Analyzing epidemic trends of SARS-CoV-2 in Switzerland

Nanina Anderegg, Julien Riou, Christian L. Althaus

Institute of Social and Preventive Medicine, Universität Bern, Switzerland

Quantifying the impact of quarantine duration on COVID-19 transmission

Peter Ashcroft, Sonja Lehtinen, Daniel Angst, Nicola Low and Sebastian Bonhoeffer

ETH Zurich & ISPM Universität Bern

Effectiveness of TTIQ

Peter Ashcroft, Sonja Lehtinen, and Sebastian Bonhoeffer

Institute of Integrative Biology, ETH Zurich, Switzerland


Re Estimation

Jérémie Scire, Jana S. Huisman et al.

ETH Zürich, D-BSSE & D-USYS

(opens in a new window)

nextstrain: Phylogenetic analysis of Swiss SARS-CoV-2 genomes in their international context

maintained by Emma Hodcroft, Richard Neher, Sarah Nadeau and Tanja Stadler.

(opens in a new window)

icumonitoring.ch

Cheng Zhao, Nicola Criscuolo, Burcu Tepekule, Monica Golumbeanu, Melissa Penny, Peter Ashcroft, Matthias Hilty, Thierry Fumeaux, Thomas Van Boeckel

ETH Zürich, Swiss TPH, Universitätsspital Zürich, Swiss Society for Intensive Care Medicine

(opens in a new window)

Analyzing epidemic trends of SARS-CoV-2 in Switzerland


Nanina Anderegg, Julien Riou, Christian L. Althaus (Institute of Social and Preventive Medicine, University of Bern)

This tool estimates national trends in daily confirmed cases, hospitalizations, ICU occupancy and deaths using a negative binomial generalized linear model. The model uses reported numbers as the response and date and weekend (0: work day, 1: weekend) as predictors. Confirmed cases and hospitalizations are further stratified by canton and age groups. Due to reporting delays, the last 3 and 5 days of confirmed cases and hospitalizations/deaths are removed, respectively. Lines and ribbons show the maximum likelihood estimate of the exponential increase/decrease and the 95% prediction intervals of the model fit, respectively.

Parameter
Cantonal trends
Loading...
Ranking
Loading...
Cantonal trends
Loading...
Ranking
Loading...

Data Tables

A collection of indicator values for Switzerland

Quantifying the impact of quarantine duration on COVID-19 transmission


Peter Ashcroft1, Sonja Lehtinen1, Daniel Angst1, Nicola Low2 and Sebastian Bonhoeffer1
1Institute of Integrative Biology, ETH Zurich, Switzerland, 2Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland

The large number of individuals placed into quarantine because of possible SARS-CoV-2 exposure has high societal and economic costs. There is ongoing debate about the appropriate duration of quarantine, particularly since the fraction of individuals who eventually test positive is perceived as being low. We use empirically-determined distributions of incubation period, infectivity, and generation time to quantify how the duration of quarantine affects onward transmission from traced contacts of confirmed SARS-CoV-2 cases and from returning travellers. We also consider the roles of testing followed by release if negative (test-and-release), reinforced hygiene, adherence, and symptoms in calculating quarantine efficacy. We show that there are quarantine strategies based on a test-and-release protocol that, from an epidemiological viewpoint, perform almost as well as a 10 day quarantine, but with fewer person days spent in quarantine. The findings apply to both travellers and contacts, but the specifics depend on the context.

Read the full preprint on medRxiv.

Empirical distributions
Loading...

Weibull distribution.
Ferretti et al., medRxiv 2020.09.04.20188516
Loading...

Shifted Student's t-distribution.
Ferretti et al., medRxiv 2020.09.04.20188516
Loading...

The distribution of incubation times follows a meta-distribution constructed from the average of seven reported log-normal distributions. Ferretti et al., medRxiv 2020.09.04.20188516
Test-and-release parameters
tE=0 time of exposure is fixed to zero *tests are subject to time-dependent false-negative results: Kucirka et al., Ann. Intern. Med. 2020 173:262-267

