I think people find it hard to understand the deal with kids reduced susceptibility to #COVID19 and what it means for schools etc

I thought I’d share a little analogy which helps me conceptualise it

Let’s imagine #COVID19 is rain... 🌧

1/8
Everyone wants to stop getting wet

Adults are just dressed normally 👦

Teens have a little hat on 🧢

Younger kids have got an umbrella ☂

2/8
When there is very little rain, you won’t notice much difference between how wet everyone is

When it starts raining a bit, you’ll notice the younger kids are less wet than adults

The teens may be just as wet as they spend more time out in the rain

3/8
But what happens when there’s a monsoon?

Well a rain hat and coat aren’t going to cut it

Everyone gets soaking wet

And the problem is once you’re wet, you start making other people wet too

4/8
What we’ve seen more recently in England is 2 things:

1. Very high infection prevalence - Monsoon rain

After this happened, we shut the adults indoors and left the kids out in the rain (in school)

5/8
2. A more infectious virus variant - this is like stripping off a layer of protection for everyone (including kids)

When it rains, more people are now getting wet than before

It will be harder to leave kids (and everyone else) out in the rain without them getting wet now

6/8
Fortunately kids and teens still don’t suffer much when wet

But the best way to keep them dry is to stop it raining

That’s what we need to do over the next few weeks to get them back to school

7/8
We can add extra measures to school (masks, more distancing, better ventilation etc), but ultimately these are like putting on a hat/raincoat etc

They are useful when it’s not raining much

They won’t hold back a flood

8/8

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More from @apsmunro

3 Jan
The current issue of #schoolclosures is so complex I am loathed to add my opinion to the twitter soup

Massive uncertainties✅
High stakes✅
Huge health trade offs✅
Opposing opinions ✅
Easy answers 🚫

I'll just add a few simple thoughts

1/9
Firstly, don't be fooled by twitter

It has the most shouty and polarised opinions only, the moderates have been largely hounded out

Both public and scientific opinion is almost certainly much more nuanced and balanced than this platform would fool you into believing

2/9
Next, advocating for widespread closure is a legitimate opinion in the current environment

Massive, uncontrolled community transmission and looming hospital capacity issues, with high prevalence of infections among children (esp teens) are a bad mix

3/9
Read 9 tweets
18 Dec 20
New findings from Swiss seroprevalence and an good opportunity to learn some more infectious disease epidemiology!

A similar prevalence in children aged 6 - 18 y (23%) than the adult population (~27%)

Lower in kids <6y (~15%) and over 65y (<15%)

What can we learn?

1/8
First, another lesson in age binning for kids (will we ever learn?)

6 - 18y is a weird age bin which I would not recommend

It hides big epidemiological differences

In fact, we have seen it in related data before...

2/8
The pre print of the first study from this team used age bin 5-19 y and reported no statistically different prevalence vs adults

medrxiv.org/content/10.110…

They changed for publication and found significantly lower prevalence in children <10y

thelancet.com/journals/lance…

3/8
Read 8 tweets
15 Dec 20
Here is a good lesson in basic epidemiology

This data is difficult to interpret in a number of ways:
1. There is no denominator, either of institutions or populations
2. It counts the number of outbreaks, not cases
3. It doesn't tell you who had the infections

This matters

1/5
An extreme example of why this is important from earlier this year

After lots of excitement about outbreaks in schools it turned out the median number of children involved was...

Zero

2/5
For some denominator context there are:

- 24,000 schools (9 million pupils) not inc. university
- 11,000 care homes (410,000 residents)
- 117 prisons (79,000 prisoners)
- 26,000 restaurants (many not/partially open)

We are comparing apples and oranges

Epidemiology 101

3/5
Read 5 tweets
11 Dec 20
It's time to clarify some things about children, schools and #COVID19 🧵

Summary: Young children seem significantly less susceptible, probably less likely to transmit. Less clear for teens. Schools mainly follow community trends, but secondary much higher risk than primary

1
The best way to determine susceptibility is through household contact tracing, as it controls for *exposure* - everyone gets more or less the same

There are many of these. Results vary, which we expect because infection is complicated

That's why we need to combine results

2
Here's 4 meta analyses; all find young children are much less susceptible than adults. Some that teens are too

Zhu (RR 0.6)
academic.oup.com/cid/advance-ar…

Goldstein (RR~0.5)
dx.doi.org/10.1093/infdis…

Viner (OR 0.41)
jamanetwork.com/journals/jamap…

Maidwell (RR ~0.5)
medrxiv.org/content/10.110…

3
Read 24 tweets
27 Nov 20
Really interesting pre print looking at school transmission in Northern Italy during the second surge

Suggests huge age dependant differences in risk of transmission in children in these settings (who would have guessed?!)

Let's take a look...

doi.org/10.1101/2020.1…

1/8
Detailed contact tracing of 43 cases
- 5 teachers
- 38 children

Whole classes tested regardless of symptoms, and swabbed a second time 10-14d later if the first was within 6 days of exposure

How much onward transmission?

2/8
Pre school (6 kids, 2 teachers) - 0/156 (0%)
Primary school (14 kids) - 1/266 (0.44%)
Secondary school (23 kids, 5 teachers) - 38/572 (6.64%)

Notice a pattern here?

3/8
Read 8 tweets
26 Nov 20
Some people are *still* lumping "children" all together in talking about transmission, schools etc

That is a dumb thing to do

It doesn't take a paediatrician to tell you a 2yr old is different from a 17yr old

Case study using data from the @ONS

ons.gov.uk/peoplepopulati…

1/8
The @ONS performs large scale, *random* population testing via rt-PCR

People often complain test data in children is biased by lower symptom burden - that is not the case for this data

Hence, it is very informative (why aren't more countries doing this?!)

2/8
Here is a graph of the last few weeks modelled incidence by age group

Huge initial spike in 17 - 24yr olds
Up behind then followed 12 - 16yr olds
Then 25 - 34yr olds
Then 35 - 49yr olds
Then 50 - 69yr olds

Eventually the 2 - 11yr olds get dragged up behind

3/8
Read 8 tweets

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