Differential Impacts of the COVID-19 Pandemic on Sociodemographic Groups in England: A Mathematical Model Framework

The Coronavirus disease-2019 (COVID-19) pandemic has had a significant impact on the world, redefining how we work, respond to public health emergencies and control efforts, and sparking increased research efforts. In this study, we have developed a deterministic, ordinary differential equation multi-risk structured model of the disease outcomes, with a focus on the total number of infections, reported cases, hospitalised individuals, and deaths in the population. The model takes into account sociodemographic risk-structure and age structured dynamics, as well as time-sensitive nonpharmaceutical interventions (lockdowns) to help observe the disease trajectory following the implementation of control measures. The primary focus of this study is to demonstrate the impact of different patterns of social mixing within and between deprivation deciles in England, to understand disparities in disease outcomes. Our analysis reveals that the diagonal kind of mixing, similar to "within-group homogenous" type of mixing assumption, results in a higher number of disease outcome compared to other types of mixing assumptions. We also explore the effectiveness of movement restriction (the first national lockdown) in controlling the spread of the virus in each social group, in order to understand how to target interventions in the future. Our analysis confirms significant disparities in infection outcomes between sociodemographic groups in England. Author summaryThe global impact of the coronavirus pandemic 2019 was evident, but different sociodemographic groups experienced disproportionate disease outcomes. In this paper, we present results from a mathematical model that simulates COVID-19 outcomes across diverse sociodemographic groups in England. Our work uses a mathematical framework that combines age and deprivation decile, to examine the disproportionate outcome in the number of infection, hospitalisation, and mortality based on social mixing patterns. Our work demonstrated the elevated risk for more deprived groups, where social and occupational factors increase contact rates, therefore intensifying disease spread. By distinguishing disease dynamics among deprivation deciles, this model offers insights for policymakers to design more equitable health strategies. This approach emphasis the need for policies that address the vulnerabilities of specific social groups to mitigate the effects of pandemics.