Decline, impact of reopenings: what's the latest on the Covid-19 epidemic, and what to expect in the autumn?
Restaurants and cultural venues are reopening, curfews have been lifted, and it's no longer compulsory to wear masks outdoors... The Covid-19 epidemic is stalling in France, and is even declining significantly faster than the models predicted last April. Mircea Sofonea, Senior Lecturer in Epidemiology and Evolution of Infectious Diseases at the University of Montpellier, explains why, and takes stock of assumptions for the months ahead.
Mircea T. Sofonea, University of Montpellier
The Conversation France: The epidemic's decline was faster than the models had anticipated in April. What happened?
Mircea Sofonea: It's important to remember that mechanistic epidemiological models (i.e. those based explicitly on explicit transmission dynamics) don't produce forecasts, but projections. Formally, they are akin to a logical implication: if condition A is fulfilled, then situation B can be expected to occur.
If the working hypotheses are not in fact satisfied, the scenarios produced become obsolete and the simulations have to be updated. This problem arises mainly when the signal of the effect of a new measure is still incomplete in the hospital data on which our models have been based since the start of the pandemic.
Our first projections for the third containment were drawn up at the end of April. At that time, the effect of the third containment was limited compared with the first two, with an average of 10 people infecting 9, compared with 8 (or even 7) in the previous two. Our most optimistic scenario was therefore based on the assumption that this trend would not be reversed.
The pleasant surprise came a week later, at the beginning of May, when the containment effect was reinforced. From then on, we were able to update the simulations.
For the past month, the epidemic has been following the model's most optimistic trend, with no need for adjustment.
TC: Why didn't you imagine an "optimistic" scenario, with such a drop in the number of reproductions?
MS: The scientific approach is based on an important principle, the principle of parsimony or "Ockham's razor" (named after the Franciscan philosopher William of Ockham who formulated it): "the simplest sufficient hypotheses must be preferred".
In the absence of solid evidence to quantitatively anticipate any reproduction dynamics not already included in the model (typically, the effect of vaccination and immunization by infection), the minimal and methodologically neutral assumption is to extrapolate the epidemic dynamics based on the latest available data. In April, this extrapolation was itself supported by the analogy with the first two confinements. On both previous occasions, the estimated number of breeding had reached its minimum level some ten days after the introduction of the measures.
However, the kinetics of the third containment were different: for the first 10 days, breeding numbers stagnated at between 0.9 and 1, then dropped sharply to below 0.8, before rising again slightly in early May.
(Editor's note: The reproduction number is an estimate, over the last 14 days, of the average number of individuals contaminated by an infected person. The basic reproduction number (or R0) is used at the start of an epidemic, in the absence of transmission control measures and when the population is entirely susceptible to the virus. Over the course of the epidemic, this number changes: we speak of the effective or temporal reproduction number (Rt). If it is less than 1, the epidemic regresses; if it is greater than 1, it progresses).
Although, in retrospect, it now seems clear that the third confinement led to a slower decline in breeding numbers than the first two confinements, it was not possible to anticipate this at the end of April, just as there was no evidence in favor of a trend reversal during the confinement, a scenario which, for the same reasons, we did not envisage.
TC: Is the reliability of the models in question?
MS: No, but it's important to understand that there are a number of reasons, both methodological and biological, which make it difficult to estimate the number of reproductions shortly after the introduction or lifting of a restriction measure.
Firstly, there's a difference between reality and what can be measured. When restrictions are implemented (confinement, curfew...) the number of reproductions drops overnight in everyday life. However, this discontinuity is not reflected in the figures collected, simply because various biological parameters vary from one person to another (incubation time, onset of contagiousness, onset of symptoms, etc.).
The discontinuity caused by the restrictions might be visible if all these events were to occur homogeneously in everyone, assuming that screening were carried out at exactly the same time, with results obtained with identical delays. But this is not the case. In fact, there is a smoothing effect, and we only see the effects after two weeks, indirectly, on daily hospitalizations and admissions to critical care units (more reliable data than screenings, a fortiori in the presence of public holidays).
Secondly, methods for calculating the number of reproductions also use smoothing, in particular to avoid the "weekend effect": the 7-day rolling average makes it possible to spread the drop in case detection activity at weekends (when laboratories are closed) over the whole week, and thus no longer be affected by irrelevant oscillations. The disadvantage is that this approach buffers variations reflecting a recent change in trend, such as the introduction of restrictive measures.
We therefore need to continue our modeling research efforts. In particular, we need to refine transmission patterns and, above all, improve inference from weak signals. Similarly, the acquisition and cross-referencing of complementary data sets is a real challenge. In the UK, for example, epidemiological surveillance in schools, contact chain monitoring, random population screening and sequencing provide valuable sources for improving model reliability.
Nevertheless, parsimonious models still have a role to play, even a year and a half after the start of the pandemic. While they may not be the most accurate in the short term, they can easily explore the full range of possibilities in the medium term, a timeframe of particular interest to decision-makers.
As such, they are ideal for informing anticipation strategies, particularly in the context of an epidemic outbreak, where a delay can translate exponentially into health impact. This means re-evaluating the timetable every two weeks, as estimates are consolidated.
TC: Looking back, do you have any idea why this decline was more rapid than in previous confinements?
MS: Today, we still lack the hindsight (and time) to explain it causally. However, as we move further away from the events, we will be able to use other statistical methods dedicated to the study of the distant past, to study the course of the epidemic more precisely and assess the contribution of the various factors. For the time being, this work cannot be carried out in a hurry.
One hypothesis is that the closure of schools made a major contribution to the effect of confinement (for the first, the closure was general, and for the second, the school vacations had already begun). As the children were less symptomatic, it takes longer to see the effects of such a measure.
