Modelling the transmission and impact of Omicron variants of Covid-19 in different ethnicity groups in Aotearoa New Zealand
Epidemics·
- DOI
- 10.1016/j.epidem.2026.100905
- PMID
- 41861564
- PMCID
- —
- OpenAlex
- W7137328740
- Study type
- Journal article
- Publisher
- Elsevier BV
- Article type
- journal-article
- Integrity
- current
Why this research matters now
Enhanced stratified forecasting tools could assist public health authorities in anticipating and addressing unequal disease burdens during emerging infectious threats.
Structured evidence summary
Research question
This modeling study investigates whether incorporating ethnicity-specific vaccination coverage, disease severity, and social contact patterns improves predictions of coronavirus disease outcomes across distinct ethnic populations in New Zealand.
Study design
The authors developed a stratified mathematical model comparing simulated outbreak trajectories against observed case, hospitalization, and mortality records during early 2022 through mid-2023.
Population and setting
The analysis focuses on four demographic strata representing Māori, Pacific, Asian, and European or other communities within New Zealand.
Main findings
Adjusting for vaccination coverage accounted for a portion of the observed outcome gaps between groups, but no tested configuration completely replicated the fluctuating epidemic curves. The analysis indicates that varying susceptibility to severe illness following infection plays a significant role in driving ethnic disparities.
Public-health relevance
Enhanced stratified forecasting tools could assist public health authorities in anticipating and addressing unequal disease burdens during emerging infectious threats.
Important limitations
The tested configurations failed to completely replicate the complex time-varying patterns observed in the recorded data.
GIDS interpretation
This article provides a methodological framework for analyzing demographic stratification in infectious disease modeling, serving as a contextual reference for studies investigating structural determinants of health outcomes.
Related GIDS surveillance
Literature context does not validate, explain, or change a surveillance signal. Exact and contextual relationships are shown separately.
Evidence relationships
This article has 7 auditable classifier relationships to diseases, places, topics, and study design.