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Optimizing spatio-temporal correlation structures for modeling food security in Africa: a simulation-based

Adusei Bofa1, Temesgen Zewotir2

  • 1School of Mathematics, Statistics, and Computer Science, University of KwaZulu Natal, Oliver Tambo Building, Westville Campus, Durban, South Africa. 221119873@stu.ukzn.ac.za.

BMC Bioinformatics
|April 27, 2024
PubMed
Summary

The Spatio-Temporal Poisson Anova Model (SPAM) is the most reliable for analyzing food security and nutrition in Africa, showing minimal bias. Other models like SPLTM and STSM overestimate or underestimate, making SPAM the top choice for accurate modeling.

Keywords:
Bayesian poisson modelMarkov chain monte carlo(MCMC)Matrix plotMean absolute errorRoot mean square errorWatanabe akaike information criterion

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Area of Science:

  • Environmental Science
  • Agricultural Science
  • Public Health

Background:

  • Food security and nutrition in Africa are complex issues influenced by spatial and temporal factors.
  • Accurate modeling is crucial for understanding and addressing these challenges.
  • Existing spatio-temporal models vary in their performance and reliability.

Purpose of the Study:

  • To investigate the impact of spatio-temporal correlation on food security and nutrition models in Africa.
  • To compare the performance of four distinct spatio-temporal models: SPLTM, TMS, SPAM, and STSM.
  • To identify the most reliable model for analyzing food security and nutrition dynamics.

Main Methods:

  • Four spatio-temporal models were employed: Spatio-Temporal Poisson Linear Trend Model (SPLTM), Poisson Temporal Model (TMS), Spatio-Temporal Poisson Anova Model (SPAM), and Spatio-Temporal Poisson Separable Model (STSM).
  • Model goodness of fit was evaluated using the Watanabe Akaike Information Criterion (WAIC).
  • Bias was assessed using root mean square error and mean absolute error.

Main Results:

  • The Spatio-Temporal Poisson Anova Model (SPAM) demonstrated minimal bias and the best goodness of fit across diverse scenarios.
  • The Spatio-Temporal Poisson Linear Trend Model (SPLTM) consistently overestimated food security.
  • The Poisson Temporal Model (TMS) showed variable bias, while the Spatio-Temporal Poisson Separable Model (STSM) tended to underestimate food security.

Conclusions:

  • SPAM is the most reliable and recommended model for analyzing food security and nutrition dynamics in Africa due to its consistent accuracy.
  • The study underscores the significant impact of spatial and temporal correlations on model performance.
  • Researchers should carefully evaluate model biases and goodness of fit to align with data and research objectives for enhanced reliability.