Declining cases, the impact of reopening: What is the current status of the COVID-19 pandemic, and what can we expect as schools reopen?
The reopening of restaurants and cultural venues, the lifting of the curfew, and the end of the mandatory mask requirement outdoors… The Covid-19 epidemic is leveling off in France and is even declining significantly faster than models projected last April. Mircea Sofonea, associate professor of epidemiology and the evolution of infectious diseases at the University of Montpellier, explains why and provides an update on the projections for the coming months.
Mircea T. Sofonea, University of Montpellier
The Conversation France: The epidemic declined more rapidly than models had predicted in April. What happened?
Mircea Sofonea: It is important to remember that mechanistic epidemiological models (i.e., those explicitly based on explicit transmission dynamics) do not produce predictions, but rather projections. Formally, they are akin to a logical implication: if condition A is met, then we can expect situation B to occur.
If the working assumptions are not actually met, the resulting scenarios become obsolete and the simulations must be updated. This problem arises primarily when the signal reflecting the impact of a new measure is still incomplete in the hospital data, on which our models have relied since the start of the pandemic.
Our initial projections regarding the third lockdown were developed in late April. At that time, the impact of the third lockdown was limited compared to the first two, since, on average, 10 people were infecting 9 others, compared to 8 (or even 7) during the previous two lockdowns. Our most optimistic scenario was therefore based on the assumption that this trend would not reverse.
The pleasant surprise came a week later, in early May, as the impact of the lockdown intensified. From that point on, we were able to update the simulations.
For the past month, the outbreak has followed the most optimistic scenario in the model, without requiring any adjustments.
TC: Why didn’t you come up with an “optimistic” scenario, given such a sharp drop in the reproduction number?
MS: The scientific method 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 should be preferred.”
In the absence of solid evidence that would allow for a quantitative prediction of changes in the reproduction number not already accounted for in the model (typically, the effects of vaccination and natural immunity), the minimal and methodologically neutral assumption is to extrapolate the epidemic’s trajectory based on the most recent data. In April, this extrapolation was itself supported by the analogy with the first two lockdowns. On both previous occasions, the estimated reproduction number had reached its lowest level about ten days after the measures were implemented.
However, the trajectory of the third lockdown was different: during the first 10 days, the reproduction number remained stable between 0.9 and 1, then dropped sharply to below 0.8, before rising slightly in early May.
(Editor’s note: The reproduction number is an estimate, based on the past 14 days, of the average number of people infected by a single infected individual. The basic reproduction number (or R0) is used at the start of an epidemic, in the absence of measures to control transmission and when the population is fully susceptible to the virus. During the epidemic, this number changes: it is referred to as the effective or temporal reproduction number (Rt). If it is less than 1, the epidemic declines; if it is greater than 1, it spreads.)
While it is now clear in hindsight that the third lockdown led to a slower decline in the reproduction number than the first two lockdowns, it was not possible to anticipate this at the end of April, just as there was no evidence to suggest that the trend would reverse during the lockdown—a scenario that, for the same reasons, we did not consider.
TC: Is the reliability of the models being called into question?
MS: No, but it’s important to understand that several factors—both methodological and biological—make it difficult to estimate the reproduction number shortly after a restriction measure is implemented or lifted.
First, there is a difference between reality and what we can infer from the data. When restrictions are implemented (lockdowns, curfews, etc.), the reproduction number drops overnight in everyday life. However, this sudden change is not reflected in the collected data, simply because various biological parameters vary from person to person (incubation period, onset of contagiousness, appearance of symptoms, etc.).
The discontinuity caused by the restrictions might be visible if all these events occurred uniformly across the entire population, assuming that testing took place at exactly the same time and results were obtained with identical delays. But that is not the case. In reality, there is a smoothing effect, and the effects are only visible after two weeks, indirectly, in daily hospitalizations and admissions to intensive care units (data that is more reliable than test results, especially when public holidays are involved).
Second, methods for calculating the reproduction number also use smoothing techniques, particularly to account for the “weekend effect”: the 7-day moving average helps spread the drop in case detection activity over the weekend (when laboratories are closed) across the entire week, thereby eliminating the impact of irrelevant fluctuations. The downside is that this approach smooths out fluctuations that indicate a recent shift in trend, such as when restrictive measures are implemented.
It is therefore necessary to continue research efforts in modeling. In particular, we will need to refine transmission patterns and, above all, improve inference from weak signals. Similarly, the collection and integration of complementary datasets present a significant challenge. In the United Kingdom, for example, epidemiological surveillance in schools, contact tracing, random population testing, and sequencing provide valuable sources for improving the reliability of models.
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 make it easy to explore all possible outcomes in the medium term—a timeframe of particular interest to decision-makers.
As such, they are well-suited to informing proactive strategies, particularly in the context of an epidemic outbreak where even a slight delay can have an exponentially greater impact on public health. This may require revising the timeline every two weeks as estimates become more reliable.
TC: Looking back, do we have any idea why this decline was faster than during previous lockdowns?
MS: At present, we still lack the necessary perspective (and time) to explain this in causal terms. However, as more time passes, we will be able to use other statistical methods designed for studying the distant past to analyze the course of the epidemic more precisely and assess the contribution of various factors. This is work that, for the time being, cannot be accommodated amid the urgency of current demands.
One theory is that school closures played a major role in the impact of the lockdown (in the first case, closures were widespread, and in the second, school vacations had already begun). However, since children tend to show fewer symptoms, it takes longer to see the effects of such a measure.
