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Portrait of Andreas Wacker. Photo: Kennet Ruona

Andreas Wacker


Portrait of Andreas Wacker. Photo: Kennet Ruona

Estimating the SARS-CoV-2 infected population fraction and the infection-to-fatality ratio: A data-driven case study based on Swedish time series data


  • Andreas Wacker
  • Anna Jöud
  • Bo Bernhardsson
  • Philip Gerlee
  • Kristian Soltesz
  • Fredrik Gustafsson

Summary, in English

We demonstrate that finite impulse response (FIR) models can be applied to analyze the time evolution of an epidemic with its impact on deaths and healthcare strain. Using time series data for COVID-19-related cases, ICU admissions and deaths from Sweden, the FIR model gives a consistent epidemiological trajectory for a simple delta filter function. This results in a consistent scaling between the time series if appropriate time delays are applied and allows the reconstruction of cases for times before July 2020, when RT-PCR testing was not widely available. Combined with randomized RT-PCR study results, we utilize this approach to estimate the total number of infections in Sweden, and the corresponding infection-to-fatality ratio (IFR), infection-to-case ratio (ICR), and infection-to-ICU admission ratio (IIAR). Our values for IFR, ICR and IIAR are essentially constant over large parts of 2020 in contrast with claims of healthcare adaptation or mutated virus variants importantly affecting these ratios. We observe a diminished IFR in late summer 2020 as well as a strong decline during 2021, following the launch of a nation-wide vaccination program. The total number of infections during 2020 is estimated to 1.3 million, indicating that Sweden was far from herd immunity.


  • Mathematical Physics
  • NanoLund: Center for Nanoscience
  • Division of Occupational and Environmental Medicine, Lund University
  • EpiHealth: Epidemiology for Health
  • Department of Automatic Control
  • ELLIIT: the Linköping-Lund initiative on IT and mobile communication

Publishing year





Scientific Reports



Document type

Journal article


Nature Publishing Group


  • Control Engineering
  • Public Health, Global Health, Social Medicine and Epidemiology




  • COVID-19: Dynamical modelling for estimation and prediction


  • ISSN: 2045-2322