Road networks to explore COVID-19 infection

Abstract

COVID-19 pandemic triggered an unprecedented level of restrictive measures globally. Most countries resorted to lockdowns at some point to buy the much-needed time for flattening the curve and scaling up vaccination and treatment capacity. Although lockdowns, social distancing and business closures generally slowed down the case growth, there is a growing concern about the social, economic and psychological impact of these restrictions, especially on the disadvantaged and poorer part of society. While we are all in this together, these segments are often taking the heavier toll of the pandemic and facing harsher restrictions or getting blamed for community transmission. This study tries to explore this perspective using quantitative analysis and network theory. The research is set in the context of the latest delta and omicron outbreaks in the Greater Sydney area, Australia, during late 2021. We first try to model how the local road networks between the neighbouring suburbs (i.e., neighbourhood measure) and current infection count affect the case growth and how they differ between delta and omicron variants. We use a geographic information system, population and infection data to measure road connections, mobility and transmission probability across the suburbs. We then looked at three socio-demographic variables: age, education and income and explored how they moderate independent and dependent variables (infection rates and neighbourhood measures). The result shows strong model performance to predict infection rate based on neighbourhood road connection. However, apart from age in the delta variant context, the other two variables (income and education level) do not seem to moderate the relation between infection rate and neighbourhood measure. The results indicate that suburbs with a more socio-economically disadvantaged population do not necessarily contribute to more community transmission. The study findings could be potentially helpful for stakeholders in tailoring any health decision for future pandemics.