Development of the reproduction number from coronavirus SARS-CoV-2 case data in Germany and implications for political measures

  1. Sahamoddin Khailaie
  2. Tanmay Mitra
  3. Arnab Bandyopadhyay
  4. Marta Schips
  5. Pietro Mascheroni
  6. Patrizio Vanella
  7. Berit Lange
  8. Sebastian C. Binder
  9. Michael Meyer-Hermann

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Abstract

Background

SARS-CoV-2 has induced a worldwide pandemic and subsequent non-pharmaceutical interventions (NPIs) to control the spread of the virus. As in many countries, the SARS-CoV-2 pandemic in Germany has led to a consecutive roll-out of different NPIs. As these NPIs have (largely unknown) adverse effects, targeting them precisely and monitoring their effectiveness are essential. We developed a compartmental infection dynamics model with specific features of SARS-CoV-2 that allows daily estimation of a time-varying reproduction number and published this information openly since the beginning of April 2020. Here, we present the transmission dynamics in Germany over time to understand the effect of NPIs and allow adaptive forecasts of the epidemic progression.

Methods

We used a data-driven estimation of the evolution of the reproduction number for viral spreading in Germany as well as in all its federal states using our model. Using parameter estimates from literature and, alternatively, with parameters derived from a fit to the initial phase of COVID-19 spread in different regions of Italy, the model was optimized to fit data from the Robert Koch Institute.

Results

The time-varying reproduction number ( R t ) in Germany decreased to <1 in early April 2020, 2–3 weeks after the implementation of NPIs. Partial release of NPIs both nationally and on federal state level correlated with moderate increases in R t until August 2020. Implications of state-specific R t on other states and on national level are characterized. Retrospective evaluation of the model shows excellent agreement with the data and usage of inpatient facilities well within the healthcare limit. While short-term predictions may work for a few weeks, long-term projections are complicated by unpredictable structural changes.

Conclusions

The estimated fraction of immunized population by August 2020 warns of a renewed outbreak upon release of measures. A low detection rate prolongs the delay reaching a low case incidence number upon release, showing the importance of an effective testing-quarantine strategy. We show that real-time monitoring of transmission dynamics is important to evaluate the extent of the outbreak, short-term projections for the burden on the healthcare system, and their response to policy changes.

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  1. Version published to 10.1186/s12916-020-01884-4
  2. Version published to 10.1101/2020.04.04.20053637v2 on medRxiv
  3. ScreenIT

    SciScore for 10.1101/2020.04.04.20053637: (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

    Experimental Models: Organisms/Strains
    SentencesResources
    The cumulative case number is compared to the sum of infected individuals and all subsequent states in the model, i.e. with IH + IR + HU + HR + UR + UD + D + RZ.
    IH + IR + HU + HR + UR + UD + D + RZ
    suggested: None

    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:
    Opening the shops was done together with a recommendation of a limitation of the number of people per surface and of avoidance of direct contacts. Indeed, in the UK the reduction of inter-personal contacts was estimated to bring down the reproduction number below one [42]. Face masks were recommended in Germany and compulsory after one week of open shops. Thus, social distancing and the trained new culture of mutual care was interfering with the increased risk of viral transmission. In addition, seasonal effects might have helped to contain the virus further. It is plausible that a major seasonal effect is an increased frequency of being at fresh air and more frequently exchanged air in closed rooms during summer time. Aerosols may accumulate in closed rooms [43, 44] and there is evidence that they contribute to virus transmission [45, 46], suggesting that such seasonal effects could have a substantial effect on the overall epidemic evolution.

    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 10.1101/2020.04.04.20053637v1 on medRxiv