Additional Hygiene & Social Distancing Measures
Quantifying the impact of quarantine for traced contacts
Loading...
Loading...
Quarantine duration parameters
Quantifying the effect of duration and delay for the standard quarantine protocol (no test) for traced contacts.
Loading...
Loading...
Further considerations
Adherence: Changing the duration of quarantine could affect how many people adhere to the guidelines. Here we show the fold-change in adherence required to offset the change in quarantine efficacy if the duration is changed.
Symptoms: Individuals who develop symptoms should isolate independent of quarantine. Here we deduct these cases once they develop symptoms from the transmission prevented by quarantine.
Adherence and symptoms
Loading...
Loading...
Test-and-release parameters
tQ=0 travellers enter quarantine immediately upon arrival *tests are subject to time-dependent false-negative results: Kucirka et al., Ann. Intern. Med. 2020 173:262-267
Normalisationnormalise to local transmission or total transmission

Additional Hygiene & Social Distancing Measures
Quantifying the impact of quarantine for returning travellers
Loading...
Loading...
Quarantine duration parameters
Normalisationnormalise to local transmission or total transmission
Quantifying the effect of travel duration and quarantine duration for the standard quarantine protocol (no test) for returning travellers
Loading...
Loading...
Further considerations
Adherence: Changing the duration of quarantine could affect how many people adhere to the guidelines. Here we show the fold-change in adherence required to offset the change in quarantine efficacy if the duration is changed.
Symptoms: Individuals who develop symptoms should isolate independent of quarantine. Here we deduct these cases once they develop symptoms from the transmission prevented by quarantine.
Adherence and symptoms
Loading...
Loading...

Quantifying the impact of test-trace-isolate-quarantine (TTIQ) strategies on COVID-19 transmission


Peter Ashcroft1, Sonja Lehtinen1 and Sebastian Bonhoeffer1
1Institute of Integrative Biology, ETH Zurich, Switzerland

We present a mathematical model that leverages empirically determined distributions of incubation period, infectivity, and generation time to quantify how test-trace-isolate-quarantine (TTIQ) strategies can reduce the transmission of SARS-CoV-2. The TTIQ strategy is determined by five independent parameters:

  1. f : the fraction of infected individuals who test positive and are isolated from the community;
  2. Δ1 : the delay between symptom onset of an infected individual and the time they are isolated from the community;
  3. τ : the duration of time for which close contacts are traced prior to symptom onset;
  4. g : the fraction of close contacts per index case that are successfully traced and quarantined;
  5. Δ2 : the delay between isolating the index case and quarantining the secondary cases.

This is work in progress. This has not yet been peer-reviewed.

Empirical distributions
Loading...
Weibull distribution
Ferretti et al., medRxiv 2020.09.04.20188516
Loading...
Shifted Student's t distribution
Ferretti et al., medRxiv 2020.09.04.20188516
Loading...
Lognormal distribution
Li et al., NEJM 2020 382:1199-1207
TTIQ parameters

Focal TTIQ parameter set

Tertiary cases under TTIQ
Loading...
Loading...
Testing and isolation parameters
We set g=0 to eliminate contact tracing.
Tertiary cases under testing and isolation
Loading...
Loading...
Testing and isolation parameters
We set g=0 to eliminate contact tracing.
Secondary cases under testing and isolation
Loading...
Loading...

CH Covid-19 Dashboard

Covid-19 Dashboard for Switzerland. A collection of exploratory data bites.

Data Sources

Please see individual modules for specifics.

Code

Source Code for this site is available on GitHub License: GPL v3

Bug reports, issues or other reports and suggestions are always welcome on out GitHub Issue Tracker!

Contributors

in no particular order.

  • Timothy Vaughan, Computational Evolution, D-BSSE, ETH Zürich
  • Chaoran Chen, Computational Evolution, D-BSSE, ETH Zürich
  • Peter Ashcroft, Theoretical Biology, D-USYS, ETH Zürich
  • Sonja Lethinen, Theoretical Biology, D-USYS, ETH Zürich
  • Daniel Angst, Theoretical Biology, D-USYS, ETH Zürich
  • Sebastian Bonhoeffer, Theoretical Biology, D-USYS, ETH Zürich
  • Tanja Stadler, Computational Evolution, D-BSSE, ETH Zürich
  • Monica Golumbeanu, Swiss Tropical and Public Health Institute and University of Basel
  • Melissa Penny, Swiss Tropical and Public Health Institute and University of Basel
  • Nanina Anderegg, Institute of Social and Preventive Medicine, University of Bern
  • Christian L. Althaus, Institute of Social and Preventive Medicine, University of Bern
  • Julien Riou, Institute of Social and Preventive Medicine, University of Bern