The desease affects everyone – but not everyone equally

The Corona virus is basically a danger to the general public, but figures suggest that certain groups among the (urban) population are more at risk than others. For instance, the age of a population seems to be a crucial aspect in this regard. This could therefore also be an indication of how the infection-figures and rates are distributed spatially in the urban context, considering “older” and “younger” places in the cities.

People beyond age 70, for example, are more than 40 times likely to die from Corona-infections than people under 40 worldwide. While this seems obvious, there is an indication of higher risks of infections among certain ethnic groups.

In the U.S., African Americans and Latinos are about three times more likely to contract the virus than white people. African Americans, Indians, Bangladeshis and Pakistanis are significantly more likely to die than whites in the UK. In an interview with the British „Guardian,“ an expert described the situation as follows: “We are all going through the same storm – but we are not in the same boat.”
This could be an indication that individual groups have a disadvantageous disposition with regard to spatial and density aspects within the socio-spatial fabric of cities.

Covid-19 distribution – a question of size?

If we take a step back, the search for explanations is complicated. The numerical material is thin and not particularly detailed. The amount of data points within Berlin’ s twelve districts is not sufficient for statistical analysis and full of “zeros”. It is also very likely that several factors play a role at the same time. Thus, it is difficult to establish a context, which data should be considered and which not.

Which data correlates with each other and ultimately provides information about spatial distribution patterns? Accordingly, it is only possible to look for reasons very roughly and with a great deal of caution. But there are probably factors that are more or less suitable as explanations.

In this regard, the population density factor is difficult to read. There is supposedly an obvious connection between population density and the number of infections. More people in less space is, of course, a conditions that makes it easier for the virus to jump from one person to the next. But, as already illustrated by the New York-example, there is growing indication that spatial density alone is not sufficient to adequately describe the distribution of infection-numbers. As spatial density in Berlin’ s districts does not explicitly correlate to the highest infection-numbers.

Initial studies also show that more people become ill on average in urban centers and that the phase of high infection-rates lasts longer. Here, different spatial aspects and set-ups can influence the occurrence of infections. Denser structures seem to promote the occurrence of infections through increased mobility and higher numbers of contacts.

This can be demonstrated (using the example of certain cities in Asia and Italy) by means of concrete measures that were implemented in the study areas. Restricting mobility led to lower incidence rates, but population density still seems to be an independent factor when looking at the incidence of infection between dense and less dense areas. High density areas seem to develop higher incidence rates over a longer period of time, while lower density areas show high rates at a given time. This suggests that single events are “responsible” for high incidences in less populated areas. Thus, in less densely populated areas (study areas in China – Prefectures), more spiky and shorter outbreaks confined to specific neighbourhoods were often observed, while in more densely populated prefectures, protracted outbreaks of larger definitive size could occur, spilling over to more connected neighbourhoods.

Adding to this, certain properties of infectious diseases (respiratory diseases) themselves, also lead to different outcomes in terms of density. For certain infectious diseases (of the respiratory tract), the temporal clustering of cases in an epidemic (i.e. the shortest period of time in which most cases are observed) varies with increasing indoor population and socioeconomic and climatic factors.

Berlin‘s Covid-19 distribution in districts – a question of size?

For the example of Berlin, higher numbers over time in more dense areas seem to be true, but not linear. The district of Reinickendorf for instance is much more sparsely populated than the district of Pankow and yet much more affected, although not a specific event for supporting higher numbers is known. Berlin’ s “incidence leader” Neukölln has only almost half as many people per district area as Friedrichshain-Kreuzberg and yet is clearly ahead. This is not to say that population density does not matter at all. But at least with district-level data available, there is no indication of a trend. However, this may also be due to the fact that there are, of course, already huge differences within the districts.

In terms of spatial qualities, income patterns and presumably availability to work and stay at home and therefore individual mobility patterns, residential aspects like internal residential density among many other indicators.

A question of age?

As we have glimpsed on earlier, different age groups are affected by the virus at different levels. Berlin is not a specifically old or young city, but the spatial distribution of different age-groups might be a hint.It seems likely that the younger people in a society tend to have a larger radius of mobility, which (as we mentioned earlier) might support the danger to suffer or pass an infection.

In addition, one often assumes a more carefree relationship with the virus, as well as certain age groups in Germany and thus also in Berlin (especially cities with a high number of students) tend to support types of housing (e.g.”Wohngemeinschaften”) that show a higher occupancy of apartments and thus a higher internal residential density. Another explanation for the pattern of nine infections is the age distribution in the city.

Age groups with the highest incidences here were, in descending order, the 15- to 19-year-olds, followed by the 20- to 24-year-olds and finally the 25- to 29-year-olds. Spatially, the districts of Mitte, Friedrichshain-Kreuzberg and Neukölln emerge as the districts with the highest incidences in these age groups. Here, too, it cannot be deduced that increased infection-figures are promoted by younger age groups, since the most severely affected districts show a different order. Here, too, we can conclude that there are more dynamic reasons for the incidence of infection.