School’s out forever?

Colleagues from the University of Edinburgh did a really nice job exploring the impact of individual public health interventions on the SARS-CoV-2 reproduction number (R) across 131 countries. Their work fueled the discussion on whether schools should be closed to control transmission. Rightfully so? Read Patricia Bruijning-Verhagens’ take on this study.

For their analyses they used the real-life interventions as they were implemented when the pandemic started and subsequently lifted this summer, inevitably with differences in timing and sequences between countries. Yet, this variation allowed them to explore how each intervention influenced the effective R-value (Reff) over time in each country. A few reflections on the study:

First, we need to understand how comparisons were made; for each country they cut the observation period into time fragments based on the non-pharmaceutical public health interventions (NPIs) that were used. A change in NPI – implemented or lifted – starts a new fragment, which can last from days to months.  For each day in a fragment, they took the Reff from the available country data, and compared the Reff from the last day of a fragment to the Reff on the first day of the new fragment, and subsequently to the Reff values of all subsequent days in that fragment. The result is a daily ratio of old versus new Reff values following a change in NPI.

Next, all Reff ratios were entered in a multivariate model to determine associations between Reff ratios and implementation or lifting of individual NPI. Results can be interpreted as; what is the relative effect of implementing intervention A on Reff, while keeping measures B, C, D, etc. constant.  Importantly, effects are quantified in terms of  RELATIVE reduction/increase in Reff. ABSOLUTE effects of NPI will depend on the Reff at the start of intervention. For example; The Reff ratio for a ban on public gatherings is 0.76 (minus 24%) when we compare the Reff at day 28 after implementation to a situation without bans. Then, if Reff was 3 before implementation, the ban on public gatherings will reduce the Reff to 0.76*3=2.28 at day 28, yielding an absolute reduction in Reff of 0.72. Yet, if Reff was 1.2 at the start, then the absolute reduction will be 0.29 (0.76%*1.2=0.91).

The results of the multivariate model highlight another effect that needs to be considered; whith multiple NPIs implemented/lifted at the same time, their joint effect is smaller than the sum of their individual effects. This is estimated as interaction parameters Z1 and Z2. For instance, closing schools has an Reff ratio of 0.86 on day 14 following closure and the Reff ratio for banning public gatherings is 0.83. The Reff ratio for interaction on day 14 is approximately 1.17 as you can see in the figure below.

So, the interaction eliminates the effect of one of both interventions. The same happens when lifting two interventions at the same time; the joint increase in Reff is less than would be expected on the Reff ratios from each NPI separately. The effect of an NPI may thus differ, depending on the context (i.e. other NPIs in place). An alternative explanation is that the model overestimates the single intervention Reff ratios, because of collinearity in the data. Ideally, one would estimate interaction effects separately for each possible combination of two NPIs, but this requires inclusion of many more parameters in the multivariate model, which were not available. This interaction effect also becomes apparent when we look at the four scenarios of composite NPIs; Moving from scenario candidate 3 to 4, the Reff ratio for day 28 changes by 0.10 only, although two more interventions were added (school closure and stay at home requirements).

An important limitation of the data is that many interventions were implemented or released shortly after one another, seriously limiting the number of informative datapoints and precluding quantification of individual effects of interventions. This is reflected by the wide confidence intervals for many estimates. For instance, schools were already closed at the start of the observation period in 64 of 131 countries and only 25 countries lifted school closure at some point. Moreover, school closure was followed by other interventions within a week in 75% of countries, leaving only 16 countries with more than 7 days to quantify effects of school closure as separate intervention. Furthermore, differences across countries add to heterogeneity in the data and, thus, to imprecision in estimates.

To conclude, this study provides some insight in the effectiveness of some NPIs, but precise effects of individual interventions remains uncertain and will highly depend on the prevailing Reff at the time of implementation/lifting, and other interventions implemented, lifted or maintained. The authors acknowledge some of these limitations and caution that ‘ the impact on R by future reintroduction and re-relaxation of interventions might be substantially different’. Obviously, many readers that claimed major effects of NPI, in particular of school closure, didn’t make it till this stage of the manuscript.

