Any discussion of daily testing vs quarantine for contacts of cases in schools needs to address the key epidemiological question: if a child in a school tests positive, what do you do next? 1/
Encouraging ventilation etc. to reduce transmission risk is important, but you still have to decide what to do about a positive result. Do you quarantine their contacts or not? 2/
If you decide to abandon quarantine because you think ventilation etc. has sufficiently reduced risk, then this still means accepting higher transmission risk than if quarantine had remained in place. 3/
However, if you decide anything that results in more transmission risk than implementing full quarantine is unacceptable, then this means implicitly placing zero value on keeping children in school… 4/
Suppose, hypothetically, daily testing means 1% higher transmission risk but avoids quarantine keeping contacts out of school. Is this a ‘worse’ outcome than before? How about if transmission risk is 5% higher? Or more? 5/
If you decide 'less transmission is always optimal' is guiding principle, then should quarantine be extended to 14, or even 21 days, to ensure maximum transmission reduction? 6/
More broadly, should school-based measures be implemented in future for other respiratory infections (e.g. cidrap.umn.edu/news-perspecti…)? 7/
Ideally, we’d all like less COVID and less disruption. But the has pandemic forced decisions on what to prioritise, and what is an acceptable tradeoff, so need to consider available options carefully. And finally, a reminder:
How long could UK cases continue to rise? And how might hospitalisations increase alongside? A thread... 1/
Despite relatively high vaccination rates compared to other countries, cases are growing and in many areas R is now above 1.5. Remember, immunity is already 'priced in' to this number - without vaccination and the social distancing still in place, R would be *much* higher. 2/
If R is 1.5 and contacts/control remain the same, then we'd need remaining part of the population who could potentially spread COVID to shrink by at least 33% before R drops below 1 & epidemic peaks. This would require additional immunity, either from infections or vaccines. 3/
A common Q: “how can COVID hospitalisations in UK still grow if vaccine % high?” Answer: look at the data. Average was ~120 daily COVID admissions over past week. These would have been infected about 2-3 weeks earlier, when case numbers ~2000 per day. This shows two things... 1/
First, there was still a group at risk of hospitalisation a few weeks ago. And second, this risk was large enough to show up as hundred of admissions in recent data, even though cases were at relatively low levels. 2/
So the key question here: if case numbers were to grow X times larger, why wouldn’t hospitalisations also grow X times larger? 3/
One thing that has hugely shaped countries' response to COVID, and which I don't think gets enough discussion: genuine constraints and perceived constraints. A thread... 1/
Some apparent local constraints have persisted throughout the pandemic. For example if you look globally, there are still notable differences in approaches to surveillance and quarantine... 2/
It's worth reading these papers on the response in Taiwan (jamanetwork.com/journals/jama/…) and Korea (jamanetwork.com/journals/jama/…). Should more countries include these data-intense approaches in future pandemic plans? Or does reluctance to date reflect an immovable constraint? 3/
Preliminary UK data on vaccine effectiveness against B.1.617.2 (originally detected in India) now available: khub.net/documents/1359…. A few things to note... 1/
First and foremost, it’s another reminder that *second doses matter*. By Aug/Sep, UK will be in much better position against B.1.617.2, but there’s a risk of substantial transmission in meantime as things reopen. 2/
Also remember that when vaccine effectiveness high, small absolute differences can have big effect. E.g. a drop from 95% to 90% would double number at risk (and probably more than double outbreak size given non-linear nature of transmission). 3/
In real-time, epidemic data streams are patchy, delayed, biased and often contradictory. That's why scientists use terms like 'realistic possibility', 'medium confidence' etc. Uncertainty is inevitable (although will reduce over time) - and yet decisions still need to be made. 1/
B.1.617.2 has been spreading fast in some areas, and people are working hard to disentangle causes & quantify exactly what it means for wider transmission. Control measures are now both going in (e.g. testing, vaccination) & being relaxed, which making analysis even trickier. 2/
Have recently been thinking more about the transmissibility of endemic seasonal coronaviruses... antibody positivity increases sharply at a relatively low age, suggesting high transmissibility in susceptibile populations (below from: nature.com/articles/s4146…). 1/
Older groups have built immunity to seasonal CoVs, but above suggests R0 (i.e. R in fully susceptible population) could be quite high for these viruses. Possibly so high that even stringent measures wouldn't be enough to control in susceptible pop?