Youyang Gu Profile picture
14 Dec, 12 tweets, 4 min read
Many people are unaware that the COVID-19 vaccine has significantly more side effects than the flu vaccine. I hope to see more honest discussions regarding this.

Props to @Cat_Ho for her realistic, data-centric reporting of this issue. It's much needed.

sfchronicle.com/health/article…
Some notable numbers from the vaccine trial participants after the 2nd dose (age 16-55):

- 16% developed a fever vs 0% for the placebo
- 59%/52% had fatigue/headache vs 23/24% for placebo
- 45% took pain medication vs 13% for placebo

Those age 55+ have a slightly lower rate.
In comparison, roughly 1% of flu shot participants report a fever (16x lower), and ~20% report fatigue/headache (2x lower).

On top of that, COVID-19 vaccine participants have to go through this twice. Though the side effects are milder after dose #1.

1% of trial participants developed a high fever (102-104°F). If we vaccinate 200 million people, that means 2 million will develop a high fever.

This is just for the Pfizer vaccine. Preliminary data suggests Moderna's vaccine may have a higher rate of severe side effects.
That said, these side effects are perfectly normal for a vaccine.

The Shingrix vaccine for shingles has a significantly higher rate of side effects than the COVID-19 vaccine. It is recommended by the CDC for those age 50+, and millions get it yearly.

Many people are under the false impression that this vaccine is similar to a flu shot.

But when news inevitably spreads of the side effects (and having to endure them twice), vaccine acceptance may nose-dive.

I think it's better to be upfront and transparent about this.
I suspect one reason the side effects aren't being as widely talked about is that there is a fear that it will feed vaccine hesitancy.

But an overwhelming majority of people won't receive the vaccine for months. This issue *will* come up eventually.

The elephant is in the room.
Mask acceptance is a big issue because people felt like the public health messaging overpromised their effectiveness. Similar issue with lockdowns.

Are we making the same mistake with the vaccine? Downplaying the side effects may backfire and hurt public confidence later on.
I think it make more sense to highlight the side effects as much as possible *now*, when demand for the vaccine greatly outnumber supply.

Set realistic expectations & exhaust the debate as much as possible now, rather than wait until spring, when people actually have the option.
In my opinion, in a few months, you'd rather have people think "oh it's not as bad as they said it was" than "it's worse than they said - I feel deceived."

I imagine the former would lead to a higher vaccine acceptance than the latter.

But I'm not a public messaging expert.
With that said, let's put things to perspective:

While the COVID-19 vaccine may be more unpleasant than the flu vaccine, severe side effects are very low (<5%). And it's a lot better than actually getting COVID-19. Or seeing our loved ones get it.

The vaccine will save lives.
The @sfchronicle headline sums it up best: "The coronavirus vaccine comes with more side effects than a flu shot. Experts urge people to get it anyway."

For those that are interested, the source for all of the side effects can be found in this paper: nejm.org/doi/full/10.10…

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More from @youyanggu

15 Dec
If we vaccinate 10 million people today, statistically 300 of them will die the very next day. Regardless if they actually got vaccinated or not.

Over the next months, it's important to watch for misinformation that blames adverse events on the vaccine.

Below is an example of the misinformation that can spread.

The annual incidence of Bell's palsy is ~25 per 100k. There were 4 cases out of 40k participants.

The FDA concluded it's "consistent with the expected background rate in the general population."

In statistics, this is a simple application of something called Bayes Rule.

In essence, we must consider the likelihood of an event happening independently.

For ex: a 90-year-old has a 1 in 6 chance of dying within a year. So this happening after a vaccine would not be unusual.
Read 4 tweets
11 Dec
By many accounts, the US will have 100 million vaccine doses by February.

I estimated yesterday that we need ~100 million people to gain immunity via vaccination to reach herd immunity.

So *theoretically*, we can reach herd immunity by March if we vaccinate the right people.
This involves allocating the initial (limited) supply of vaccines based on two main criteria:

1) Each individual receives only one dose instead of two.
2) We prioritize individuals who have not had a prior infection.

This would be temporary, until supply catches up.
There is some evidence, though inconclusive, that even one dose of the vaccine can have reasonable efficacy (potentially >80%).

Read 15 tweets
10 Dec
I launched a new page that shows the path to US COVID-19 herd immunity: covid19-projections.com/path-to-herd-i…

It's built on the assumption that herd immunity will be achieved via vaccination and natural infection.

Tl;dr version: I estimate a "return to normal" by June/July 2021.
The underlying methodology is a simple model that simultaneously simulates daily vaccinations and new infections through 2022.

By May/June 2021, I estimate vaccinations to exceed 1 million people per day as they become available to the general public.
By mid-summer 2021, I estimate roughly 1/2 of the population have been vaccinated & 1/3 of the population have been infected.

After accounting for overlap/loss of immunity, this amounts to ~60% of the population possessing immunity to the virus, sufficient for herd immunity.
Read 8 tweets
9 Dec
The COVIDhub Ensemble model that combines all the models did not perform well over the past 2 months.

This is due to the fact that the majority of model submissions did not properly forecast this current wave.

Roughly half of all models failed to beat the baseline. ImageImage
This is a known issue with pandemic modeling. For most scenarios, it's beneficial for models to make forecasts close to the status quo (since that's usually true).

This means the they're accurate a majority of the time, but they will miss large spikes such as this current wave.
On the flip side, if a model predicts a large spike and is wrong, it will be heavily penalized by most evaluation metrics. This can happen even if the spike does happen but is a few weeks early/late.

That's the dilemma a lot of modelers face, including myself earlier this year.
Read 7 tweets
3 Dec
I posted the methodology for the new covid19-projections.com nowcasting model:

covid19-projections.com/estimating-tru…

I'm going to do a layman summary here, and hopefully receive some feedback from #epitwitter. Image
I've adjusted the methodology that I posted back in August based on new data and research:



Disclaimer: with that said, this is still a simple heuristic and hence is not perfect. There are more advanced methods (e.g. see covidestim.org).
The basic idea is this: for each day, we try to estimate the ratio of true infections to reported cases that day.

We call this the prevalence ratio, and we model this ratio as a function of the day and positivity rate: Image
Read 16 tweets
1 Dec
I deployed some new features to covid19-projections.com over the past week. Here's a brief summary:

1) Maps over time - you can now view how the pandemic progresses over time for the US, on both a state and county level: covid19-projections.com/maps-infection…
2) Plots of confirmed cases and deaths for every state and county in the US (in addition to estimates of true infections).

Example: covid19-projections.com/infections/us Image
3) Methodology writeup: covid19-projections.com/estimating-tru…. Will write a more detailed Tweet soon.

4) Daily county-level estimates: github.com/youyanggu/covi…. Due to storage constraints I moved it to a separate repository.
Read 5 tweets

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