Intervention strategies against COVID-19 and their estimated impact on Swedish healthcare capacity

  1. Jasmine M Gardner
  2. Lander Willem
  3. Wouter Van Der Wijngaart
  4. Shina Caroline Lynn Kamerlin
  5. Nele Brusselaers
  6. Peter Kasson

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Abstract

Objectives

During March 2020, the COVID-19 pandemic has rapidly spread globally, and non-pharmaceutical interventions are being used to reduce both the load on the healthcare system as well as overall mortality.

Design

Individual-based transmission modelling using Swedish demographic and Geographical Information System data and conservative COVID-19 epidemiological parameters.

Setting

Sweden

Participants

A model to simulate all 10.09 million Swedish residents.

Interventions

5 different non-pharmaceutical public-health interventions including the mitigation strategy of the Swedish government as of 10 April; isolation of the entire household of confirmed cases; closure of schools and non-essential businesses with or without strict social distancing; and strict social distancing with closure of schools and non-essential businesses.

Main outcome measures

Estimated acute care and intensive care hospitalisations, COVID-19 attributable deaths, and infections among healthcare workers from 10 April until 29 June.

Findings

Our model for Sweden shows that, under conservative epidemiological parameter estimates, the current Swedish public-health strategy will result in a peak intensive-care load in May that exceeds pre-pandemic capacity by over 40-fold, with a median mortality of 96,000 (95% CI 52,000 to 183,000). The most stringent public-health measures examined are predicted to reduce mortality by approximately three-fold. Intensive-care load at the peak could be reduced by over two-fold with a shorter period at peak pandemic capacity.

Conclusions

Our results predict that, under conservative epidemiological parameter estimates, current measures in Sweden will result in at least 40-fold over-subscription of pre-pandemic Swedish intensive care capacity, with 15.8 percent of Swedish healthcare workers unable to work at the pandemic peak. Modifications to ICU admission criteria from international norms would further increase mortality.

What is already known?

  • -

    The COVID-19 pandemic has spread rapidly in Europe and globally since March 2020.

  • -

    Mitigation and suppression methods have been suggested to slow down or halt the spread of the COVID-19 pandemic. Most European countries have enacted strict suppression measures including lockdown, school closures, enforced social distancing; while Sweden has chosen a different strategy of milder mitigation as of today (10 April 2020).

  • -

    Different national policy decisions have been justified by socio-geographic differences among countries. Such differences as well as the tempo and stringency of public-health interventions are likely to affect the impact on each country’s mortality and healthcare system.

    • What this study adds?

  • -

    Individual-based modelling of COVID-19 spread using Swedish demographics and conservative epidemiological assumptions indicates that the peak of the number of hospitalised patients with COVID-19 can be expected in early May under the current strategy, shifted earlier and attenuated with more stringent public health measures.

  • -

    Healthcare needs are expected to substantially exceed pre-pandemic capacity even if the most aggressive interventions considered were implemented in the coming weeks. In particular the need for intensive care unit beds will be at least 40-fold greater than the pre-pandemic capacity if the current strategy is maintained, and at least 10-fold greater if strategies approximating the most stringent in Europe are introduced by 10 April.

  • -

    Our model predicts that, using median infection-fatality-rate estimates, at least 96,000 deaths would occur by 1 July without mitigation. Current policies reduce this number by approximately 15%, while even more aggressive social distancing measures, such as adding household isolation or mandated social distancing can reduce this number by more than 50%.

    • Article activity feed

    1. ScreenIT

      SciScore for 10.1101/2020.04.11.20062133: (What is this?)

      Please note, not all rigor criteria are appropriate for all manuscripts.

      Table 1: Rigor

      NIH rigor criteria are not applicable to paper type.

      Table 2: Resources

      Software and Algorithms
      SentencesResources
      Results files are available on Zenodo (DOI:10.5281/zenodo.3748120).
      Zenodo
      suggested: (ZENODO, RRID:SCR_004129)

      Results from OddPub: Thank you for sharing your code and data.

      Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
      Strengths and weaknesses of the study: The model predictions presented here utilize the best international understanding of the parameters of COVID-19 infectivity, clinical course, and transmissibility. Key parameters were further estimated from the Swedish data that we believe most robust to potential testing bias: ICU admissions and deaths. Based on this, the model makes striking predictions for the course of infection in Sweden over the coming weeks, based on Swedish government policy through 10 April 2020. In addition to estimating the results of public-health policy decisions on healthcare capacity and mortality, these predictions also provide a way to test current knowledge of the disease. Should either those reported data or current global understanding of COVID-19 biology include substantial errors, those will become evident as a divergence between model predictions and Sweden’s public health situation. In addition, residential care for the elderly is not accounted for explicitly in the models; this may lead to incorrect estimation of mortality in the older age groups. Meaning of the study: possible explanations and implications for clinicians and policymakers: Our results predict that Sweden’s healthcare capacity (both for COVID-19 patients and for others) will be rapidly overwhelmed under the current strategy, both through the need to care for COVID-19 patients and because healthcare workers will themselves become ill and unable to work.41-49 Intensive care capacity...

      Results from TrialIdentifier: No clinical trial numbers were referenced.

      Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

      Results from JetFighter: We did not find any issues relating to colormaps.

      Results from rtransparent:
      • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
      • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
      • No protocol registration statement was detected.

      About SciScore

      SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

    2. ScreenIT

      SciScore for 10.1101/2020.04.11.20062133: (What is this?)

      Please note, not all rigor criteria are appropriate for all manuscripts.

      Table 1: Rigor

      NIH rigor criteria are not applicable to paper type.

      Table 2: Resources

      Software and Algorithms
      SentencesResources
      Results files are available on Zenodo ( DOI:10.5281/zenodo.3748120) .
      Zenodo
      suggested: (ZENODO, SCR_004129)

      Results from OddPub: Thank you for sharing your code.

      About SciScore

      SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore is not a substitute for expert review. SciScore checks for the presence and correctness of RRIDs (research resource identifiers) in the manuscript, and detects sentences that appear to be missing RRIDs. SciScore also checks to make sure that rigor criteria are addressed by authors. It does this by detecting sentences that discuss criteria such as blinding or power analysis. SciScore does not guarantee that the rigor criteria that it detects are appropriate for the particular study. Instead it assists authors, editors, and reviewers by drawing attention to sections of the manuscript that contain or should contain various rigor criteria and key resources. For details on the results shown here, including references cited, please follow this link.

    3. Version published to 10.1101/2020.04.11.20062133v1 on medRxiv