Heterogeneous contact network patterns and infectious respiratory disease inequities in contagion models  — The Association Specialists
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Heterogeneous contact network patterns and infectious respiratory disease inequities in contagion models  (23732)

Zoe Kumbaroff 1 2 3 , Jesse Whitehead 1 2 , Dion O'Neale 1 4 , Emily Harvey 1 5
  1. Te Pūnaha Matatini, Auckland, New Zealand
  2. University of Waikato, Hamilton, WAIKATO, New Zealand
  3. New Zealand Institute of Public Health and Forensic Science, Porirua, New Zealand
  4. Nicholson Consulting, Wellington, New Zealand
  5. Auckland Council, Auckland, New Zealand

The Royal Commission on COVID-19 in Aotearoa found that different population groups experienced differential burden of COVID-19. However, differences such as these can remain hidden in mathematical epidemiological models that focus on infectious disease transmission at the total population level. This can underestimate the risk of infection and the burden of disease for vulnerable groups, potentially further widening inequities when results are used to inform policy.

We developed a model of infectious disease spread based on realistic contact networks for Aotearoa New Zealand, developed from the Integrated Data Infrastructure. The model consists of individuals who are linked to each other by dwellings, workplaces, schools, and social events where face-to-face interactions make it possible for infection transmission to occur. Our model accounts for known structural differences in contact patterns by factors including age and ethnicity. For example, Pacific Peoples are more likely to live in larger dwellings and in multigenerational households.

We find that dimensions like ethnicity are strongly predictive of heterogeneity in the structure of contact networks. Through simulating the spread of respiratory disease on these contact networks we show that including information about household composition is important for contagion models to produce the observed differences in rates of infection and burden of disease such as those observed through routine severe acute respiratory illness surveillance at Auckland hospitals.

Epidemic models of disease spread based on realistic contact networks are naturally able to capture some of the key differences in contact patterns that drive acute respiratory illness inequities. Including model features that represent drivers of inequities allows researchers to understand how these factors lead to differential disease outcomes. In doing so, models are better placed for identifying points for evidence-based interventions and strategies that reduce inequities and improve health for the most vulnerable populations.