( A) The change in total movements Δ n ( t ) in 2020, relative to March 2019. Mobility changes in Germany during the COVID-19 pandemic. Flows below a threshold of F j i ( t ) < 5 were omitted from the data due to anonymization requirements.įig. The baseline average daily flow between all pairs of locations is ⟨ F j i ( t ) ⟩ = 1,103 (averaged over all days in March 2019) with a SD of S D = 26.413.
During times with normal mobility (e.g., during March 2019, which we use as a baseline, see below) the total flow is 176 m i l l i o n trips per day on average, recorded among 43.6 million users ( 24), corresponding to an average of 3.8 trips per user per day. Note that flows within counties F i i ( t ) are included. Locations are the m = 401 counties of Germany. The element F j i ( t ) quantifies the total number of trips from location i to location j on a given day t. These trips are aggregated over the course of a day to build the daily flow matrix F ( t ). A resting phase is defined as a mobile phone not switching its connected cell tower. The data counts the number of trips, where a trip is defined as a single mobile phone switching cell towers at least once, between two resting phases of at least 15 min ( Fig.
We base our analysis on mobility flows collected from mobile phone data. Mobility Trends in Germany General Mobility Changes. Using simulations of a commuter-based susceptible-infected-removed (SIR) model, we demonstrate that these changes have considerable practical implications as they suppress (or “flatten”) the curve of an epidemic remarkably and delay the disease’s arrival between distant regions. This shows a marked reduction of the “small-world” characteristic, where geographic distance is usually of lesser importance in determining path lengths ( 22, 23). Most importantly, we find a changed scaling relation between path lengths and geographic distance: During lockdown, the effective distance (and arrival time in spreading processes) to a destination continually grows with geographic distance. Due to the reduction of long-distance travel, the network becomes more local and lattice-like. Beyond a general reduction in mobility, we find considerable structural changes in the mobility network. We analyze movements recorded from mobile phones of 43.6 million individuals in Germany. Here, we analyze structural changes in mobility patterns in Germany during the COVID-19 pandemic. We demonstrate that these structural changes have a considerable effect on epidemic spreading processes by “flattening” the epidemic curve and delaying the spread to geographically distant regions. The trimming of long-range network connectivity leads to a more local, clustered network and a moderation of the “small-world” effect. We find that long-distance travel was reduced disproportionately strongly. Here we use movement data of mobile phone users to show that mobility in Germany has not only been reduced considerably: Lockdown measures caused substantial and long-lasting structural changes in the mobility network. However, it remains unclear whether these reductions caused deeper structural changes in mobility networks and how such changes may affect dynamic processes on the network. Studies using digital data sources show that the mobility of individuals was effectively reduced in multiple countries.
In the wake of the COVID-19 pandemic many countries implemented containment measures to reduce disease transmission.
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