Great article. But I guess the whole 'flatten the curve' cycle was shaped by politics. Most leaders would see the 'year or two' message as political suicide.
This post tries to compare the initial pandemic spring '20 wave with the winter '20/'21 wave - both peak levels and cumulative levels. While you talk about cumulative numbers of Covid deaths (area under the curve), no cumulative numbers are cited in the post.
However, due to the contentious issue of deaths "from" vs. "with" Covid, a comparison of the true mortality impact of those periods can only be effectively illustrated by considering total deaths due to all causes. Similarly, the overall impact on healthcare would more appropriately be illustrated by comparing the all-cause ICU occupancy, or all-cause hospital admissions. For example, NHS data for England suggests occupancy of critical care beds only exceeded normal winter levels in January-February '21. Otherwise, your arguments are open to criticism of failing to consider base levels of mortality and healthcare burden outside of the designation of Covid-related.
Finally, and perhaps most surprisingly, your post presents zero evidence that the implemented lockdowns and other non-pharmaceutical measures causatively affected the shape of the Covid-19 pandemic waves. In contrast, there appears to be a growing appreciation that such measures were markedly less impactful than previously assumed:
"A new international study on the course of the COVID-19 pandemic in six northern European countries has unexpectedly discovered that the pre-existing seasonal nature of coronaviruses may have played more of a role during the pandemic than any of the government public health intervention policies – including vaccinations, lockdowns, masks and travel restrictions. The scientific study was published in the peer-reviewed journal, Journal of Clinical Medicine."
1. You can see from the plot that the area under the 2020/21 winter curve is larger than the spring 2020 one, but I link to the UK data for COVID hospitalisations/deaths if you want to dig into the numbers.
2. There is plenty of evidence out there that excess mortality in 2020 and early 2021 was predominantly down to COVID deaths (e.g. see ONS and Health Foundationn reports – https://www.health.org.uk/publications/long-reads/what-has-happened-to-non-covid-mortality-during-the-pandemic). Most of the commentators still making misleading claims about ‘with’/‘from’ COVID in 2020 – and ‘casedemics ’ etc. – are unfortunately people who apparently hadn’t looked at the evidence available.
3. The study you cite makes the common mistake of trying to linearly correlate cases/deaths and control measures. This is a not a valid approach because control measures will change transmission, which affects the growth rate (i.e. slope) of the case curve – and hence subsequent case dynamics in a non-linear way. To be honest, I’ve no idea how it got through peer-review. A correct analysis should account for this (those that have find much clearer links between control and epidemic growth, e.g. Brauner et al, Science, 2020 and Sharma et al, Nature Comms 2021). Analysis of social mixing patterns (e.g. Davies et al, Science, 2021) also show that reductions in contacts matched reductions in transmission (as we’d expect from a directly transmitted infection like SARS-CoV-2).
Great article. But I guess the whole 'flatten the curve' cycle was shaped by politics. Most leaders would see the 'year or two' message as political suicide.
Will humans ever address the elephant in the room that is compulsive lying by all of our political leaders?
Adam, this is excellenet!
Thank you!
This post tries to compare the initial pandemic spring '20 wave with the winter '20/'21 wave - both peak levels and cumulative levels. While you talk about cumulative numbers of Covid deaths (area under the curve), no cumulative numbers are cited in the post.
However, due to the contentious issue of deaths "from" vs. "with" Covid, a comparison of the true mortality impact of those periods can only be effectively illustrated by considering total deaths due to all causes. Similarly, the overall impact on healthcare would more appropriately be illustrated by comparing the all-cause ICU occupancy, or all-cause hospital admissions. For example, NHS data for England suggests occupancy of critical care beds only exceeded normal winter levels in January-February '21. Otherwise, your arguments are open to criticism of failing to consider base levels of mortality and healthcare burden outside of the designation of Covid-related.
Finally, and perhaps most surprisingly, your post presents zero evidence that the implemented lockdowns and other non-pharmaceutical measures causatively affected the shape of the Covid-19 pandemic waves. In contrast, there appears to be a growing appreciation that such measures were markedly less impactful than previously assumed:
"A new international study on the course of the COVID-19 pandemic in six northern European countries has unexpectedly discovered that the pre-existing seasonal nature of coronaviruses may have played more of a role during the pandemic than any of the government public health intervention policies – including vaccinations, lockdowns, masks and travel restrictions. The scientific study was published in the peer-reviewed journal, Journal of Clinical Medicine."
https://www.mdpi.com/2077-0383/13/2/334
A few comments:
1. You can see from the plot that the area under the 2020/21 winter curve is larger than the spring 2020 one, but I link to the UK data for COVID hospitalisations/deaths if you want to dig into the numbers.
2. There is plenty of evidence out there that excess mortality in 2020 and early 2021 was predominantly down to COVID deaths (e.g. see ONS and Health Foundationn reports – https://www.health.org.uk/publications/long-reads/what-has-happened-to-non-covid-mortality-during-the-pandemic). Most of the commentators still making misleading claims about ‘with’/‘from’ COVID in 2020 – and ‘casedemics ’ etc. – are unfortunately people who apparently hadn’t looked at the evidence available.
3. The study you cite makes the common mistake of trying to linearly correlate cases/deaths and control measures. This is a not a valid approach because control measures will change transmission, which affects the growth rate (i.e. slope) of the case curve – and hence subsequent case dynamics in a non-linear way. To be honest, I’ve no idea how it got through peer-review. A correct analysis should account for this (those that have find much clearer links between control and epidemic growth, e.g. Brauner et al, Science, 2020 and Sharma et al, Nature Comms 2021). Analysis of social mixing patterns (e.g. Davies et al, Science, 2021) also show that reductions in contacts matched reductions in transmission (as we’d expect from a directly transmitted infection like SARS-CoV-2).