tetano
Editor, Senior Moderator
Epidemics
. 2026 Mar 16:55:100905.
doi: 10.1016/j.epidem.2026.100905. Online ahead of print.
Modelling the transmission and impact of Omicron variants of Covid-19 in different ethnicity groups in Aotearoa New Zealand
Samik Datta[SUP] 1 [/SUP], Vincent X Lomas[SUP] 2 [/SUP], Nicole Satherley[SUP] 3 [/SUP], Andrew Sporle[SUP] 3 [/SUP], Michael J Plank[SUP] 4 [/SUP]
Affiliations
Previous pandemics, including influenza pandemics and Covid-19, have disproportionately impacted Māori and Pacific populations in Aotearoa New Zealand. The reasons for this are multi-faceted, including differences in socioeconomic deprivation, housing conditions and household size, vaccination rates, access to healthcare, and prevalence of pre-existing health conditions. Many mathematical models that were used to inform the response to the Covid-19 pandemic did not explicitly include ethnicity or other socioeconomic variables. This limited their ability to predict, understand and mitigate inequitable impacts of the pandemic. Here, we extend a model that was developed during the Covid-19 pandemic to support the public health response by stratifying the population into four ethnicity groups: Māori, Pacific, Asian and European/other. We include three ethnicity-specific components in the model: vaccination rates, clinical severity parameters, and contact patterns. We compare model results to ethnicity-specific data on Covid-19 cases, hospital admissions and deaths between 1 January 2022 and 30 June 2023, under different model scenarios in which these ethnicity-specific components are present or absent. We find that differences in vaccination rates explain only part of the observed disparities in outcomes. While no model scenario is able to fully capture the heterogeneous temporal dynamics, our results suggest that differences between ethnicities in the per-infection risk of clinical severe disease is an important factor. Our work is an important step towards models that are better able to predict inequitable impacts of future pandemic and emerging disease threats, and investigate the ability of interventions to mitigate these.
Keywords: Compartment-based model; Contact matrix; Infectious disease dynamics; Mathematical epidemiology; SARS-CoV-2; Vaccination.
. 2026 Mar 16:55:100905.
doi: 10.1016/j.epidem.2026.100905. Online ahead of print.
Modelling the transmission and impact of Omicron variants of Covid-19 in different ethnicity groups in Aotearoa New Zealand
Samik Datta[SUP] 1 [/SUP], Vincent X Lomas[SUP] 2 [/SUP], Nicole Satherley[SUP] 3 [/SUP], Andrew Sporle[SUP] 3 [/SUP], Michael J Plank[SUP] 4 [/SUP]
Affiliations
- PMID: 41861564
- DOI: 10.1016/j.epidem.2026.100905
Previous pandemics, including influenza pandemics and Covid-19, have disproportionately impacted Māori and Pacific populations in Aotearoa New Zealand. The reasons for this are multi-faceted, including differences in socioeconomic deprivation, housing conditions and household size, vaccination rates, access to healthcare, and prevalence of pre-existing health conditions. Many mathematical models that were used to inform the response to the Covid-19 pandemic did not explicitly include ethnicity or other socioeconomic variables. This limited their ability to predict, understand and mitigate inequitable impacts of the pandemic. Here, we extend a model that was developed during the Covid-19 pandemic to support the public health response by stratifying the population into four ethnicity groups: Māori, Pacific, Asian and European/other. We include three ethnicity-specific components in the model: vaccination rates, clinical severity parameters, and contact patterns. We compare model results to ethnicity-specific data on Covid-19 cases, hospital admissions and deaths between 1 January 2022 and 30 June 2023, under different model scenarios in which these ethnicity-specific components are present or absent. We find that differences in vaccination rates explain only part of the observed disparities in outcomes. While no model scenario is able to fully capture the heterogeneous temporal dynamics, our results suggest that differences between ethnicities in the per-infection risk of clinical severe disease is an important factor. Our work is an important step towards models that are better able to predict inequitable impacts of future pandemic and emerging disease threats, and investigate the ability of interventions to mitigate these.
Keywords: Compartment-based model; Contact matrix; Infectious disease dynamics; Mathematical epidemiology; SARS-CoV-2; Vaccination.