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Bo Bernhardsson

Modellering och styrning av osäkra system. Programdirektör för masterprogrammet i maskininlärning, system och styrning.

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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

Författare

  • 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.

Avdelning/ar

  • Matematisk fysik
  • NanoLund: Centre for Nanoscience
  • Avdelningen för arbets- och miljömedicin
  • EpiHealth: Epidemiology for Health
  • Institutionen för reglerteknik
  • ELLIIT: the Linköping-Lund initiative on IT and mobile communication

Publiceringsår

2021

Språk

Engelska

Sidor

1-11

Publikation/Tidskrift/Serie

Scientific Reports

Volym

11

Issue

23963

Dokumenttyp

Artikel i tidskrift

Förlag

Nature Publishing Group

Ämne

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

Status

Published

Projekt

  • COVID-19: Dynamical modelling for estimation and prediction

ISBN/ISSN/Övrigt

  • ISSN: 2045-2322