Research Fellow at University of Oxford @bdi_oxford. Using maths and stats to understand infectious disease dynamics, mostly viral kinetics and serology.
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Apr 6 • 12 tweets • 4 min read
New preprint: Reconstructed influenza A/H3N2 infection histories reveal variation in incidence and antibody dynamics over the life course
1/12medrxiv.org/content/10.110…
TLDR: we inferred infection histories for 1,130 individuals using 70,000 antibody titers measured against influenza A/H3N2 strains isolated between 1968 and 2014. This enabled us to study the long-term epidemiology of influenza. 2/12
Jun 23, 2022 • 24 tweets • 9 min read
Preprint: impact of immune history and variant on SARS-CoV-2 viral kinetics and viral rebounds: medrxiv.org/content/10.110…
Ct trajectories from ~3000 infections (pre-BA.2) stratified by exposure (vaccine/infection) history, symptoms, variant, and antibody titers to WA1 spike.
1/24
Highlights
- >50% Ct<30 on day 5 regardless of symptom, immunity & variant
- Viral rebounds rare but more frequent in BA.1/booster era
- *BA.1-infected*, boosted individuals had longer clearance times than non-boosted, and *lower WA1 spike antibody titers prior to booster*
2/24
Mar 18, 2022 • 14 tweets • 4 min read
Speculation that these data are suggestive of antigenic sin/seniority effects. IMO these data are just consistent with back-boosting when you have relevant immune memory to call upon, and little back-boosting when you don't. Seniority effects are possible, but not necessary 1/13
2) Omicron had lower peak viral load and more variable early viral growth durations than Delta
1/13
We obtained longitudinal RT-qPCR tests (a combined anterior nares & oropharyngeal swab in each person) collected between July 5th 2021 and January 10th 2022 from the NBA occupational health program. This included 97 Omicron infections and 107 confirmed Delta infections. 2/13
Apr 20, 2021 • 13 tweets • 2 min read
What is a public health test? A thread on population-level vs. individual-level optimal criteria (this is for a thread to be linked elsewhere so is probably quite incoherent)
From a clinical perspective we want a test that is optimal for helping the individual being treated. So we want to be as sure as possible that the result leads us to the right diagnosis and treatment
Feb 13, 2021 • 14 tweets • 5 min read
Ct values can be used to estimate epidemic dynamics UPDATE! Ct values are expected to change depending on whether the epidemic is growing or declining, and we harness this to estimate the epidemic trajectory. Lots of cool new analyses and methods! 1/12
medrxiv.org/content/10.110…
Highlights:
- Cts from symptom-based surveillance change over time, but the effect is weaker
- Methods to infer incidence using single cross-sections of Cts
- Unbiased by changing testing coverage
- Gaussian process (wiggly line) model for incidence tracking using Ct values
2/12
Oct 14, 2020 • 25 tweets • 7 min read
Ct values can be used to estimate epidemic dynamics! We show that the distribution of viral loads changes during an epidemic and develop a new method to infer growth rates from cross-sectional virological surveys without using reported case counts.
1/25
medrxiv.org/content/10.110…
Highlights:
- The distribution of observed viral loads is determined by recent incidence trends
- The median and skew of detectable Cts in Massachusetts were correlated with R(t), as predicted
- A novel statistical method to infer the epidemic growth rates using Ct values
2/25
Oct 7, 2020 • 18 tweets • 5 min read
Very excited to share our updated preprint on pooled testing for SARS-CoV-2 surveillance. This has been a fantastic modeling and lab collaboration with @BrianCleary, @michaelmina_lab and Aviv Regev, and it’s all about our favorite topic: viral loads. 1/16
medrxiv.org/content/10.110…
Highlights:
-PCR sensitivity and efficiency are linked to epidemic dynamics and viral kinetics
-Prevalence estimation without testing individual samples using a few dozen tests
-Simple (by hand) strategies optimized for resource-constrained settings
Full story below. 2/16
Mar 27, 2020 • 10 tweets • 4 min read
The lack of clinical COVID19 cases in children is odd. Understanding why will be essential in deciding which social interventions are most useful. We discussed this alongside a modeling analysis here: dash.harvard.edu/handle/1/42639… @DrDJHaw@BillHanage@CJEMetcalf@michaelmina_lab
We propose 4 possible explanations for the lack of cases in children:
1) Kids haven’t been making as many contacts as normal. This may contribute, but probably isn’t the only factor: medrxiv.org/content/10.110…
Mar 25, 2020 • 20 tweets • 6 min read
A recent analysis from Oxford presented a range of model scenarios consistent with observed COVID death counts. I’m going to reproduce their analysis here and then present some slight modifications to provide a conservative (if technical) perspective. (gonna be 15ish tweets)
They showed you can estimate the same number of deaths with either a high % of the population at risk of severe disease and a recent epidemic start, or a low % and an earlier start. Some media outlets have reported this as suggesting “majority of the UK has already been infected”
Mar 7, 2020 • 4 tweets • 1 min read
Total isolation isn’t the only way to reduce #coronavirus spread. Reducing unnecessary contacts (only invite the best people to your birthday party) helps. Based on school maths: if you roll a die 10 times, the probability of 1 or more 6s is 84%. If you roll it twice, it’s 31%.
This also applies if you reduce the nature of your contacts. For example, if you stop licking your friend’s face, maybe you go from a 6-sided die to a 20-sided die. Then the probability of at least 1 20 at your 2-mate birthday party goes down to 10%.
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