Introducing a drift and diffusion framework for childhood growth research [version 2; peer review: 2 approved]
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...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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F1000 Research Ltd
2020-11-01
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| Series: | Gates Open Research |
| Online Access: | https://gatesopenresearch.org/articles/4-71/v2 |
| _version_ | 1818666180336943104 |
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| author | Fraser I Lewis Godfrey Guga Paschal Mdoe Esto Mduma Cloupas Mahopo Pascal Bessong Stephanie A Richard Benjamin J J McCormick |
| author_facet | Fraser I Lewis Godfrey Guga Paschal Mdoe Esto Mduma Cloupas Mahopo Pascal Bessong Stephanie A Richard Benjamin J J McCormick |
| author_sort | Fraser I Lewis |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-17T06:00:26Z |
| format | Article |
| id | doaj.art-ab036e29f3574cc9b5a5c365fcafcfb2 |
| institution | Directory Open Access Journal |
| issn | 2572-4754 |
| language | English |
| last_indexed | 2024-12-17T06:00:26Z |
| publishDate | 2020-11-01 |
| publisher | F1000 Research Ltd |
| record_format | Article |
| series | Gates Open Research |
| spelling | doaj.art-ab036e29f3574cc9b5a5c365fcafcfb22022-12-21T22:00:54ZengF1000 Research LtdGates Open Research2572-47542020-11-01410.12688/gatesopenres.13123.214421Introducing a drift and diffusion framework for childhood growth research [version 2; peer review: 2 approved]Fraser I Lewis0Godfrey Guga1Paschal Mdoe2Esto Mduma3Cloupas Mahopo4Pascal Bessong5Stephanie A Richard6Benjamin J J McCormick7Independent Researcher, Utrecht, The NetherlandsHaydom Lutheran Hospital, Haydom, TanzaniaHaydom Lutheran Hospital, Haydom, TanzaniaHaydom Lutheran Hospital, Haydom, TanzaniaUniversity of Venda, Thohoyandou, 0950, South AfricaUniversity of Venda, Thohoyandou, 0950, South AfricaFogarty International Center, Bethesda, MD, USAFogarty International Center, Bethesda, MD, USABackground: 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.https://gatesopenresearch.org/articles/4-71/v2 |
| spellingShingle | Fraser I Lewis Godfrey Guga Paschal Mdoe Esto Mduma Cloupas Mahopo Pascal Bessong Stephanie A Richard Benjamin J J McCormick Introducing a drift and diffusion framework for childhood growth research [version 2; peer review: 2 approved] Gates Open Research |
| title | Introducing a drift and diffusion framework for childhood growth research [version 2; peer review: 2 approved] |
| title_full | Introducing a drift and diffusion framework for childhood growth research [version 2; peer review: 2 approved] |
| title_fullStr | Introducing a drift and diffusion framework for childhood growth research [version 2; peer review: 2 approved] |
| title_full_unstemmed | Introducing a drift and diffusion framework for childhood growth research [version 2; peer review: 2 approved] |
| title_short | Introducing a drift and diffusion framework for childhood growth research [version 2; peer review: 2 approved] |
| title_sort | introducing a drift and diffusion framework for childhood growth research version 2 peer review 2 approved |
| url | https://gatesopenresearch.org/articles/4-71/v2 |
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