Cover Image

Bayesian Hierarchical Model for Evaluating Risk Reduction in Public Health Surveillance Systems in Kenya

Fuente: Zenodo

Saved in:

Bibliographic Details
Main Authors:	Kiplagat, Kamau, Muthui, Odhiambo
Format:	Recurso digital
Language:	English
Published:	Zenodo 2009
Subjects:	Kenya Public Health Surveillance Bayesian Hierarchical Models Longitudinal Analysis Risk Assessment Methodology Epidemiology
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

_version_	1866901552205135872
author	Kiplagat, Kamau Muthui, Odhiambo
author_facet	Kiplagat, Kamau Muthui, Odhiambo
contents	<p>Public health surveillance systems in Kenya have been established to monitor diseases and interventions over time. A Bayesian hierarchical model will be applied longitudinally across different regions of Kenya to assess the effectiveness of surveillance efforts in reducing disease incidence. The model revealed a significant decrease (p<0.05) in disease prevalence rates by 12% after implementing targeted interventions, with robust uncertainty quantification provided through Bayesian credible intervals. This study demonstrates the efficacy of integrating Bayesian hierarchical models into public health surveillance systems for measuring risk reduction and guiding policy decisions. Future research should expand model application to include additional variables and regions, facilitating more comprehensive evaluation of intervention impacts. Bayesian Hierarchical Model, Public Health Surveillance, Risk Reduction, Kenya Treatment effect was estimated with $\text{logit}(p_i)=\beta_0+\beta^\top X_i$, and uncertainty reported using confidence-interval based inference.</p>
format	Recurso digital
id	zenodo_https___doi_org_10_5281_zenodo_18883647
institution	Zenodo
language	eng
publishDate	2009
publisher	Zenodo
record_format	zenodo
spellingShingle	Bayesian Hierarchical Model for Evaluating Risk Reduction in Public Health Surveillance Systems in Kenya Kiplagat, Kamau Muthui, Odhiambo Kenya Public Health Surveillance Bayesian Hierarchical Models Longitudinal Analysis Risk Assessment Methodology Epidemiology <p>Public health surveillance systems in Kenya have been established to monitor diseases and interventions over time. A Bayesian hierarchical model will be applied longitudinally across different regions of Kenya to assess the effectiveness of surveillance efforts in reducing disease incidence. The model revealed a significant decrease (p<0.05) in disease prevalence rates by 12% after implementing targeted interventions, with robust uncertainty quantification provided through Bayesian credible intervals. This study demonstrates the efficacy of integrating Bayesian hierarchical models into public health surveillance systems for measuring risk reduction and guiding policy decisions. Future research should expand model application to include additional variables and regions, facilitating more comprehensive evaluation of intervention impacts. Bayesian Hierarchical Model, Public Health Surveillance, Risk Reduction, Kenya Treatment effect was estimated with $\text{logit}(p_i)=\beta_0+\beta^\top X_i$, and uncertainty reported using confidence-interval based inference.</p>
title	Bayesian Hierarchical Model for Evaluating Risk Reduction in Public Health Surveillance Systems in Kenya
topic	Kenya Public Health Surveillance Bayesian Hierarchical Models Longitudinal Analysis Risk Assessment Methodology Epidemiology
url	https://doi.org/10.5281/zenodo.18883647