Modeling transmission dynamics and human behavior during the SARS-CoV-2 epidemic in Switzerland

The coronavirus disease (COVID-19) pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has posed an unprecedented threat to public health, the economy, and society at large. In response to mitigate the impact of SARS-CoV-2, Switzerland, in line with many other countries, implemented non-pharmaceutical interventions (NPIs) such as face masks and bans on gatherings. Later, vaccine and natural infection induced immunity in the population protect from COVID-19. Nevertheless, the spread of SARS-CoV-2 enabled evolution and the emergence of variants of concern (VoCs). The complexity of transmission, human behavior, control measures, and their interaction led to many unanswered questions about the spread and consequences of COVID-19 in Switzerland. Understanding the dynamics of SARS-CoV-2 is critical for mitigating transmission and ultimately preventing severe cases and deaths. Modeling in infectious diseases can support collected data such as surveillance data and offer the possibility to study gaps in observations. The aim of this doctoral thesis was to gain insights into the SARS-CoV-2 epidemic in Switzerland, in particular, the local impact of imported cases, the impact of control measures on the spread of VoCs, changes in social contact patterns, and associations with vaccination uptake. To this end, I explored a variety of data such as (genomic) surveillance and survey data, and different methods including transmission and regression models. Overall, the cross-disciplinary approach employed in this thesis provided evidence that imported cases significantly impacted the local SARS-CoV-2 epidemic during a period of low incidence (in chapter 2). Implementing border closures following the announcement of VoCs would have had limited impact on delaying their spread (in chapter 3). During the SARS-CoV-2 pandemic the number of social contacts was substantially reduced compared to pre-pandemic times (in chapter 4). Sociodemographic factors as well as individual behaviors and attitudes played an important role in COVID-19 vaccination uptake (in chapter 5). Finally, modeling provides evidence that can in collaboration with authorities improve public health.