Supporting COVID-19 Policy-Making with a Predictive Epidemiological Multi-Model Warning System

  1. Martin Bicher
  2. Martin Zuba
  3. Lukas Rainer
  4. Florian Bachner
  5. Claire Rippinger
  6. Herwig Ostermann
  7. Nikolas Popper
  8. Stefan Thurner
  9. Peter Klimek

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Abstract

In response to the SARS-CoV-2 pandemic, the Austrian governmental crisis unit commissioned a forecast consortium with regularly projections of case numbers and demand for hospital beds. The goal was to assess how likely Austrian ICUs would become overburdened with COVID-19 patients in the upcoming weeks. We consolidated the output of three independent epidemiological models (ranging from agent-based micro simulation to parsimonious compartmental models) and published weekly short-term forecasts for the number of confirmed cases as well as estimates and upper bounds for the required hospital beds. Here, we report on three key contributions by which our forecasting and reporting system has helped shaping Austria’s policy to navigate the crisis, namely (i) when and where case numbers and bed occupancy are expected to peak during multiple waves, (ii) whether to ease or strengthen non-pharmaceutical intervention in response to changing incidences, and (iii) how to provide hospital managers guidance to plan health-care capacities. Complex mathematical epidemiological models play an important role in guiding governmental responses during pandemic crises, in particular when they are used as a monitoring system to detect epidemiological change points.

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  1. Version published to 10.1101/2020.10.18.20214767v3 on medRxiv
  2. ScreenIT

    SciScore for 10.1101/2020.10.18.20214767: (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

    No key resources detected.

    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    Our forecast-based decision support comes with limitations. First of all, the weekly prognosis is based on shared data from the Ministry of Health and the Ministry of Internal Affairs which comes with quality and reporting bias limitations. Moreover, even though the consortium has access to the most accurate and up-to-date data about the epidemic in Austria, a lot of information required for valid epidemiological forecasting is available only with considerable delay, or unavailable, since it is and cannot be measured. For instance, the fraction of undetected persons due to asymptomatic disease progression would be one example for such a variable. Further, our forecast is based on simulation models which are generally subject to errors that come from abstraction and simplification of the real system. Through the harmonized handling of three models with entirely different approaches we attempted to reduce such structural uncertainties. Finally, our decision support framework is mostly limited by its political and public visibility. According to our experience, our forecast was of special public and policy interest in periods of rapid movements but also had a confirmatory effect in times of decreasing case numbers or slow growth with respect to taken policy measures. In conclusion, we argue that worst-case coronavirus science is maybe interesting as an academic exercise, but modellers need to be more cautious and responsible in communicating the strongly speculative nature of th...

    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.

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  3. Version published to 10.1101/2020.10.18.20214767v1 on medRxiv
  4. Version published to 10.1101/2020.10.18.20214767v2 on medRxiv