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

Update on COVID-19: part 14, the changing picture

Where the world is gradually (or instantaneously) facing the COVID-19 reality, China claims victory. Yet, it ain’t over till it’s over, and many fear a rebound once daily life has returned to its normal practices and contact patterns. In the meantime our Chinese colleagues keep on producing very impressive epidemiological studies. Such as this one, published today. Continue reading

Update on COVID-19: part 13, it giet oan?

In February many in my country just want to hear three words: “it giet oan”. This means that the famous eleven city ice-skating race will actually happen. Since 1909 this occurred 15 times (last time in 1997), as it needs about three weeks of continuous frost. No chance for that this year, but the words must have crossed many clinical microbiologists’ mind this weekend when the news on SARS-CoV-2 from Italy unfolded. Continue reading

Update on COVID-19 caused by SARS-CoV-2; part 12

part from its new name, nothing really happened in the last week. New confirmed cases per day remained high, seemed to decline somewhat until it became clear that there was shortage of testing capacity. Definition adapted, and then the numbers of infected patients in the affected Chinese regions exploded. So, one question answered from previous blogs: it’s big in China and we may (still) not know everything that is happening there (or in some other countries). Continue reading

Update on 2019-nCoV: part 10

The 2019_CoV outbreak remains as interesting as the House of Cards once was (until it was bypassed by reality). After the gold rush for R_0, last week was devoted to the question on silent transmission; yes or no. Tuesdays’ story had an unexpected follow-up today, but the true clifhanger is a new study published yesterday. Continue reading

Update on 2019-nCoV: part 9 – ‘silent’ transmission

A rude awakening this morning: “Study claiming new coronavirus can be transmitted by people without symptoms was flawed” was reported in Science. This is the patient described by German colleagues in NEJM and by Jon in the previous blog post.

The story in short: An index patient (a woman from Shanghai) was visiting Germany for business, and managed to transmit the coronavirus to two patients, who subsequently transmitted the virus to two other colleagues, before symptoms arose (in the index patient). The story was reported by hospital physicians from Munich and a virologist from the Charité hospital in Berlin (where the famous Robert Koch once worked).

In Science: “The Robert Koch Institute (RKI), the German government’s public health agency, has written a letter to NEJM to set the record straight, even though it was not involved in the paper.”

The researchers in Munich didn’t actually speak to the woman before they published the paper. “Afterward, however, RKI and the Health and Food Safety Authority of the state of Bavaria did talk to the Shanghai patient on the phone, and it turned out she did have symptoms while in Germany. According to people familiar with the call, she felt tired, suffered from muscle pain, and took paracetamol, a fever-lowering medication.”

When reading this I recalled two dreams I had this night.

Somewhere – in a not-mentioned country – physicians were very excited as the first patient with disease X had fallen in their lap. “Let’s first send this to NEJM and then contact our public health officials, otherwise they run away with it.” The day after – at the other site of the country – the NEJM fell on the floor in the oval office of the head of public health – responsible for the nations’ faith. He/she contacted the index and found out that she – in retrospect and after 20 times repeating the same question – admitted that “yes, she felt tired, had some muscle pain and took a paracetamol”. “That’s it”, he/she shouted, “this is the perfect call”, “READ THE TRANSCRIPT!” and called Science.

The other dream: Somewhere – in a not-mentioned country – physicians were very excited as the first patient with disease X had fallen in their lap, and they immediately contacted the head of public health – responsible for the nations’ faith. “Let’s first send this to NEJM and then contact the index. Might give us 2 citations in NEJM.” The day after the NEJM fell on the floor in the oval office of the head of public health and he/she contacted the index and found out that she – in retrospect and after 20 times repeating the same question – admitted that “yes, she felt tired, had some muscle pain and took a paracetamol”. “That’s it”, he/she shouted, “this is the perfect call”, “READ THE TRANSCRIPT!” and called Science.

Then my alarm went off, and I couldn’t dream of other, more realistic scenarios.

