New forecast: deaths will peak in Florida around August 6 between 160-190 deaths/day.
CFR increased from 1.2 to 1.5% because the median age is higher
My previous forecasts have proven very accurate. See open-source modeling code & doc on GitHub: github.com/mbevand/florid…
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In addition to charting deaths by reported date (thick black solid curve), I also chart deaths by exact date of death ("deaths by day", thin black solid curve)
Many don't understand that "deaths by day" is always higher than "deaths by date reported" in periods of increase
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On average it takes Florida about 10 days to report 85% of deaths in the "deaths by day" dataset (see my other analysis: github.com/mbevand/florid…).
So my forecast charts only show "deaths by day" up to 10 days before the present day, so as to not be misleading.
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But because data up to 10 days ago is still incomplete (on average it misses 15% of deaths), I make an effort to adjust "deaths by day" to complete the data.
This is represented by the thin black long-dashed curve:
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My #PredictionWasCorrect — deaths in Florida peaked at 185 per day on Aug 4 as measured by the 7-day moving average
Let's point out numerous obvious flaws in his analysis, shall we?
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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 🤣
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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.
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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
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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.