The stagnation in breeding numbers observed during the first week of the third containment could be explained by the fact that transmission chains were maintained among adults (in the workplace in particular). On the other hand, when the school vacations began, the transmission chains initiated in schools no longer existed, and breeding numbers fell sharply, as many adults were also on vacation.
TC: This result once again raises the question of the role of schools...
MS: Yes, and all the more so as the "vaccine" effect has been favorably integrated.
However, we can't really say that there was a greater "schools effect" during this containment than during the first, as conditions were different: presence of the alpha variant (formerly known as "British", more contagious than the historical strain, particularly in younger people), vaccination of people most at risk of complications.
In addition, other factors may have had an impact: the parks were open, the weather was better than in autumn (but milder weather alone cannot explain the drop). But in forward-looking modelling, it remains tricky to include a parameter like the weather in projections, even when forecasts beyond one week are uncertain.
TC: Where do things stand today? Are we seeing any effect from the reopenings?
MS: When the schools reopened on April 26 (and the half-gauge secondary school the following week), and then the terraces on May 19, we saw a slight upturn in the number of reproductions, which fell back to between 0.8 and 0.9, stabilizing at around 0.8 at present.
We can therefore observe an effect of the return to school and, more modestly, of the reopening of terraces, but nothing that would be likely to restart the epidemic, which remains in decline, albeit at a slightly slower pace than at the beginning of May. This suggests that the terraces could have been reopened earlier, particularly in areas where the incidence was already lower. Generally speaking, a territorial approach to the lifting of restrictions (and not just their implementation) can generate data that can help inform decision-making in less spared areas.
In addition, vaccination plays a key role, as the various scenarios show: even if the number of reproductions were to rise back to just over 1, the progress of vaccination could cause it to stabilize or fall back rapidly.
Going forward, we need to remain vigilant with regard to indoor gatherings with limited air renewal. The reopening of concert halls and restaurants is still too recent to assess its effect on the epidemic.
TC: Wearing a mask outdoors is no longer compulsory. What are your thoughts on this?
It should be remembered that the reopening of the terraces took place against a backdrop of recognition of the contribution of the airborne route (by aerosol) in the transmission of SARS-CoV-2 and the dynamics of the epidemic.
Recent literature suggests, however, that the risk of transmission outdoors is very low. It may nevertheless persist in situations of prolonged proximity without a draught, if a person is exposed for several minutes to the aerosol cloud produced by a contagious person without the cloud having had time to dissipate.
TC: What can we expect in autumn? What variants can we expect to see circulating, in what part of the population?
MS: Once again, the aim of mechanistic models is not to predict how many hospitalizations there will be within a given number of days, but rather to find out, for example, what level of slackening we can afford without fearing hospital saturation, or what the death potential of a fourth and final wave is.
At present, the alpha variant is in the majority (although the beta variant of South African origin now seems to be spreading more rapidly than the alpha variant in the Île-de-France and Hauts-de-France regions, perhaps because it escapes natural immunity to a greater extent, see our latest work published in the Journal of the European Centre for Disease Prevention and Control).
Given that the alpha variant spreads more widely among young people, and that older people have been vaccinated as a priority, we can naturally expect younger people to become the reservoir for virus circulation over the coming months.
The real question is: what are our ambitions for the new season? Do we want, for example, to do away with the wearing of masks altogether, not just outdoors, but indoors too? Do we want to reintroduce all cultural events, regardless of audience size? If so, the continuation of the vaccination campaign this summer will be crucial.
TC: What are the estimates in terms of potential deaths?
MS: At present, if 90% of the adult population were vaccinated with two doses at the start of the school year, we would still be under threat of 15,000 hospital deaths nationwide - the equivalent of a major seasonal flu epidemic. This is an order of magnitude, under current conditions (barring immune escape from a variant, which for the moment does not seem likely).
Are we prepared to accept a further 15,000 deaths? Given that we have already collectively accepted more than 110,000 deaths due to this pathology, there is little reason to imagine the contrary...
From a purely pragmatic point of view, the risk is that, if these 15,000 deaths were to occur close together, they could once again put a strain on certain local hospitals. For this reason, it's crucial to start preparing for the autumn now. In particular, hospitals will need to determine how best to organize themselves to manage residual Covid-19 activity: will they need to maintain a dedicated platform, or divide hospitalizations between departments?
TC: To prevent this type of problem, is it necessary for vaccination to be uniform throughout the country?
MS: Not necessarily. In a model initiated by Olivier Thomine based on OpenStreetMap data, not yet peer-reviewed, the spatial heterogeneity of the epidemic suggests that it's important to achieve high levels of vaccination coverage above all in large metropolises.
This argues in favor of differentiated territorial measures, including for vaccination coverage: having lower vaccination coverage in the Gers than in Paris, Lyon or Seine-Saint-Denis is not necessarily a problem. However, if the virus were to reach areas with far too low vaccination coverage, there would be a risk of local epidemics. This is what happened with measles in the Netherlands, for example.
Apart from the problem of the delta variant (of Indian origin), whose dynamics in France are being closely monitored, the end of the epidemic in France will be determined by vaccination coverage. Adherence may weaken as the hospital situation improves and the perceived infectious risk declines.
Epidemiological models do not yet incorporate human behavior, although this is an active area of research. In the meantime, modelling must produce scenarios based on a range of realistic, documented hypotheses, so that they cover a range of possibilities capable of anticipating epidemic risk.
Mircea T. Sofonea, Senior Lecturer in Epidemiology and Evolution of Infectious Diseases, MIVEGEC Laboratory, University of Montpellier
This article is republished from The Conversation under a Creative Commons license. Read theoriginal article.