The big question now is whether our view on the transmission dynamics of this outbreak should change with this new information. The point of silent transmission, i.e. before symptoms occur, is that it will be more difficult to identify infectious persons and isolate them in time to interrupt transmission. The symptoms reported in retrospect (when knowing of being indeed infected, susceptible to recall bias!) were “tiredness and muscle pain”. Cough and fever are not mentioned. If this is what it is, then this woman might still have been identified of being at risk for 2019_nCoV infection in Germany, simply because she came from China. Yet, in Wuhan this would probably not be recognized as a risk, necessitating isolation. And the same would hold if sustained transmission occurs in other countries.

So, this information sheds new light, and addresses the definition of being asymptomatic. If symptomatic would be defined as “symptoms that allow someone to be recognized as infected and to be isolated in time to prevent transmission”, I think, this subject would be considered asymptomatic.

The letter from RKI has not yet been published. The Science reporting is based on someone who was in the room during the phone call (sweet irony).

Disclaimer: if the letter describes a feverish Chinese woman coughing continuously, I immediately change some of my views.

Update on 2019_nCoV outbreak part 7

We live in fascinating times. Within a week R_0 has be become a fashionable topic to discuss at cocktail receptions, science has transitioned from old-fashioned hidden peer review to open review on preprint servers and China is doing the largest experiment in infection control ever. And since tonight we have a public health emergency of international concern (PHEIC).

I’m very impressed by the actions in China, so far. It looks as if they very rapidly (within a month) recognized a cluster among patients with severe pneumonia in which no pathogen was detected (which happens in about 50% of these patients). Then, within a month they identified the cause of these infections, sequenced it and found the receptor for the virus, and immediately shared all information.

They also must have realized around that time that they were dealing with something very transmissible, which was subsequently confirmed by the many R_0 estimates. I think most agree that R_0 is somewhere between 2 and 3, but what does that mean for infection control?

R_0 is determined in the very early phase of the epidemic, when all subjects are susceptible to infection and when preventive measures have not been started. In that phase the virus is transmitted in a certain network, in this case a city of 10 Million, in which many contacts occur that could lead to successful transmission. When the outbreak is recognized, many mechanisms start that reduce transmission. In hospitals protective measures are taken and outside hospitals people start changing their behaviour. For instance, they avoid public transport and mass gatherings and stay at home. From then on it is better to name R an effective R value, which is of course less than R_0. Infection control aims to bring the effective R value below 1, and keep it there. The latter is not to be underestimated: if the original behaviour is resumed at a time that the virus is still circulating, the epidemic may rapidly speed up again. As may happen if the virus escapes to another – unprepared – network.

How can we see whether R declines? Just look at the epidemic curve (from which R is derived). As long as the number of newly infected subjects per day increases, R is >1. So, we want to reach the downhill slope of the epidemic curve, as fast as possible.

How to do that? Every epidemic dies out if the number of susceptibles declines. That is when a large part of the population has become immune, either through vaccination, surviving the infection (with immunity), or protective therapy (not really immune, but similar effect). The proportion of the population needed in this category is around 60-70% with this R_0, which is unlikely to happen soon for this virus. This leaves classical measures to reduce transmission with at least 60-70%.

China has the laudable ambition to do this. For that they have quarantined millions, which is a daunting task. Imagine Ursula van der Leyen (chair of the European commission) quaratining London, Paris, Amsterdam and Madrid.

Within the fence almost all transmission must be stopped. How long will this take? Well, if all transmission (100%) is prevented as of tomorrow, new infections will occur for at least a week, due to the incubation period. If interruption is less effective, say 70%, it will take (much) longer. The effectiveness to stop transmission outside hospitals requires isolation of infectious persons during their infectious period, which might start before being symptomatic. So, it may take some time before the outbreak is controlled sufficiently to open the fences without risks of the epidemic starting all over again. Question is how long they can keep the gates closed.