Patricia Bruijning-Verhagen, MD, PhD, is pediatrican and epidemiologist at the Julius Center for Health Sciences and Primary Care, at the UMC Utrecht

The role contaminated surfaces in COVID-19 transmission: a HIS audience-led webinar

The next instalment of the HIS audience-led webinar series is on the role of contaminated surfaces in COVID-19 transmission. I was delighted to be part of the panel for this one:

  • Dr Lena Ciric – Associate Professor in Environmental Engineering, University College London
  • Dr Stephanie Dancer – Consultant Microbiologist, NHS Lanarkshire and Professor of Microbiology, Edinburgh Napier University, Scotland
  • Dr Manjula Meda – Consultant Clinical Microbiologist and Infection Control Doctor, Frimley Park Hospital
  • Dr Jon Otter – Infection prevention and control Epidemiologist, Imperial College London
  • Chair: Dr Surabhi Taori, Consultant microbiologist and infection control doctor, Kings College Hospital NHS Foundation Trust

Here’s the recording:

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Update on 2019-nCoV: part 11 – where will it end?

I’m sure we’ve all been following the emerging story of the 2019-nCoV outbreak closely, with the third cases reported in the UK yesterday (pleased to see this is where you’d expect the UK to be based on Marc’s post earlier)! There’s been a small explosion of publications in the peer reviewed literature. I’ve chosen one slightly randomly to discuss today: a short modelling study providing some insight on the likely volume of unreported cases (very much the ice berg and not the tip!) and some sense of where this outbreak will end (it depends on how we respond, globally).

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Update on 2019-nCoV: part 8 – ‘silent’ transmission

One of the key questions that we posed when this virus first emerged was is ‘silent’ transmission (that is transmission to others before symptoms become apparent) possible? And if it’s possible, is it the norm? A short letter published in yesterday’s New England Journal of Medicine answers the first part of that: silent transmission of 2019-nCoV is possible – but just how common is it?

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Update on 2019-nCoV: part 6 (winging its way around the world)?

I am interested, selfishly, in understanding the risk to Europe presented by the novel coronavirus (which now has a “working” name: 2019-nCoV; catchy isn’t it?!). It seems likely that there will be more imported cases, and possibly also some limited cross-transmission in Europe over the coming weeks.

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Novel coronavirus outbreak: an update

I posted at the beginning of last week about the emergence of the as-yet-formally-unnamed novel coronavirus that has emerged in China. At that stage, it could have been a parochial anomaly in the annals of ID history. What a difference a week makes! Now we are looking at a rapidly emerging international outbreak!

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Can you GES which carbapenemase caused this CPE outbreak?

An unusual and interesting outbreak of CPE was published recently in Clinical Infectious Diseases. Several key points: don’t rely solely on a PCR detecting the “Big 5” carbapenemases (NDM, KPC, OXA-48, IMP, VIM) – at some point you need to test for phenotypic carbapenemase activity; WGS can really help us in unravelling complex transmission routes; and covert plasmid propagation within and between species is a reality.

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CPE has landed in East London

The team at Barts Health, one of the largest NHS hospital groups in the country, has published the findings of a point prevalence screen of all inpatients for carbapenemase-producing organism (CPO) carriage. Overall, 30 (3.1%) of the 977 patient tested were carrying 35 different CPOs (all but one of which were CPE). Risk factors for CPO carriage included hospitalisation abroad, any hospitalisation, and overseas travel (especially to India, Pakistan, and Bangladesh). These findings help us to understand an emerging picture of CPO in the UK.

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It (the flu) came from the desert

We tend to find that the flu season in Australia is an early predictor for the severity of the coming flu season in the Europe. And the early indications are the flu in Aus this year is bad – unprecedentedly bad. So, let’s get our flu vaccination campaign planning hats on!

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