Reduction of Mobility During the COVID-19 Pandemic in Germany According to Age, Sex, and Federal State

It is assumed that there is a strong link between mobility and COVID-19 infection rate (1). For that reason, lockdowns and mobility restriction measures were introduced in Germany by state ordinances and, from 22 April 2021, by federal law to reduce the likelihood of viral transmission. These included recommendations to refrain from unnecessary travel, temporary travel bans to certain tourist areas and a nighttime curfew.

Mobile network data from Germany have already been used in earlier studies to investigate the association between mobility and the occurrence of COVID-19 cases (2). These studies found a reduction in mobility after the introduction of the restriction measures and a negative correlation between mobility and the number of confirmed cases (3). The use of anonymized data is the primary limitation of these studies as they do not allow analyses by individual characteristics.

The cohort of our study is not subject to this limitation. The aim of this study is to explore the mobility pattern over the course of the pandemic by demographic characteristics. Earlier analyses of this cohort until mid-May 2020 showed an overall strong reduction in mobility at the start of the pandemic uniformly across all groups of the population, followed by a gradual increase (4).

Methods

We evaluated a dynamic cohort of members of the GapFish online market research panel. Sociodemographic data and individual geolocation data of more than 2500 persons living in Germany—aged from 16 to 89 years; 53% male—is available for the period starting 1 January 2020. This geolocation information has been collected continuously by a smartphone app (4). The GapFish sample recruitment protocol is designed to produce a cross-section of the German population which is representative in terms of sociodemographic characteristics.

We analyzed the data of this cohort until 27 June 2021, stratified by age group, sex and federal state. Mobility was defined as the daily distance travelled by an individual person. For each day, we calculated the moving average of the past seven days. We used the mean mobility in the two months preceding the pandemic (1 January 2020 to 29 February 2020) as reference values to calculate the daily relative reduction in mobility. For the age groups 16–29, 30–59 and 60+, the mean mobility distances were 19.5 km, 19.0 km and 9.1 km, respectively.

Results

Figure 1 shows the relative reduction by age group which was most pronounced in April 2020 and in January and February 2021 when mobility was less than 50% compared to the reference period. Presumably, public awareness of the risks was greatest at these times. The pattern of relative reduction was similar in all age groups and in both sexes, with a slightly higher relative mobility in the younger age groups in summer 2020 and in the last month of observation.

Figure 1

Relative mobility

Figure 2 shows the median absolute mobility by sex, again as a 7-day moving average. In the two months preceding the pandemic, the distance travelled daily was about 19 km in men and 13.5 km in women. During the periods of lowest mobility, these distances were reduced to daily 7.5 km and 5 km, respectively. Over the entire observation period, men show a 40% higher mobility compared to women. The male-female mobility ratio remained relatively constant during the observation period, indicating that the implementation of the recommendations for reduced mobility was over time similar in men and women. The analyses by federal state showed a lower mobility in the city states which is in line with the findings of the studies using anonymized data. State-specific regulations had no clearly demonstrable effects on mobility.

Figure 2

Average daily distance travelled

Discussion

It was shown that the pattern of mobility reduction during the COVID-19 pandemic was similar in all age groups, in both sexes and in all regions. Assumptions that the younger population is less prepared to accept restrictions could not be confirmed; however, a stronger increase in mobility was noted in the younger age group in the last weeks of the observation period. We have also shown that men and women adhere equally to the recommendations on mobility reduction. These results are suggestive of homogeneity in the society.

We are aware of the possibility that the structure of the online panel, consisting of voluntary smartphone users, may have led to selection bias. Consequently, our sample does not allow for an unbiased estimation of the absolute mobility pattern in the German population. Nevertheless, we consider it sufficiently probable that the changes observed in our sample are in line with the mobility pattern in the general population.

There is only limited data available to quantify the effect of mobility on COVID-19 incidence or on the reproduction number R and to distinguish this effect from effects of other measures. A positive correlation between mobility and the R value 14 days later was observed in the Greater Toronto Area (GTA) in Canada where it was shown that mobility had a time-lag effect on infection numbers (5).

Heiko Becher, Sebastian Bönisch, Karl Wegscheider

Institute of Medical Biometry and Epidemiology, University Medical Center Hamburg-Eppendorf UKE (Becher, Wegscheider) [e-mail]

GIM Gesellschaft für Innovative Marktforschung mbH, Heidelberg (Bönisch), Germany

Conflict of interest statement
The authors declare that no conflict of interest exists.

Manuscript received on 12 May 2021, revised version accepted on 8 July 2021

Translated from the original German by Ralf Thoene, MD

Cite this as:
Becher H, Bönisch S, Wegscheider K: Reduction of mobility during the COVID-19 pandemic in Germany according to age, sex, and federal state.
Dtsch Arztebl Int 2021; 118: 536–7. DOI: 10.3238/arztebl.m2021.0293

References

1. Zachreson C, Mitchell L, Lydeamore MJ, Rebuli N, Tomko M, Geard N: Risk mapping for COVID-19 outbreaks in Australia using mobility data. J R Soc Interface 2021; 18: 20200657

2. Walker MD, Sulyok M: The relationship between mobility and COVID-19 in Germany: modeling case occurrence using apple‘s mobility trends data. Methods Inf Med 2021; 59: 179–82

3. Covid-19 mobility project. www.covid-19-mobility.org/de/ (last accessed on 1 July 2021).

4. Bönisch S, Wegscheider K, Krause L, et al.: Effects of coronavirus disease (COVID-19) related contact restrictions in Germany, March to May 2020, on the mobility and relation to infection patterns. Frontiers in Public Health 2020; 8: 619

5. Dainton C, Hay A: Quantifying the relationship between lockdowns, mobility, and effective reproduction number (Rt) during the COVID-19 pandemic in the greater Toronto Area. Res Sq 2021; rs.3.rs-378425. Preprint