Socio-molecular analysis of the effectiveness of non-pharmaceutical interventions during the COVID-19 pandemic in Denmark

Background. During the COVID-19 pandemic, governments worldwide implemented non-pharmaceutical interventions (NPIs) such as school closures and stay-at-home orders to mitigate transmission. The effectiveness of NPIs became a topic of intense debate because individual country experiences regarding their effectiveness varied. Despite extensive research on the population-level effectiveness of NPIs, many studies lacked differentiation between various population subgroups and infection settings. Aims. My Postdoc.Mobility project aims to provide a more specific assessment of NPI effectiveness during the COVID-19 pandemic using fine-grained viral genomic sequence data and demographic information from Denmark. The primary aims are to investigate how NPIs influenced the spread of the virus within transmission networks, evaluate the impact of NPIs on transmission in various settings such as households, schools, and workplaces, and understand how sociodemographic factors such as age, gender or income moderated NPI effectiveness. Methods. In Denmark, viral genomic sequence data have been linked with demographic information from the centralised Danish registry. Sampling dates and genetic differences are currently used to infer transmission networks, estimating the probability that individual A infected individual B, which I aim to improve using additional sociodemographic information about possible contact, e.g. from geospatial distance between households. The inferred transmission networks will be used to study NPI effectiveness specifically and with respect to transmission setting and sociodemographic characteristics. First, I will analyse changes in the structure of transmission networks to estimate the specific effects of NPIs in Denmark, for example, the effect of gathering bans on reducing superspreading events by examining the number of secondary infections per individual over time. Second, I will estimate the frequency of transmission in different settings such as households, schools, and workplaces, linking changes in transmission rates to the implementation dates of NPIs, and evaluating their effectiveness using counterfactual scenarios. Third, I will use methods from network analysis to assess transmission intensity between different population subgroups, analyse the position of individuals in the transmission networks, and describe the characteristics of superspreaders and intermediaries in spreading the virus across different demographic groups. Impact. This project aims to provide a comprehensive understanding of how NPIs influenced the spread of SARS-CoV-2 at both the population and individual level. Leveraging Denmark's unique dataset of viral genomic sequences linked to detailed demographic information will provide valuable insights into the effectiveness of NPIs and its variation between population subgroups. These findings can inform more targeted public health interventions in future pandemics, helping to protect vulnerable populations and optimise the allocation of resources. The results will also contribute to the broader field of infectious disease epidemiology by demonstrating the potential of socio-molecular network analysis in understanding disease transmission dynamics, ultimately enhancing pandemic preparedness and response strategies.