Spatiotemporal modeling of first and second wave outbreak dynamics of COVID-19 in Germany

  1. Dorothee Lippold
  2. Andreas Kergaßner
  3. Christian Burkhardt
  4. Matthias Kergaßner
  5. Jonas Loos
  6. Sarah Nistler
  7. Paul Steinmann
  8. Dominik Budday
  9. Silvia Budday

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Abstract

The COVID-19 pandemic has kept the world in suspense for the past year. In most federal countries such as Germany, locally varying conditions demand for state- or county-level decisions to adapt to the disease dynamics. However, this requires a deep understanding of the mesoscale outbreak dynamics between microscale agent models and macroscale global models. Here, we use a reparameterized SIQRD network model that accounts for local political decisions to predict the spatiotemporal evolution of the pandemic in Germany at county resolution. Our optimized model reproduces state-wise cumulative infections and deaths as reported by the Robert Koch Institute and predicts the development for individual counties at convincing accuracy during both waves in spring and fall of 2020. We demonstrate the dominating effect of local infection seeds and identify effective measures to attenuate the rapid spread. Our model has great potential to support decision makers on a state and community politics level to individually strategize their best way forward during the months to come.

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  1. Version published to 10.1007/s10237-021-01520-x
  2. Version published to 10.1101/2020.06.10.20126771v2 on medRxiv
  3. ScreenIT

    SciScore for 10.1101/2020.06.10.20126771: (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
    All simulations were implemented and performed in Octave 5.2.0 using packages optim 1.6.0, statistics 1.4.1, io
    Octave
    suggested: (GNU Octave, RRID:SCR_014398)

    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:
    The presented model has certain limitations that we aim to address in the future. One drawback of all SIR-type modeling approaches is that they hardly account for the various courses of disease: in such rate-dependent models, some appear as infinitely long infectious. To prevent this issue from significantly affecting our optimized parameters, we only considered the latest dead count in our fitting procedure (see Methods). Still, we plan to adapt our model to integrate detailed information on specific courses of disease within a memory-based or delayed ODE, as introduced e.g. in [34]. Most noticeable deviations of our model predictions on a state level occurred in Bremen, a city-state with overall very low infection numbers. Despite a high dark figure and concomitant uncertainty, on the city level our quasi-continuum modeling approach and the underlying exponential growth seem to approach their validity limit, while stochastic effects start to become more important. Whereas SIR-type compartment models may capture the spread on a macro- and meso-scale level, at very low infection numbers or high spatial resolution, individual agent-based models [19, 35] are required to accurately predict the course of the epidemic. It is noteworthy, though, that current agent-based models may scale up to ≈ 50.000 agents, leaving quite a gap to meso-scale models like ours. We will investigate how coupling both types of methods in a multi-scale model can close this gap in the future. Similarly, ...

    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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  4. Version published to 10.1101/2020.06.10.20126771v1 on medRxiv