Investigating the transmission risk of infectious disease outbreaks through the Aotearoa Co-incidence Network (ACN): a population-based study

Abstract

Background The emergence and re-emergence of infectious diseases presents a significant challenge to public health and broader society. This study utilises novel nationwide data to calculate the transmission risk and potential inequity of infectious disease outbreaks through use of network analysis.Methods Nationwide employment and education microdata (»4.7 million individuals in Aotearoa New Zealand) were used to develop the Aotearoa Co-incidence Network (ACN). The ACN considers connections generated when individuals are employed at the same workplaces or enrolled at the same schools. Through forms of network analysis, connections between geospatial areas can be established and provide proxy measures of infectious disease transmission risk. The ACN was also overlayed with nationwide population vulnerability data based on the number of older adults (>65 years) and individuals with long-term health conditions.Findings We identify areas that have both high potential transmission risk (i.e., highly connected) and high vulnerability to infectious diseases. Community detection identified geographic boundaries that can be relevant to the application of regional restrictions for limiting infectious disease transmission.Interpretation Integrating novel network science and geospatial analytics provides a simple way to study infectious disease transmission risk and population vulnerability to outbreaks. Our replicable method has utility for researchers globally with access to such data. It can help inform equitable preparation for, and responses to infectious disease outbreaks.

Modelling the dynamics of infection, waning of immunity and re-infection with the Omicron variant of SARS-CoV-2 in Aotearoa New Zealand

“…There are challenges in parameterising a compartment model of the type studied here to include multiple demographic groups and including this level of detail may be better suited to an agent-based modelling (e.g ( Thompson et al, 2022 ).) or network contagion modelling approach (see ( Turnbull et al, 2022 )).…”

Near-term forecasting of Covid-19 cases and hospitalisations in Aotearoa New Zealand

“…M particles), with each particle consisting of time series for I t , R t and Z t . At each time step, particle j was assigned a weight j using the likelihood of the observed value of Ĉt under the distribution in Eq (4). The population of M particles was then resampled by drawing, with replacement, from the full set of particles with weights j.…”

“…A range of epidemiological models have been used to provide situational awareness and policy advice to inform the New Zealand Government's pandemic response. These have primarily consisted of increasingly complex mechanistic models of transmission dynamics, including factors such as age structure, vaccination status [3], social contact networks [4], waning immunity [5], reinfection [6], dynamic behavioural change, new variants [2]. This level of detail requires making relatively strong assumptions on the mechanisms underlying observed dynamics and is hence most appropriate for scenario analysis, which does not aim to make accurate long-term predictions but rather to deliver insights into key mechanisms affecting epidemic dynamics and a systematic approach to exploring the likely consequences of alternative strategies or policy decisions.…”

Near-term forecasting of Covid-19 cases and hospitalisations in Aotearoa New Zealand

“…A range of epidemiological models have been used to provide situational awareness and policy advice to inform the New Zealand Government's pandemic response. These have primarily consisted of increasingly complex mechanistic models of transmission dynamics, including factors such as age structure, vaccination status [3], social contact networks [4],…”