Assessing the impact of non-pharmaceutical interventions on SARS-CoV-2 transmission in Switzerland

  1. Joseph. C. Lemaitre
  2. Javier Perez-Saez
  3. Andrew S. Azman
  4. Andrea Rinaldo
  5. Jacques Fellay

Reviewed by

Read the full article

Listed in

Log in to save this article

Abstract

Following the rapid dissemination of COVID-19 cases in Switzerland, large-scale non-pharmaceutical interventions (NPIs) were implemented by the cantons and the federal government between 28 February and 20 March 2020. Estimates of the impact of these interventions on SARS-CoV-2 transmission are critical for decision making in this and future outbreaks. We here aim to assess the impact of these NPIs on disease transmission by estimating changes in the basic reproduction number (R0) at national and cantonal levels in relation to the timing of these NPIs. We estimated the time-varying R0 nationally and in eleven cantons by fitting a stochastic transmission model explicitly simulating within-hospital dynamics. We used individual-level data from more than 1000 hospitalised patients in Switzerland and public daily reports of hospitalisations and deaths. We estimated the national R0 to be 2.8 (95% confidence interval 2.1–3.8) at the beginning of the epidemic. Starting from around 7 March, we found a strong reduction in time-varying R0 with a 86% median decrease (95% quantile range [QR] 79–90%) to a value of 0.40 (95% QR 0.3–0.58) in the period of 29 March to 5 April. At the cantonal level, R0 decreased over the course of the epidemic between 53% and 92%. Reductions in time-varying R0 were synchronous with changes in mobility patterns as estimated through smartphone activity, which started before the official implementation of NPIs. We inferred that most of the reduction of transmission is attributable to behavioural changes as opposed to natural immunity, the latter accounting for only about 4% of the total reduction in effective transmission. As Switzerland considers relaxing some of the restrictions of social mixing, current estimates of time-varying R0 well below one are promising. However, as of 24 April 2020, at least 96% (95% QR 95.7–96.4%) of the Swiss population remains susceptible to SARS-CoV-2. These results warrant a cautious relaxation of social distance practices and close monitoring of changes in both the basic and effective reproduction numbers.

Article activity feed

  1. ScreenIT

    SciScore for 10.1101/2020.05.04.20090639: (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
    We contrast the estimated changes in R0 with changes in activity-related mobility data produced by Google (Google LLC 2020).
    Google
    suggested: (Google, RRID:SCR_017097)
    All data and code except for individual hospitalization data from Canton of Vaud have been deposited on Zenodo (doi).
    Zenodo
    suggested: (ZENODO, RRID:SCR_004129)

    Results from OddPub: Thank you for sharing your data.

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    We note several limitations to this work. First, due to the relatively recent introduction of SARS-CoV-2 in Switzerland compared to the length of hospital and ICU stays, the time distribution of in- and out-of hospital patients is biased towards shorter duration (SM Figure 3). In addition, due to the limited data available in some places, we were only able to fit our model for twelve of the twenty-six cantons. Modeling results presented in this work are subject to our hypothesis on yet uncertain parameters of COVID-19 including the infection fatality rate and the proportion of severe infections requiring hospitalization. Ongoing serological studies in Switzerland will provide key data to narrow these uncertainties. Moreover our estimates of time-varying basic reproduction numbers assume that the generation interval for COVID-19 in Switzerland remained unchanged, thus potentially ignoring the joint role of R0, the infectious period and contact rates in determining the disease’s intrinsic growth rate (Yan 2008). Assuming that the generation interval increased with the reduction of social contact, our estimates are conservative over-estimates of the “true” value of R0, which is encouraging from a public health perspective. Another limitation of our study is that it was not possible to disentangle the individual contribution of each NPI on R0 in this analysis due to the early onset of changes in R0 and in mobility patterns as well as the very close spacing between the different t...

    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. Version published to 10.4414/smw.2020.20295
  3. Version published to 10.1101/2020.05.04.20090639v1 on medRxiv