Declining Cases, Impact of Reopenings: What Is the Current Status of the COVID-19 Pandemic, and What Can We Expect as School Starts Again?
The reopening of restaurants and cultural venues, the lifting of the curfew, and the end of the mandatory mask requirement outdoors… The COVID-19 pandemic is leveling off in France and is even declining significantly faster than models had projected last April. Mircea Sofonea, an associate professor of epidemiology and the evolution of infectious diseases at the University of Montpellier, explains why and provides an update on the outlook for the coming months.
Mircea T. Sofonea, University of Montpellier
The Conversation France: The epidemic declined more rapidly than the models had predicted in April. What happened?
Mircea Sofonea: It is important to note that mechanistic epidemiological models (that is, those explicitly based on explicit transmission dynamics) do not produce predictions, but rather projections. Formally, they are similar 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 of the effect 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 easing of lockdown restrictions 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 with 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 lockdown took full effect. From that point on, we were able to update the simulations.
For the past month, the outbreak has been following 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 factored into the model (typically, the effect 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 an 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 last 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 course of an epidemic, this number changes; it is then 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, in hindsight, it is now clear that the third lockdown led to a slower decline in the reproduction number than the first two lockdowns did, 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—complicate the estimation of the reproduction number shortly after a restriction measure is implemented or lifted.
First, there is a difference between reality and what we can glean 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, with results obtained within identical time frames. 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 critical care units (data that is more reliable than test results, especially when public holidays are involved).
Second, the methods used to calculate the reproduction number also employ 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 drawback is that this approach smooths out variations that indicate a recent change in trend—for example, 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 of data to improve the reliability of the models.
Nevertheless, parsimonious models still have a role to play, even a year and a half after the start of the pandemic. Indeed, while they may not be the most accurate in the short term, they make it easy to explore the full range of possibilities in the medium term—a time horizon 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 exponential impact on public health. This may require reassessing the timeline every two weeks, as estimates become more reliable.
TC: Looking back, do we have any idea what might explain why the 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 we gain more distance from these events, we will be able to use other statistical methods designed for studying the distant past to examine the course of the epidemic more precisely and assess the contribution of various factors. This work, for the time being, cannot be carried out amid the urgency of current demands.
One hypothesis 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 breaks began, the chains of transmission that had originated in schools no longer existed at that point, and the reproduction number dropped sharply, since many adults were also on break.
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 there was a more significant “school effect” 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 had an impact: the parks were open, and the weather was better than in the fall (though the milder weather alone cannot explain the decline). However, in a prospective model, it remains challenging to incorporate a variable such as the weather into projections, especially since forecasts beyond one 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 at around 0.8 today.
We are therefore clearly seeing an effect from the resumption of in-person schooling 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. In general, a region-by-region 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 slightly 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 with limited air circulation. The reopening of performance venues and indoor dining areas at restaurants 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 is worth noting 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 literature suggests, however, that the risk of transmission outdoors 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 Île-de-France and Hauts-de-France, 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 readily among young people and that, in addition, older adults have been vaccinated first, it is only natural to expect that younger people will become the reservoir for the virus’s circulation in the coming months.
The real question is what our goal is for the start of the school year: Do we want, for example, to do away with mask-wearing entirely—not only outdoors but also indoors? 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 for 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 15,000 more 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: should they maintain a dedicated ward, or distribute hospitalizations across various departments?
TC: To prevent this type of problem, is it absolutely necessary for vaccination rates 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 levels of vaccination coverage, particularly in large 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 far too low vaccination coverage, 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 (which originated in India)—whose spread in France is being closely monitored—the end of the pandemic in France will be determined by vaccination coverage. Vaccination uptake may decline as the situation in hospitals improves 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.