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iNZight Analytics' Andrew Sporle is a co-principal investigator on the Te Niwha project Improving models for pandemic preparedness and response: modelling differences in infectious disease dynamics and impact by ethnicity, alongside Dr. Samik Datta and Prof. Michael Plank. iNZight team members Nicole Satherley, Tori Diamond, and Ruby Pankhurst are also key personnel on the project.
Funded by: Te Niwha Infectious Disease Research Platform
Hosted by: University of Canterbury
From the grant: The Covid-19 pandemic demonstrated the value of mathematical models for informing policy decisions and the public health response to infectious disease threats. However, a major flaw in many models is that they either overlook or poorly characterise differences in disease burden between population subgroups. In New Zealand, Māori and Pacific populations have disproportionately worse health outcomes from infectious diseases and pandemics, but current cutting-edge models cannot account for the disparity in infectious disease vulnerability in these populations.
Our project will create new modelling methods that account for the diversity of vulnerability within and between populations. We have two research aims:
Aim 1. Develop new mathematical models that can capture differential dynamics of disease transmission within and between population subgroups, such as ethnicity groups or deprivation index. This will enhance understanding of epidemic dynamics by using stratified models to simulate the behaviour of future epidemic events.
Aim 2. Apply and validate these models using recent case studies on differences between ethnicity groups in Aotearoa New Zealand. We will parameterise and validate our models using anonymised age- and ethnicity-specific data, as well as linked health, Census and administrative data from Stats NZ, the Ministry of Health and Te Whatu Ora.
iNZight Analytics is involved in Michael Plank's Marsden Fund research project Robust modelling of inter and intra-ethnic variability in infectious disease outcomes. This project develops infectious disease models that incorporate socioeconomic and population-group differences in New Zealand to better understand and reduce health inequities, informing more equitable responses to future pandemics.
Funded by: Royal Society of New Zealand Te Apārangi
Grant number: 24-UOC-020
From the grant: Mathematical models are an essential tool for understanding and responding to infectious diseases and pandemics. However, models often do a poor job of capturing heterogeneities in epidemic dynamics between different parts of the population, such as different ethnicities. This severely limits their usefulness in understanding why different groups are differentially impacted by infectious diseases, and how to respond. In this project, we will develop new mathematical theory and design novel models that capture time-varying differences in transmission rates between and within different subpopulations. We will validate these models by benchmarking against New Zealand epidemiological data for measles and Covid-19.
