Between February and July 2020, the uneven circulation of COVID-19 across European regions raised immediate questions regarding the socioeconomic, environmental, financial and demographic dimensions of the pandemic. Why have some areas been hit harder than others? How can regional variations be explained? Is it possible to identify links between the spread of the disease and territorial characteristics that are likely to influence it?
The aim of the ESPON study 'Geography of the COVID-19 out break and first policy answers in European regions and cities' was to provide the first regional analysis of the pandemic at the European level. In this study we used fatalities and hospitalisations as proxies to overcome the limitations of infection rates, which are too dependent on national testing policies.
In highlighting a possible link between mortality and territorial characteristics, it should be noted that the spread and intensity of infectious diseases (e.g. Ebola, SARS, MERS and COVID-19) are determined by the interaction between several sociospatial processes.
One of the strong hypotheses that emerged from the first set of empirical evidence and hypotheses is that the first wave of the pandemic occurred with the first places being hit by 'random events', which then triggered cumulative local processes. Eventually, from February to July 2020, the spread of the virus seemed to remain constant, no matter how strict the lockdown measures were.
The ESPON study confirms these first assumptions,describing a three-step kinetic process for the COVID-19 pandemic.
The unexpected kinetics of the pandemic
In the early phase of the pandemic all cases had a direct link to China. The virus clearly took advantage of global exchanges and spread across the networks of the largest global cities. By exploiting the mobility networks at the heart of economic development and tourism, COVID-19 has flourished in the densest, most productive and most sociable spaces.
The first cases identified in January 2020 were returnees from China to France, followed by returnees from China to Germany, Finland, Italy, the United Kingdom and Sweden. The fact that northern Italy was the first European region to be particularly affected was not surprising, given the importance of the Chinese diaspora in the country and the movements of people that occur between Milan and China. Milan Malpensa Airport, the country's second largest airport, has become a FedEx hub in southern Europe and an important freight airport that is well connected to China, the world's leading exporter. Milan is also directly connected to China by rail freight, following the opening of the first direct link between Chengdu and Milan on 12 February 2019, which contributed to the structuring of the new Silk Road.
This could explain why Lombardy was hit earlier than other regions by COVID-19. Countries that were hit later by the virus managed to gain some control over the first clusters that emerged following the detection of these first cases linked to China.
"The ESPON study confirms these first assumptions, describing a three-step kinetic process for the COVID-19 pandemic.
A totally unexpectedsecond phase -seen as a game changer - then arose and shaped the peculiar regional geography of the first wave. In addition to global networks, COVID-19 found favourable conditions for its circulation through super-spreading events, which accelerated the spread of the virus, for example a religious conference in eastern France, a football game in northern Italy, carnival festivities in western Germany and an event at a night-life venue in a ski resort in the Austrian Alps.
Some resulted in major regional outbreaks that explained most of the uneven distribution of fatalities between regions in some countries. For instance, a religious event bringing together 2,500 people over one week in February 2020 was the cause of the first massive outbreak of COVID-19 in France. By 1 April 2020, one third of deaths from COVID-19 in the country had originated in this region. Four months later, these deaths still amounted to 22 %.
All of these super-spreading events contributed to widespread diffusion of the virus through interregional mobility. The pandemic spread to many different regions, with its intensity also being dependent on the numbers of returnees from tourist and business trips abroad.
Northern Italy was an important source of infections because, as well as experiencing a severe COVID-19 outbreak that began in mid-February, it is both one of the most connected regional economies in Europe and a very popular winter tourist destination. Diffusion of the virus over large distances through the mobility of societies caused a huge scaling of the outbreak process in many European regions, strongly affecting the largest metropolitan areas in particular.
It was at this point that several regions in Scandinavia, the Benelux countries and the United Kingdom began to be severely affected (see the article 'Impact of COVID-19 in European cities and metropolitan areas' by Alfredo Corbalan, page 26). These examples of relocation diffusion demonstrate the importance of network logics for the regional development of the outbreak during this first phase.
As public authorities started to implement lockdowns and close borders all over Europe, network-based scaling processes suddenly stopped. The big picture of the regional geography of the outbreak was frozen, and a third phase characterised by proximity logic became the most prominent.
As COVID-19 spread around large cities and along regional transport routes, it did so with a lower intensity. Overall, rural remote regions were less taffected. With the exception of some Romanian and Polish regions, most of the Baltic countries and eastern and south-eastern Europe were spared.
However, the most densely populated regions in western and central Europe were severely hit; outbreaks in closed environments such as care homes were an important feature of the geography of the outbreak process almost everywhere in Europe.
Although they were introduced in a temporal sequence, these three phases should not be looked at separately but rather should be viewed as overlapping. The interaction between network logics and proximity logics and the fact that super-spreading events act as accelerators should be considered together to reveal the pattern of this first wave of COVID-19 between February and August 2020.
"A low population density is not necessarily a protective factor for rural areas.
Against this backdrop, an objective of the ESPON study was also to conduct initial enquiries to understand the territorial features that could somehow explain how the virus was circulating once the above processes had been triggered.
Can regional differences be explained?
The study has established that regional fatality rates relate to a multifactorial causality system. The pandemic is currently revealing the spectacular vulnerability of large cities, with the virus paralysing global functioning within a few weeks and immobilising territories that are under confinement.
The analysis shows that urban areas have been the most severely affected. These are areas where the level of sociability is high and therefore the risk of transmission of the virus is higher than average (see also the article 'COVID-19 challenge in Lombardia' by Luisa Pedrazzini, page 28).
However, population density is not always able to explain the numbers of deaths. For example, Germany, with an average population density of 234 inhabitants/km², has been much less affected than Spain, which has a relatively low average population density of 93 inhabitants/km² (EU average: 109 inhabitants/km²). Looking at the regional level, a large part of Germany, in the west, has had a relatively low mortality rate despite the high population density.
This is also the case inside Spain; all the regions around Greater Madrid that were heavily affected have low population densities (less than 50 inhabitants/km²), even though they are undoubtedly in close contact with the capital region. Moreover, a low population density is not necessarily a protective factor for rural areas. On the contrary, it seems to increase the mortality rate once the virus has managed to spread to these areas (because of the poor healthcare facilities in these areas).
The study also highlights that regions with a higher proportion of older persons were not necessarily the most severely affected. For example, at the national level, 23% of the Italian population is aged over 65 years, which is comparable to the rate in Germany, although the latter has been much less affected by the pandemic. On a finer scale, the composition of households is important and may have played a role: the cohabitation of different generations and common living structures have exposed older people to multiple sources of contagion.
Another important takeaway from the ESPON study is the fact thatregional economic characteristics cannot explain the cumulative mortality rates.In terms of both gross domestic product per capita and poverty rate, no significant relationship with mortality appears. A future analysis on a finer scale (looking within urban districts of metropolitan regions) is necessary to explain how socioeconomic inequalities result in different fatality rates.
As a first reflection on this first wave, it can be concluded that the geographical approach adopted in this ESPON study demonstrates that globalisation does not abolish either history or memory of facts(thinking about the reception of and reactions to the pandemic) on the one hand, or space and distances (thinking about the temporalities and modalities of diffusion) on the other hand. From this point of view, the COVID-19 pandemic is just another way of revealing the persistent territorial inequalities between European regions.