I dusted off my COVID-19 model (that predicted the Florida July wave) & applied it to Sweden
After today's data update from the Swedish Public Health Agency (FHM) I confidently forecast Sweden will surpass the peak of 100 COVID deaths/day they had in April
Hard to believe?
1/n
Specifically: by 25 December we will see Sweden has recorded 100 deaths/day around 11 December
(due to reporting delays, it takes up to 2 weeks past a given date to have a complete count of deaths on this date:
FHM only gives the cumulative number of cases per 10-year age group since the beginning of the pandemic in their data file (arcgis.com/sharing/rest/c…)
This is not enough to forecast deaths
6/n
We need the age of cases 𝗼𝘃𝗲𝗿 𝘁𝗶𝗺𝗲.
The only way to get this is to make archives of the FHM data over time. Adam Altmejd maintains such an archive: github.com/adamaltmejd/co…
7/n
These archives give us cases by age group over time.
Note how despite cases rising well above their April peak for many weeks, it's only 𝘁𝗵𝗶𝘀 week that cases among ages 80+ reached the same level as April, see also this heatmap by @jocami_ca
8/n
We then multiply the known age-stratified Infection Fatality Ratio of COVID-19 with the number of cases by age group
This gives us the estimated number of deaths ~3 weeks from today, as the mean infection-to-death time is 22.9 days (see pg. 4 of static-content.springer.com/esm/art%3A10.1…)
9/n
It's important to not confuse IFR & CFR
CFR has decreased 𝘀𝗶𝗴𝗻𝗶𝗳𝗶𝗰𝗮𝗻𝘁𝗹𝘆 over time but it's an artifact of the improving case ascertainment rate
IFR has remained relatively constant over time—studies suggest just slight 10-20% decline:
My source for the age-stratified IFR is the geometric mid-point of 13 independent studies: github.com/mbevand/covid1…
11/n
With all that taken into account, we find that the forecast actually underestimates deaths
This is because not all cases are detected. We need to adjust FHM data to account for undetected cases. IOW we need to find the case ascertainment rate.
12/n
Through trial and error I found the case ascertainment rate was about 50% a month ago, but has been declining to about 35%, meaning Sweden now detects 1 in 3 cases.
35% isn't great but is supported by reports of heavily strained testing capacity:
Let's point out numerous obvious flaws in his analysis, shall we?
1/n
Firstly, Berenson chooses to ignore Asian countries. Why? Because geography is "the most important factor in how hard Covid hits a country"
In other words: "countries who did better than Sweden don't count, because, well, I sAy So"
Such well-reasoned logic. Much wow 🤣
2/n
Secondly, he claims geography is the most important factor, but IGNORES ALL the countries geographically close to Sweden: Finland, Norway, Denmark (none of them are charted)
I modeled excess deaths per capita by age group, for each US state, since the beginning of the pandemic.
I believe this is the first time an analysis of this type has been done BY AGE GROUP for each state. This removes the need to do "age-adjustment" to compare states.
1/n
Based on NYT's mask survey across United States counties, plot deaths/capita recorded during the survey & up to 30 days later, along with each county's mask wearing score
Result:
Linear regression (Y log-transformed) R²=0.144 in red:
There are hundreds of factors affecting the dependent variable (deaths). Ignoring ALL of these factors, looking at mask usage only, and still finding R²=0.144 is pretty cool/unexpected
Confounders abound!: people who wear masks often are likely doing more social-distancing. Etc.
Methodology behind the chart:
Two data sources:
- mask wearing survey github.com/nytimes/covid-…
- COVID deaths (and population) by US county as per JHU CSSE
His charts seem to claim that nothing works. Locking down doesn't work, masking doesn't work, vaccination doesn't work, your printer doesn't wo—wait scratch that one
1/n
One iota of critical thinking is all you need to expose numerous errors in his charts:
Error #1 — Case ascertainment rate bias:
A country may detect 1 in 2 cases, while another 1 in 4. We say the case ascertainment rate is respectively 50%, and 25%.
This variance in case ascertainment rate alone is enough to put half of @ianmSC's charts where they belong: in the trash🗑️
Real Science™ looks at covid deaths—not cases—to compare the severity of the pandemic across different regions. This avoids case ascertainment rate bias.
I compiled a list—as exhaustive as possible—of all peer-reviewed & published research articles that evaluate the effectiveness of nonpharmaceutical interventions, specifically lockdowns on COVID-19
➡️Papers finding NPIs effective outnumber, by 8 to 1, those finding the opposite
Criteria for inclusion in the list:
1-Be a RESEARCH ARTICLE (data, methods, results). Commentaries, opinion pieces, etc, do not qualify
2-Be PEER-REVIEWED & PUBLISHED among the 26,000 titles in Scopus
3-Be EXPLICIT. No secondhand interpretation of the data
Regarding criterion #3: the authors must explicitly state in the text whether their results suggest NPIs are effective or not
Their exact words have been peer-reviewed & published. Your interpretation of figures or data tables has not.
2. President of Burundi died on 8 June 2020. The cause of death was given officially as "cardiac arrest" by the Burundian government, but is suspected to be COVID: economist.com/middle-east-an…
His wife was flown to Kenya and hospitalized for COVID a week before his death.