Why are projections on Covid waves increasingly complex?

Science – It started in early June, and we can say on Tuesday, July 26 that the seventh wave of COVID-19 in France climax. On the side of positive cases like hospitalization, the indicators go down, as you can see in drawing below.

Good news (if the trend continues) because this Peak related to the BA.5 . variable It is only equal to that associated with BA.2, far from the first “Omicron wave”, caused by BA.1. Good news also because, even if the situation in Portugal and South Africa makes it possible not to worry too much, it was the first wave that the Scientific Council (and thus the government) did not have a reliable model.

Currently, the Pasteur Institute also does not have a model for future waves that will undoubtedly arrive once summer is over and our immunity will slowly decline.

This did not prevent Scientific Council, in her latest opinion released on July 19 and detailed in a press conference, to work on three scenarios for fall. But these paths are very general: the return of existing variants, a variant descended from Omicron, or worse, an entirely different variant and perhaps even more dangerous.

Models get too complicated

But why does the Scientific Council no longer have expectations that allow it to make predictions? “This BA.5 wave was the first we didn’t have a model, because over the past two years we’ve been starting with simple models, which have gradually become more complex to integrate the impact on the epidemic from variants and vaccines as well as reduced immunity,” explains Simon Cochems, modeller at Pasteur Institute and member of the Scientific Council. “These highly complex models are uncertain today.”

To make predictions on Covid-19 curves (More details in this interview with Simon Cauchemez from 2020), hypotheses are formulated about the virus (extent of infection, duration of infection, severity, etc.), and on the target population (number of contaminated people, number of contacts at risk, age, etc.).

At the start of the pandemic, things were (unfortunately) simple: Almost anyone could catch Covid-19. Even after the first wave, which affected only 5% of the population. There were only two ways to reach the peak. Either allow the virus to spread until there aren’t enough people to infect (leaving tens or even hundreds of thousands of people to die). Either you take measures that limit our vulnerable contacts in order to break the chains of transmission of the coronavirus.

But since then, things have evolved in many ways. First, thanks to vaccines, which have given us very great protection against dangerous forms and, in a lighter way, against infection. There were also variants to be incorporated into the models. Did this new set of mutations make the virus more contagious? less fierce? Able to escape from the vaccine? For a previous infection?

“We know very little about cross-immunity between variants”

All these parameters made the predictions more complex, but the modelers still saw things clearly. Until then, you can simplify by putting people in boxes. Samuel Allison, director of research at CNRS, who specializes in infectious disease modeling, explains people vaccinated with one dose, two doses, a booster, and those with innate immunity. “But the Omicron waves blew up the categories.”

With the arrival of the highly contagious variant Omicron, most Western countries, widely vaccinated, tired of frequent confinement and unable to develop non-coercive curb measures, chose to let the epidemic go unpunished. By doing so, we accepted a very large wave of cases, from hospitalization, but with much lower losses than the previous variants on the unvaccinated population. There was also A vague and ridiculous hope that this will be the last wavewhich causes “herd immunity”, which prevents the spread of the coronavirus.

But the reality, once again, quickly realized the meager hopes. This natural immunity, as we already knew, does not last forever. Decreases and disappears over time (even if against severe forms it seems to stabilize after three doses or three infections).

This is a big part of the problem. “While vaccine immunity is easy to control and monitor, natural immunity is less well known,” explains Samuel Allison. Especially with the multiplication of variables and situations. In which box is the vaccinated person placed, after 3 months of contamination, then who registered in January? How does his immunity compare to someone infected in 2020, vaccinated twice in 2021, and then re-infected with BA.1 in January 2022? Or by BA.2 in March?

“We know very little about cross-immunity between variants, for example, we saw that BA.5 can circumvent part of the immunity caused by infection with BA.1,” explains Samuel Allison. Therefore, it becomes very difficult to manage all these boxes so that the epidemiological model can provide accurate forecasts without risking getting lost completely.

Simplify without distortion

However, it is always necessary to anticipate as much as possible. “The epidemic is not over yet. We are facing a virus with a genetic evolution that is difficult to predict,” warned Jean-François Delfraissy, President of the Scientific Council, in preparation for the presentation of his last opinion.

But can we even adapt the calculations to this new situation? “Today’s models are very complex and therefore unstable. A compromise must be found with more stingy models, considering these different immune patterns. It is a work in progress,” Simon Cochems explains.

in Article – Commodity Previously published on 15 June, Samuel Allison and two colleagues, previously published on June 15, tested a new concept in an attempt to account for declining immunity. “The idea is to include in the model how long individuals have been in a particular condition, for example how long it has been since their last vaccine dose,” he explains. “One finding is that even in the absence of a novel variable, large annual waves associated with winter and a (limited) decrease in immunity can be observed.”

Surprisingly, scenarios in which the entire population is vaccinated at the same time each fall result in a more pronounced peak than if the booster was offered only to the elderly and frail (even if more were vaccinated, fewer deaths). The reason put forward by the researchers: by vaccinating everyone at the same time, the level of immunity is in sync. Obviously we suddenly have many people who have become susceptible to infection again. Another lesson from the study, the researcher notes: “In addition, so-called non-pharmaceutical interventions (improving air quality, wearing a mask, etc.) can have similar efficacy to annual vaccination campaigns. Ultimately, the best efficacy is obtained by combining between these interventions and vaccine boosters.”

Obviously, this kind of general projection has its limits. “The longer we show ourselves off in the long run, the better the model becomes,” says Samuel Allison. However, what the health authorities want are “quantitative” projections. To put it simply, let us say that the qualitative model attempts to visualize the general direction of the Covid-19 curve in the long run under various assumptions. The quantitative model will attempt to predict how many people are injured or kept in hospital. “But once we get past the month, these quantum models become accurate in light of many unknowns, and we still have to explore different scenarios.”

In conclusion, we must remember that it is clear that we are not powerless to monitor this epidemic. “We must be vigilant about upcoming phenomena, because it is very difficult to determine the dates and the size of the peaks. Today, we are monitoring what is happening with our neighbors and it is very useful”, remembers Arnaud Fontanet, an epidemiologist and member of the Scientific Council, during the press conference. However, the dominant form should not appear in the future in France. “If we are on the front line, it will be difficult, and we will have to take into account the possibility of a somewhat more turbulent emergence.”

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