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Comparison of Models Analyzing a Small Number of Observed Meningitis Cases in Navrongo, Ghana

Y Hagar1, M Hayden2, C Wiedinmyer2

  • 1Applied Mathematics, University of Colorado at Boulder, Boulder, Colorado, USA.

Journal of Agricultural, Biological, and Environmental Statistics
|January 5, 2024
PubMed

Insights

This study investigates seasonal meningitis trends in Africa's "meningitis belt," focusing on rare event modeling. It compares statistical models to better understand disease patterns and transmission dynamics.

Area of Science:

  • Epidemiology
  • Public Health
  • Mathematical Modeling

Background:

  • The "meningitis belt" in sub-Saharan Africa experiences cyclical annual meningitis outbreaks.
  • Seasonal trends significantly influence meningitis transmission, but pathways remain unclear.
  • Previous studies often relied on large sample inference, which may be unreliable for rare disease events.

Purpose of the Study:

  • To quantify seasonal patterns in meningitis rates within the context of rare events.
  • To compare the performance of different statistical models for analyzing meningitis trends.
  • To investigate disease dynamics in the meningitis belt using a case study from Navrongo, Ghana.

Main Methods:

  • Comparison of three modeling approaches: Poisson generalized linear model (GLM), Poisson generalized additive model (GAM), and an extended Bayesian hazard model.
  • Evaluation of model accuracy and robustness using bias, Root Mean Square Error (RMSE), and standard deviation of estimators.
  • Application of models to meningitis data from Navrongo, Ghana, for a detailed case study.

Main Results:

  • The study quantifies seasonal patterns in meningitis incidence.
  • Model comparison provides insights into the most accurate and robust methods for analyzing rare disease events.
  • The Bayesian hazard model demonstrated effectiveness in accommodating count data and changing at-risk populations.

Conclusions:

  • Accurate modeling of seasonal patterns is crucial for understanding meningitis outbreaks in the meningitis belt.
  • The chosen statistical models offer valuable tools for epidemiological research on rare disease events.
  • Findings contribute to a better understanding of meningitis transmission dynamics and inform public health strategies.
Keywords:
Changing at-risk populationCount dataHazard rateMulti-resolution hazardSurvival analysisTime-varying covariates

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