Models with time-varying predictors for meningitis in Navrongo, Ghana

TL;DR

This study analyzes meningitis outbreaks in Navrongo, Ghana, using survival analysis and compares three statistical models to understand seasonal patterns and improve prediction accuracy.

Contribution

It introduces a comparative analysis of Poisson GLM, Bayesian hazard, and Poisson GAM models for meningitis trend prediction in a specific African region.

Findings

Bayesian hazard model showed the lowest bias and RMSE.

Poisson GAM captured non-linear seasonal effects effectively.

Model comparison highlights the strengths and limitations of each approach.

Abstract

The "meningitis belt" is a region in sub-Saharan Africa where annual outbreaks of meningitis occur, with large epidemics observed cyclically. While we know that meningitis is heavily dependent on seasonal trends (in particular, weather), the exact pathways for contracting the disease are not fully understood and warrant further investigation. This manuscript examines meningitis trends in the context of survival analysis, quantifying underlying seasonal patterns in meningitis rates through the hazard rate for the population of Navrongo, Ghana. We compare three candidate models: the commonly used Poisson generalized linear model, the Bayesian multi-resolution hazard model, and the Poisson generalized additive model. We compare the accuracy and robustness of the models through the bias, RMSE, and the standard deviation. We provide a detailed case study of meningitis patterns for data collected in Navrongo, Ghana.