Population density : not a primary factor in Covid-19

From a demo­gra­phic stand­point, what can we say about the Covid-19 crisis ?

In my book Serons-nous sub­mer­gés ?, which was publi­shed in Octo­ber 2020, I stu­died the way the first wave of the pan­de­mic unfol­ded, day after day, in four coun­tries – France, Swit­zer­land, Ita­ly and Spain. I stu­died them at the level of regions, coun­ties and pro­vinces. The results sho­wed that geo­gra­phic – rather than social – fac­tors were key for dyna­mics of the epi­de­mic. Cer­tain­ly, as many stu­dies have shown, immi­grants and low-income people have a higher risk of dying from Covid-19 than the rest of the popu­la­tion. Not because of where they’re from or how much they earn, howe­ver. Rather, it is because of their proxi­mi­ty to the virus. These popu­la­tions work in hos­pi­tals, at super­mar­ket che­ckouts, as deli­ve­ry per­sons or taxi dri­vers. That is why they are more impac­ted. The virus can­not dis­tin­guish bet­ween an immi­grant and a non-immi­grant, nor can it tell how much money you make. Rather, it goes straight to the nea­rest per­son, and we can learn a lot more about who the nea­rest per­son will be through geography.

We often think of Covid-19 as an “urban virus”. Howe­ver, your stu­dy found that popu­la­tion den­si­ty was not one of the key fac­tors in its spread. Why is that ?

When you com­pare Coro­na­vi­rus deaths bet­ween 1^st March and 15^th May 2020 with the den­si­ty of French dépar­te­ments, there are huge dif­fe­rences. Over that per­iod, the mor­ta­li­ty rate in Ter­ri­toire de Bel­fort (1.18 per 1000 resi­dents) was 170 times higher than that of Ariège (.007 per 1,000). The contrast bet­ween a map of mor­ta­li­ty rate and a map of popu­la­tion den­si­ty shows no cor­re­la­tion (see below). 

Den­si­ty is not a fac­tor in the large-scale spread of the virus. What mat­ters is the loca­tion of clus­ters, which are ini­tial­ly lin­ked to just one per­son. The more people are conta­mi­na­ted before the infec­ted per­son rea­lises, the har­der it is to contain the epi­de­mic. This is what hap­pe­ned with the second wave, but the dif­fe­rences in mor­ta­li­ty remain consi­de­ra­bly dif­ferent – some­times as much as ten­fold higher. And we see that “patient zeros” appear in both the coun­try­side and the city inclu­ding large towns such as Mul­house and Ajac­cio, but also smal­ler ones like Auray, Creil or even a vil­lage in Savoie, Les Contamines-Montjoie. 

Hence, while den­si­ty mat­ters as the epi­de­mic spreads, the ini­tial impact is mini­mal. The only thing that we can say is that patient zero is a tra­vel­ler. So, they often appear near big inter­na­tio­nal hubs (Gene­va, Milan, Rois­sy Air­port near Paris, New York, etc.). But they also tra­vel out of the city, which is where the clus­ter deve­lops, i.e. Cré­py-en-Valois, La Bas­tide-Mont­joie, Ber­game. This is actual­ly what hap­pe­ned at the start of the AIDS epidemic. 

So to control the spread one must first control people’s move­ments – hence the lock­down. After expo­nen­tial growth of cases in the first clus­ters (Mul­house, Auray, Milan, etc.), the epi­de­mic in the first wave was contai­ned. For example, it prac­ti­cal­ly didn’t get into the Loire at all, nor into Anda­lu­sia or Sou­thern Ita­ly. It also explains why deaths were thir­ty times higher in Milan than in Naples. It’s a remin­der of how, during the last out­break of the bubo­nic plague in France (Mar­seille, 1721), lines of sol­diers were deployed to prevent the epi­de­mic from sprea­ding beyond the Pro­vence region.

Let’s go back to social cri­te­ria. Which did you select for your study ?

In each of the coun­tries inclu­ded in our stu­dy, figures for Covid-19 deaths were com­pa­red with four indi­ca­tors : den­si­ty, pover­ty, the pro­por­tion of immi­grants in the popu­la­tion, and the pro­por­tion of people over 70 years old. The geo­gra­phic dis­tri­bu­tion of these four fac­tors gave us no indi­ca­tion of which seg­ment of the popu­la­tion would be impac­ted. The maps speak volumes. This is due to the fact that the first wave was contai­ned in the four coun­tries studied.

What about the second wave ?

Para­doxi­cal­ly, the second wave ori­gi­na­ted at the end of the first lock­down. The num­ber of dai­ly cases was very low at the end of June. But people in the 15–49 age bra­cket accoun­ted for two thirds of new cases. Many of them pas­sed by unde­tec­ted, as they were asymp­to­ma­tic. With people tra­vel­ling for the sum­mer holi­days, the virus spread all over France. Older people, par­ti­cu­lar­ly grand­pa­rents, were also infected.

As such, in Octo­ber France found itself with a large num­ber of clus­ters. With the excep­tion of Mayenne, these could not be contai­ned as seen in Ajac­cio, Auray and Les Conta­mines-Mont­joie. As a result, the epi­de­mic spread pret­ty much eve­ryw­here, as it had done in the two big clus­ters in the first wave in Creil and Mulhouse.

Because of more wides­pread safe­ty mea­sures and impro­ved heal­th­care, the virus did not spread very qui­ck­ly. The spread of Covid-19 across almost the entire coun­try has pro­du­ced new social dif­fe­rences connec­ted to what hap­pe­ned during the first wave – cer­tain groups are more care­ful than others, as shown by debates about mask-wea­ring. Never­the­less, regio­nal dif­fe­rences are not insi­gni­fi­cant – bet­ween coas­tal Brit­ta­ny and the Lyon region, the rate of cases and the mor­ta­li­ty rate varies by a fac­tor of ten.

Just as we saw in the first wave, the second lock­down (pre­ven­ting people from tra­vel­ling) will pro­ba­bly main­tain these dif­fe­rences, once the wave has been stop­ped. Like other contact epi­de­mics throu­ghout his­to­ry, control­ling people’s mobi­li­ty remains an essen­tial fac­tor for control­ling the epi­de­mic. We had bet­ter remem­ber that if we want to prevent a third wave.

Interview by Clément Boulle