A narrative review of count data models
- 1School of Public Health, College of Medicine and Health Sciences, Hawassa University, Hawassa, Ethiopia
Abstract
Introduction: Count data represent the frequency of event occurrences; therefore, they are inherently discrete and non-negative. Modelling such data presents considerable challenges when excessive zeros exist or when variability exceeds expected levels. Standard linear regression approaches are inappropriate for these data types. This narrative review examined principal models for count data analysis, beginning with foundational approaches and progressing to advanced methods that address overdispersion, zero inflation, and unobserved heterogeneity. Methods: A systematic literature search was conducted across PubMed, Scopus, Web of Science, and Google Scholar for articles published between 2000 and 2026. The search strategy employed keywords including “count data,” “Poisson regression,” “Negative Binomial,” “zero-inflated models,” “hurdle models,” “overdispersion,” and “zero inflation.” I included studies that addressed methodological developments, comparative evaluations, or applications of count data models. Data extraction focused on model specifications, estimation procedures, diagnostic approaches, and practical recommendations. Results: The review identified that Poisson regression serves as the foundational approach; however, the equidispersion assumption often fails in practice. The Negative Binomial model addresses overdispersion through an additional dispersion parameter. Generalized Poisson models provide flexibility for both over- and underdispersion. Zero-Inflated Poisson and Zero-Inflated Negative Binomial models combine a point mass at zero with a count process. Hurdle models treat zeros and positive counts as separate outcomes. Advanced methods include finite mixture models and panel count models with random effects. Estimation techniques such as maximum likelihood and the expectation maximization algorithm are widely employed. Diagnostic tools including residual analysis, overdispersion tests, and zero-inflation tests are essential for model evaluation. Conclusions: This review provides researchers with a clear guide to the theory, methods, and applications of count data models across various disciplines. Proper model specification, estimation, and diagnostic evaluation are essential for valid inference.
Related
- Transparency, accountability, and scholarly integrity: A systematic narrative review of artificial intelligence governance frameworks in healthcare publication
- Triangular array progressions for population count models involving Markov-P\'{o}lya distributions and relatives
- THE OPTIMAL EIGENVALUE COUNT IN AUBRY'S SPECTRAL SPHERE THEOREM FOR DIMENSIONS AT LEAST FOUR
- Objective Bayes Factor Based Model Selection for Zero-Inflated Microbiome Count Data Under Jeffreys Prior
- Congruent quotient matrix, nodal domain, and nodal count
BibTeX
@article{debessa2026narrative,
title = {{A narrative review of count data models}},
author = {Kare Chawicha Debessa},
journal = {Journal of Interventional Epidemiology and Public Health},
year = {2026},
month = sep,
doi = {10.37432/jieph-d-26-00263},
url = {https://doi.org/10.37432/jieph-d-26-00263},
note = {CC BY},
}
RIS
TY - JOUR
TI - A narrative review of count data models
AU - Kare Chawicha Debessa
PY - 2026
DA - 2026/09/01
JO - Journal of Interventional Epidemiology and Public Health
DO - 10.37432/jieph-d-26-00263
UR - https://doi.org/10.37432/jieph-d-26-00263
AB - Introduction: Count data represent the frequency of event occurrences; therefore, they are inherently discrete and non-negative. Modelling such data presents considerable challenges when excessive zeros exist or when variability exceeds expected levels. Standard linear regression approaches are inappropriate for these data types. This narrative review examined principal models for count data analysis, beginning with foundational approaches and progressing to advanced methods that address overdispersion, zero inflation, and unobserved heterogeneity. Methods: A systematic literature search was conducted across PubMed, Scopus, Web of Science, and Google Scholar for articles published between 2000 and 2026. The search strategy employed keywords including “count data,” “Poisson regression,” “Negative Binomial,” “zero-inflated models,” “hurdle models,” “overdispersion,” and “zero inflation.” I included studies that addressed methodological developments, comparative evaluations, or applications of count data models. Data extraction focused on model specifications, estimation procedures, diagnostic approaches, and practical recommendations. Results: The review identified that Poisson regression serves as the foundational approach; however, the equidispersion assumption often fails in practice. The Negative Binomial model addresses overdispersion through an additional dispersion parameter. Generalized Poisson models provide flexibility for both over- and underdispersion. Zero-Inflated Poisson and Zero-Inflated Negative Binomial models combine a point mass at zero with a count process. Hurdle models treat zeros and positive counts as separate outcomes. Advanced methods include finite mixture models and panel count models with random effects. Estimation techniques such as maximum likelihood and the expectation maximization algorithm are widely employed. Diagnostic tools including residual analysis, overdispersion tests, and zero-inflation tests are essential for model evaluation. Conclusions: This review provides researchers with a clear guide to the theory, methods, and applications of count data models across various disciplines. Proper model specification, estimation, and diagnostic evaluation are essential for valid inference.
SN - 2664-2824
ER -