Background: Vaccination was the single most effective measure in mitigating the impact of the COVID-19 pandemic. Our study aims to quantify the impact of vaccination programmes during the initial year of vaccination (2021) by estimating the number of case fatalities avoided, using Sweden as a case study.

Methods: Using Swedish data on age-specific reported incidence and vaccination uptake, along with vaccine efficacies, age-specific contact patterns and under-reporting from the literature, we fit a Bayesian SEIR epidemic model with time-varying community contact rate for COVID-19 incidence. Age-specific fatality rates from the literature are adjusted proportionally to fit the observed number of case fatalities in the factual analysis, resulting in 5,510 (95% PI: 5,370-5,650) matching the observed number 5,430. The estimated time-varying community contact rate is then used in a counterfactual analysis where the population is unvaccinated, leading to more infections and fatalities. A sensitivity analysis is performed to identify which parameters influence our conclusions.

Findings: The counterfactual analysis result in a severe epidemic outbreak during the early autumn of 2021, resulting in about 37,100 (36,700–37,500) number of case fatalities. Consequently, the number of lives saved by the vaccination programme is estimated to be about 31,600 (31,300–32,000), out of which 5,170 are directly saved and 26,400 are indirectly saved, mainly by drastically reducing the severe outbreak in the early autumn of 2021, which would have occurred without vaccination and unchanged community contact rate.

Interpretation: Our mathematical model is used to analyse the impact of COVID-19 vaccination on lives saved in Sweden during 2021, but the same methodology can be applied to other countries. The counterfactual analysis offers insights into an alternative trajectory of the pandemic without vaccination. The results show the direct impact of vaccination on reducing deaths for infected individuals and shed light on the indirect effects of reduced transmission dynamics.

Mathematical models of infectious diseases are important for informing public health decisions. Recent progress in computational techniques, along with increased access to surveillance data, has enabled more advanced modelling approaches. In particular, central to this thesis is the integration of Bayesian inference with mechanistic epidemic models to address real-world problems such as reporting delays, intervention evaluation, and parameter uncertainty. This thesis consists of four papers, each with the aim of strengthening the theoretical and practical aspects of epidemic modelling for real-time monitoring, retrospective evaluation, and future preparedness.

Paper I presents a Bayesian nowcasting model to estimate real-time COVID-19-related fatalities in Sweden, where observed death counts are delayed due to reporting lags. This paper introduces the use of additional data streams, here ICU admissions and reported cases, to enhance predictive accuracy. Retrospective evaluation over the second and third COVID-19 waves (October 2020–May 2021) shows that the proposed model improves accuracy compared to a baseline model that does not use additional data streams. All code and data are publicly available, and nowcasts were updated weekly during the pandemic on the webpage: https://staff.math.su.se/fanny.bergstrom/covid19-nowcasting/.

Paper II conducts a counterfactual analysis of Sweden’s national COVID-19 vaccination campaign in 2021 using an age-stratified susceptible-infectious-exposed-recovered (SEIR) model within a Bayesian framework. The model incorporates age-specific incidence data, vaccine uptake, and demographic contact patterns. It estimates that approximately 31,600 deaths related to COVID-19 were averted during 2021, of which 5,170 were due to direct protection and 26,400 from indirect effects (that is, reduced transmission). These findings underscore the importance of community-level vaccine-induced immunity. The study also includes sensitivity analyses to test robustness under various assumptions, such as reporting rates and pre-existing immunity.

Paper III enhances the World Health Organization’s Global Situational Alert System (GSAS), which supports real-time risk assessments at the country level. The paper introduces a hierarchical Bayesian model that accounts for transmission dynamics and reporting delays using case and death data. A three-part model includes components for case growth, delay-adjusted case-to-death mapping, and excess mortality-based calibration. The system assigns alert levels based on projected deaths per capita. Retrospective evaluation shows that modelling reporting delays improve forecast accuracy.

Paper IV focuses on a fundamental issue in epidemic modelling: the identifiability of parameters. Using a modified susceptible-infectious-recovered (SIR) model, the paper shows that under-reporting rates, pre-existing immunity levels, and transmission rates cannot be uniquely inferred from case data alone. An analytical proof of the unidentifiability is provided. Through a simulation study, the paper demonstrates that identifiability can be restored if data, such as sample survey data on population immunity or prevalence, are available for at least one parameter. This finding has implications for model design and data collection strategies, highlighting the need for identifiability analysis to ensure reliable inference.

The real-time analysis of infectious disease surveillance data is essential in obtaining situational awareness about the current dynamics of a major public health event such as the COVID-19 pandemic. This analysis of e.g., time-series of reported cases or fatalities is complicated by reporting delays that lead to under-reporting of the complete number of events for the most recent time points. This can lead to misconceptions by the interpreter, for instance the media or the public, as was the case with the time-series of reported fatalities during the COVID-19 pandemic in Sweden. Nowcasting methods provide real-time estimates of the complete number of events using the incomplete time-series of currently reported events and information about the reporting delays from the past. In this paper we propose a novel Bayesian nowcasting approach applied to COVID-19-related fatalities in Sweden. We incorporate additional information in the form of time-series of number of reported cases and ICU admissions as leading signals. We demonstrate with a retrospective evaluation that the inclusion of ICU admissions as a leading signal improved the nowcasting performance of case fatalities for COVID-19 in Sweden compared to existing methods.