The stagnation in the reproduction number observed during the first week of the third lockdown could be explained by the fact that transmission chains persisted among adults (particularly in the workplace). However, once school vacations began, the transmission chains that had started in schools no longer existed at that point, and the reproduction number dropped sharply, since many adults were also on vacation.
TC: This result once again raises the question of the role of schools…
MS: Yes, especially since the “vaccine” effect has been well received.
However, it cannot really be said that the “school effect” was more pronounced during this lockdown than during the first one, as the circumstances were different: the presence of the Alpha variant (formerly known as the “British” variant, which is more contagious than the original strain, particularly among younger people), and the vaccination of those most at risk of complications.
In addition, other factors may have played a role: the parks were open, and the weather was better than in the fall (though milder weather alone cannot explain the decline). However, in a predictive model, it remains challenging to incorporate a variable such as the weather into projections, especially since forecasts beyond a week are uncertain.
TC: Where do we stand today? Are we seeing any effects from the reopening?
MS: When schools reopened on April 26 (with high schools operating at half capacity the following week), followed by the reopening of outdoor dining areas on May 19, we saw a slight increase in the reproduction number, which rose back to between 0.8 and 0.9 before stabilizing around 0.8 today.
We can therefore clearly observe an impact from the return to school and, to a lesser extent, from the reopening of outdoor dining areas, but nothing that would be likely to cause the epidemic to resurge; the epidemic continues to decline, even if the rate of decline is slightly slower than it was in early May. This suggests that outdoor dining areas could have been reopened earlier, particularly in regions where the incidence rate was already lower. Generally speaking, a regionalized approach to lifting restrictions (and not just to implementing them) helps generate data that can inform decision-making for regions that have been harder hit.
Furthermore, vaccination plays a key role, as the various scenarios show: even if the reproduction number were to rise again to just above 1, the progress of the vaccination campaign could cause it to stabilize or decline rapidly.
Moving forward, we must remain vigilant regarding indoor gatherings where air circulation is limited. The reopening of performance venues and indoor dining areas is still too recent to assess their impact on the epidemic.
TC: Wearing a mask outdoors is no longer mandatory. What do you think about that?
It should be noted that the reopening of outdoor dining areas took place against the backdrop of growing recognition of the role of airborne transmission (via aerosols) in the spread of SARS-CoV-2 and the dynamics of the epidemic.
Recent research suggests, however, that the risk of transmission in outdoor settings is very low. Nevertheless, the risk may persist in situations involving prolonged close contact without airflow, if a person is exposed for several minutes to the aerosol cloud produced by an infectious person before it has had time to dissipate.
TC: What can we expect this fall? Which variants can we expect to see circulating, and among which segments of the population?
MS: Once again, the goal of mechanistic models is not to predict how many hospitalizations there will be within a given number of days, but rather to determine, for example, how much we can ease restrictions without risking hospital system overload, or what the potential death toll of a fourth and final wave might be.
Currently, the Alpha variant is the dominant strain (although the Beta variant, which originated in South Africa, now appears to be spreading more rapidly than the Alpha variant in the Île-de-France and Hauts-de-France regions, perhaps because it may be more resistant to natural immunity; see our latest study published in the journal of the European Centre for Disease Prevention and Control).
Given that the Alpha variant spreads more easily among younger people and that older adults have been vaccinated first, it is only natural to expect that younger people will become the primary source of virus transmission in the coming months.
The real question is what goals we set for the start of the school year: do we want, for example, to do away with mask-wearing entirely—not only outdoors, but indoors as well? To allow all cultural events to resume, regardless of audience size? In that case, continuing the vaccination campaign this summer will be crucial.
TC: What are the estimates regarding potential deaths?
MS: Currently, even if 90% of the adult population were fully vaccinated by the start of the school year, we would still face the threat of 15,000 hospital deaths nationwide—the equivalent of a major seasonal flu outbreak. This is an order of magnitude, under current conditions (barring immune escape by a variant, which does not appear to be a concern at this time).
Are we prepared to accept another 15,000 deaths? Given that we have already collectively accepted more than 110,000 deaths from this disease, there is little reason to imagine otherwise…
From a purely pragmatic standpoint, the risk is that if these 15,000 deaths were to occur in quick succession, they could once again strain certain local hospitals. For this reason, it is crucial to start preparing for the fall season now. In particular, hospitals will need to determine how to organize themselves to best manage the remaining COVID-19 caseload: will it be necessary to maintain a dedicated ward or distribute hospitalizations across departments?
TC: To prevent this kind of problem, does vaccination coverage have to be uniform across the entire country?
MS: Not necessarily. In a model developed by Olivier Thomine based on OpenStreetMap data—which has not yet been peer-reviewed—the spatial heterogeneity of the epidemic suggests that it is important to achieve high vaccination coverage levels, particularly in major metropolitan areas.
This argues for differentiated, region-specific measures, including 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 regions with vaccination coverage that is far too low, there would be a risk of local outbreaks. This is what was observed, for example, in the Netherlands with measles.
In addition to the issue of the Delta variant (originating in India), whose spread in France is being closely monitored, the end of the pandemic in France will depend on vaccination coverage. Public support for vaccination may wane as hospital conditions improve and the perceived risk of infection decreases.
Epidemiological models do not yet incorporate human behavior, although this is an active area of research. In the meantime, models must generate scenarios based on a range of realistic and well-documented assumptions, so that they cover a spectrum of possibilities capable of anticipating the risk of an epidemic.
Mircea T. Sofonea, Associate Professor of Epidemiology and the Evolution of Infectious Diseases, MIVEGEC Laboratory, University of Montpellier
This article is republished from The Conversation under a Creative Commons license. Readthe original article.