Exploring Lockdown Efficacy and Stringency of Policy Responses

Exploring Lockdown Efficacy and Stringency of Policy Responses

Welcome! In this simulation you can explore two facets of lockdown strategies:

  1. Their effect on the R-Value, according to this article published in the Nature journal
  2. Their level of "stringency", as defined by Oxford scientists here

Your goal is to find a trade-off between controlling the rising infection numbers (ideally, getting R below 1) and avoiding the measures you find the most unpleasent. As you will see, it is not always easy to find such a trade-off. This is what decision-makers in a pandemic have to do on a regular basis. With one important difference: They can, unlike you, not choose the scenario. So if you really want to get a feel for how hard it can be to find a compromise, make sure to not only stick to the "best"-scenario! By clicking on the "Stringency"-tab, you can compare your level of response-stringency to that of different countries of the world.

There are two types of parameters which you can set individually. The first is the scenario. Since different measures have different effects depending on circumstances, their effect on the R-Value can vary. Detailed information can be found below, but roughly speaking, "worst" assumes, that the measures do not have much effects on R, "best" assumes, that the measures have large effects on R, and "normal" is in between. The second is a list of different COVID-response measures. You can choose the measures which you find appropriate, and see if those measures suffice to get the R-Value below 1.

IMPORTANT NOTE: this Simulation does not take into account of any indirect and unintended effects of the inquired measures. Those include indirect health effects (like a rise in the prevalence of mental health diseases or increased substance abuse), economic effects, (like an increase in unemployement and poverty) and social effects (like more domestic violence and income-selective disadvantages in education). These effects, even if invisible here, are still very real. When, just like in this simulation, only infection numbers are displayed and everything else is ignored, people's perceptions of the pandemic can easily become biased. Please keep in mind, that statistics is not only about the "how", but also about the "what", and, maybe even more importantly, about the "what not".

Set Parameters

Lockdown Efficacy

Data on Lockdown efficacy taken from(1): https://doi.org/10.1038/s41467-021-26013-4

Additional Information (Efficacy)

This simulation is subject to certain assumptions, simplifications and limitations:

  • In reality, different response measures are not independent of each other. For example, a night-time curfew may not affect the R value as much, when private gatherings are already prohibited. Here, however, we have, for the sake of simplicity, assumed that the measures do not interfere with each other.
  • Measure-efficacy highly depends on the circumstances under which they are applied. The reductions of the R values are taken from an observational study [2], which was conducted during the second wave in Europe, from August 2020 to January 2021. The measures taken in European countries may not have the same effects as in other world regions. Additionally, vaccination was not available in Europe until the very end of the study. The rise in immunity among the population may lower the effects of response measures, since transmission becomes less likely. In addition, new virus-variants (e.g., Delta and Omicron) occurred after the publication of this study, and therefore our app does not take into account of these new, possibly more dangerous, variants.

Further information about the "Scenario" Parameter:

In the previously mentioned Nature article [1], the estimated size of effects of measure is given together with a 95% credible interval. The scenario "worst" assumes the lower end of this CI, "normal" assumes the median, "best" assumes the upper end of the CI. So, for example, with the scenario "worst", you will need more strict measures to get R below 1 than with the scenario "normal".

Government Stringency Index

Data on Government Stringency Index taken on 2021-11-21 from: https://ourworldindata.org/grapher/covid-stringency-index
The Government Stringency Index was introduced here (2): https://doi.org/10.1038/s41562-021-01079-8

Additional Information (Stringency)

The countries displayed in the table may not have implemented the same measures as you did. They are solely selected by their average government stringency indexes (between 21.01.2020 and 21.11.2021). The appearance of certain countries just implies that they have implemented COVID-19 response strategies with - on average - a stringency-level similar to yours.





Endnotes

[1]: Sharma, M., Mindermann, S., Rogers-Smith, C. et al. Understanding the effectiveness of government interventions against the resurgence of COVID-19 in Europe. Nat Commun 12, 5820 (2021). https://doi.org/10.1038/s41467-021-26013-4

[2]: Thomas Hale, Noam Angrist, Rafael Goldszmidt, Beatriz Kira, Anna Petherick, Toby Phillips, Samuel Webster, Emily Cameron-Blake, Laura Hallas, Saptarshi Majumdar, and Helen Tatlow. (2021). “A global panel database of pandemic policies (Oxford COVID-19 Government Response Tracker).” Nature Human Behaviour. https://doi.org/10.1038/s41562-021-01079-8
Information on how the index is calculated can be found here: https://github.com/OxCGRT/covid-policy-tracker/blob/master/documentation/index_methodology.md