So, let’s carefully follow the epidemic curves, and hope that they are accurate and not compromised by reduced case notification due to shortness in testing capacity or collapses of hospital systems, which may give a false-positive reassurance. If China succeeds in controlling this outbreak, their efforts could be placed next to the Chinese Wall, as another Wonder of the World.

Update on 2019_nCoV, part 5

All quiet at the 2019_nCoV outbreak front, today (so far), it seems. As the world is now informed about the R_0 concept, we can start thinking how to best provide medical care to patients. The evidence base is empty and needs to be filled, preferably with unbiased estimates from randomized studies. Yet, if we had to define research questions now, and then start looking for partners and then draft a protocol, find funding and wait for IRB approval, the first patient-in might coincide with the last patient affected by the virus. So, for pandemic research you better be PREPARED.

The Platform for European Preparedness Against (Re-)emerging Epidemics (PREPARE) is an ”EU funded network for harmonized large-scale clinical research studies on infectious diseases (IDs), prepared to rapidly respond to any severe ID outbreak, providing real-time evidence for clinical management of patients and for informing public health responses.” The trick for better preparedness is to have ongoing large-scale studies in many different EU regions, that can capture a pandemic whenever it starts.

For this we started almost 6 years ago with what is now called “A Randomized, Embedded, Multifactorial, Adaptive Platform trial for Community-Acquired Pneumonia (REMAP-CAP).” This is a perpetual European adaptive RCT evaluating the effectiveness of several interventions (antibiotics, corticosteroids) in improving outcome in adults admitted to ICU with severe CAP, see and here.

The study design allows addition of study domains, such as for instance diagnostic testing or new treatments, embedded in the existing study protocol. Only a “simple” amendment is required as IRBs already approved the possibility of such adaptations, in case of a pandemic.

REMAP-CAP is enrolling patients in 52 centres in 13 countries in Europe, Australia/New Zeeland and North-America and more ICUs are ready to start. The study team is discussing – on a daily basis – what interventions to add. Corticosteroids and macrolide use for immunomodulation are already included.

Any studies in China? Yes, 2 protocols posted. “A randomized, open-label, blank-controlled trial for the efficacy and safety of lopinavir-ritonavir and interferon-alpha 2b in hospitalization patients with novel coronavirus infection”, see. The comparator is standard care and the target is 160 patients. The other is “a prospective comparative study for Xue-Bi-Jing injection in the treatment of pneumonia cause by novel coronavirus infection”, see. Xue-Bi-Jing is Chinese herbal medication, which will be compared to standard of care, and here the target is 200 patients per study arm. Rumor has it that recruitment has already been completed.

Any news on R_0 today? Not much. A new publication was embraced as good news, as it showed a dramatic reduction of R_0 in time. Indeed, it started with 8 on December 29th and then rapidly declined to a steady 2.5-3 from Januari 2nd till 18th……2019_nCoV.

Novel coronavirus outbreak, update part 4

As the weekend comes to an end, the burning question remains whether 2019_nCoV is transmitted by infected persons before clinical symptoms are obvious. Yesterday, I summarized the description of a Chinese family of seven that visited Wuhan and became infected without visiting the market, that is still being considered the “source” of the outbreak. Indeed, there were 2 family members without symptoms, and one of them (patient #5) not only had similar symptoms as family members on pulmonary CT, but also tested positive for the spike gene in the respiratory tract sample (thank you readers for pointing this out to me).

Today, newspapers reported that the observed incubation periods of the virus had ranged from 1 to 14 days and that the virus could be transmitted in this period. Apparently, this was communicated by the Chinese authorities, but a source of this data was not reported. Unless this is the source, see. I haven’t seen data, and it may still be based on rare events.

And the R_0 estimates keep coming in; all within the range from 2 to 5 (what I saw). But there is also warning against over-interpretation of these early-derived estimates. The population will respond to what is happening, trying to avoid infection, and that in itself may already reduce the effective R_0, as happened with SARS. Yet, in Wuhan the situation seems compatible with R_0 values as reported, a short generation time and transmission before symptoms occur.

Compared to Friday night, little – if any – news to think this will pass unnoticed.