Dr Rosanna C Barnard (she/her) Profile picture
Dec 11, 2021 18 tweets 9 min read Read on X
The B.1.1.529 SARS-CoV-2 variant was first reported on 24th November 2021, and 2 days later @WHO designated it #Omicron. Since then we (me @_nickdavies @cap1024 @markjit and John Edmunds) have worked to understand potential consequences for England. 🧵 [cmmid.github.io/topics/covid19…] Figure 1 from the paper linked in the tweet. This figure has
First, what do we know about Omicron?

1⃣ it has a large number of mutations (see covdb.stanford.edu/page/mutation-…)
2⃣ there is evidence from South Africa (s/out @SACEMAdirector and team) of an increased reinfection risk associated with the emergence of Omicron: An image showing the mutations within the genome of the Omic
3⃣ Omicron neutralisation studies are emerging (~5 so far this week). These suggest a drop in neutralisation for Omicron
4⃣ @UKHSA vaccine effectiveness data suggests a significant reduction for dose 2 Omicron compared to Delta ()
5⃣ the same @UKHSA data shows that following a Pfizer booster, vaccine effect is increased against Omicron (71.4% and 75.5% protection vs. symptomatic disease for people who received AZ and Pfizer primary courses, 2 weeks post their boosters)
khub.net/documents/1359…
Given all of this, we adapt our existing model of SARS-CoV-2 transmission in England to include the Omicron variant.

The model is fitted to data on COVID-19 deaths, hospitalisations, PCR prevalence and seroprevalence data, as well as vaccination coverage including boosters.
We estimate that Omicron is exhibiting a 2.4-day doubling time in England.
Given this, we expect that Omicron will become the dominant variant in England in a matter of weeks.
We consider two scenarios for Omicron's immune escape (5.1- and 12.8- fold reductions in neutralisation titre relative to Delta). These correspond to ~45% and ~70% reductions in vaccine protection against infection using a scaling from Khoury et al.

nature.com/articles/s4159…
We assume vaccine protection against severe outcomes is more robust than it is against mild infection. This relationship has held for all variants so far ➡️ no reason to think this won't hold true for Omicron.

Again, we use Khoury et al. for this relationship (see above)
We then consider low and high scenarios for the effectiveness of booster vaccinations against Omicron: 2.5- and 4.9-fold increases in neutralisation. This gives us four main scenarios. Full details in the preprint.
Under existing control measures, we project a wave of infections resulting from Omicron that exceeds the January 2021 levels, for all four scenarios.

In the optimistic scenario, hospital admissions are projected to remain below Jan 2021 levels.
In the pessimistic scenario, hospital admissions are projected to exceed levels recorded in January 2021, by around 2-fold. The other scenarios are intermediate.
We also consider the introduction of control measures in addition to current "Plan B" measures.

Optimistic scenario: milder control measures can keep hospitalisations below January 2021 levels.

Pessimistic scenario: the model projects stronger control measures may be needed.
We look at sensitivity analyses for the rate of Omicron introductions into England, booster uptake, and speed of booster rollout.

Changes in introduction rate of Omicron shift the epidemic.

Booster uptake has a significant effect.

Booster rollout speed has a smaller effect.
Caveats:

1⃣ Data are still coming in. We will update this work continuously as new data emerges.

2⃣ We don't yet know what the severity of Omicron is. We have assumed no change in the intrinsic severity of Delta ➡️ Omicron.
3⃣ We assume future behaviour remains flat (we model this using mobility data)

4⃣ We only consider the epidemiological outcomes of control measures here. We do not assess their wider effects, e.g. on social, economic and wider health costs, which we know are substantial.
Fundamentally, it's up to policy makers to weigh up all of the evidence and decide what to do about #Omicron

We can only provide evidence on the likely epidemiological outcomes.
Thanks to my co-authors @_nickdavies @cap1024 @markjit John Edmunds @cmmid_lshtm @LSHTM

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More from @BarnardResearch

Aug 23, 2022
My research (with @_nickdavies @cmmid_lshtm @markjit & John Edmunds) is published in @NatureComms 🥳

This is the culmination of many months of work (my funding from @Epipose)🍾🎉

nature.com/articles/s4146…

Until I write a book on this (publishers/agents hmu😝), here is a 🦣🧵
The code to reproduce this work is available at: github.com/rosannaclaireb… 🛠️⚙️🧩🏗️

This implementation of 'covidm' builds on work by @_nickdavies (the OG creator) in the early #COVID days

Since joining @LSHTM in July 2020, I have developed covidm with @_nickdavies

👩‍💻👩‍🔧👩‍🎨👩‍💼👩‍🔬
Early 2020 discussions @cmmid_lshtm led @_nickdavies to build #covidm

CMMID members @LSHTM (e.g. @rozeggo, John Edmunds, & @adamjkucharski) contributed to these discussions

Many other colleagues contributed to model development (e.g. @cap1024 @Lloyd_Chapman_ @KevinvZandvoort)
Read 29 tweets
Jul 12, 2021
An updated 🧵 on our latest report modelling #step4 of the #roadmap on 19th July 2021 🛣️🪜 with @_nickdavies @markjit & John Edmunds @cmmid_lshtm 👩‍💻 (published today) ... #storytime with pictures 📒📷 gov.uk/government/pub…
We were asked to model roadmap #step4 happening on 19th July with:
▶️ different vaccine effectiveness vs. Delta δ B.1.617.2
▶️ different transmission levels after step 4

We used the same #agestructured #compartmental #deterministic model as before An image showing the compartments and transitions between di
This time, we ran *a lot* of model fits, shoutout @PushoverApp.

We fitted the transmissibility (TX) of δ relative to α given (central and optimistic) assumptions on vaccine effect 💉

We also produced model fits w/ combinations of:
🌘 waning immunity🌖
😣 severity of delta 🏥 A screenshot showing the Pushover app with lots of notificat
Read 11 tweets
Jun 14, 2021
It's been a busy few days/weeks/months modelling steps 2, 3 and 4 of the #roadmap out of lockdown in England, with my #ateam @_nickdavies @markjit and John Edmunds. A 🧵 on our latest work looking at #step4 🪜👩‍💻 and a #photodump of some nice things I saw along the way A photograph of the West pier and Brighton beach with low ti
We project the dynamics of #SARSCoV2 transmission in England using an #agestructured #transmission #model which divides the population into vaccine states and disease states. The model has compartments for three #COVID19 variants (OG, #alpha and #delta) and 2 two-dose vaccines 💉 An illustration of the age-structured compartmental transmis
To capture changes in behaviour (➡️ the amount people mix in the model), we use historic #mobilitydata and make assumptions about what might happen to mobility when policies are implemented, e.g. #step4 🍀 We make low, medium and high assumptions to account for uncertainty... ⭐️ Figures showing google mobility data between January 2020 an
Read 11 tweets
Dec 24, 2020
A lot of attention on our @cmmid_lshtm preprint on the SARS-CoV-2 VOC 202012/01. For some background, the precursor work on a (single-variant) model looking at tiered restrictions and lockdown in England was published last night: authors.elsevier.com/sd/article/S14… ✨PEER REVIEWED(!)✨ 1/9
We fitted a (single-variant) model of SARS-CoV-2 transmission to the first and second waves in England from March - October 2020, using a number of data sources across NHS England regions 2/9 Image
We used mobility and contact survey data to assess the effects of regional tiered restrictions (Tiers 1, 2, 3) introduced in England in October 2020 (pictured), and the effects of the Welsh firebreak 🔥 Northern Irish circuit breaker 🔐 lockdowns 3/9 Image
Read 9 tweets

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