectory data from the Haydom, Tanzania and Venda, South Africa sites within the MAL-ED cohort. These data comprise n=460 children aged 0-24 months. A comparison with classical curve fitting (linear mixed models) is also presented. Results: The SDE models offered a wide range of new flexible shapes and parameterizations compared to classical additive models, with performance as good or better than standard approaches. The predictions from the SDE models suggest distinct longitudinal clusters that form distinct 'streams' hidden by the large between-child variability. Conclusions: Using SDE models to predict future growth trajectories revealed new insights in the observed data, where trajectories appear to cluster together in bands, which may have a future risk assessment application. SDEs offer an attractive approach for child growth modelling and potentially offer new insights.

Original languageEnglish
Pages (from-to)1-19
Number of pages19
JournalGates Open Research
Volume4
Issue number71
Early online date29 Jun 2020
DOIs
Publication statusPublished - 26 Nov 2020

Keywords

  • Child growth
  • Dynamic modelling
  • MAL-ED
  • Stochastic differential equations

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