Neighborhoods affect covid development in Barcelona

Density, public transport, and income have an impact on covid-19 transmission

3 min
A woman walking down an empty street in Sarriá

Population density, per capita income, and even public transport and the number of schools end up being factors that determine the transmission of the coronavirus. This is confirmed by a study by the Universitat Rovira i Virgili (URV), Tarragona, which has analyzed the impact the virus has had on 73 neighborhoods in the city of Barcelona during the first and second waves of the pandemic, and confirms the social impact of a health crisis like this, when Catalonia has already exceeded 19,000 deaths and is close to reaching half a million infections.

The study has been published in the Journal of Public Health based on data collected by the Barcelona Public Health Agency in two different periods: the first, between 26 February and 15 July 2020, and the second between 16 July and 16 October, when a total of 41,606 people were infected with covid-19.

The researchers conclude that the contagions followed a very clear territorial pattern in a city with very different neighborhoods in terms of socioeconomic context. "There are certain characteristics that show a persistence in its effects regardless of the wave, such as more population density increases cases, and more income level reduces them", Josep Maria Arauzo-Carod, professor of economics at the URV and one of the authors of the study, says in statements to the Efe agency. The coexistence of many neighbours within a few kilometres logically increases mixing and personal contact, an essential factor for the transmission of the respiratory coronavirus.

"The variable related to economic income is stable in both waves", the authors write, who note that the districts of Nou Barris and Sant Andreu suffer more impact than those of the northwest, Sarria-Sant Gervasi and Les Corts, with the highest incomes in the capital. The wealthier have the resources to protect themselves against potential contagion, as they can avoid going out on the streets both to shop and to work, as they can work from home.

Getting out of lockdown

In contrast, the population density factor is key to explaining the behaviour of the virus in the second wave, when restrictive measures were relaxed and mobility was allowed. This is especially reflected in the increase of infections in the districts of Besòs, Maresme, La Verneda and La Pau, as well as Sant Antoni and Poble Sec. Following this same reasoning of interactions, it is explained that from the opening of schools the incidence increased, especially in Ciutat Vella, but not so much by infections in schools but by the activities and habits that take place after school hours.

The authors explain that there is an evident difference in collective behaviour that can also explain how the epidemic evolves in each neighbourhood. For example, areas with a younger population were those with a considerable increase in positive cases due to the intensity of social interactions during the summer. On the contrary, during the weeks of lockdown, it was the older areas that were most affected because the restrictions imposed reduced mobility and contacts of young people and children to a minimum, due to the closure of schools, and work and leisure restrictions for the adult population. Thus, the authors write that "the opening of educational facilities enhance the growth of the pandemic, even if these institutions apply all the necessary hygiene measures".

The coronavirus benefits from good public transport connections, say the authors, who have found that with more or less normalized work and school activities, areas with fewer bus or subway stops have fewer contagions.

From the conclusions of the study, the authors point out that with such heterogeneous territories, the best way to stop the pandemic is to apply equally diverse measures, adapted to each specificity in order to attack each cause.