Can a Mayor Change the Course of a Pandemic? An Agent-Based Study on the COVID Spread on Local Level in Germany
- Institute of Physics, Johannes Gutenberg University, Germany
- Physikalisches Institut, Rheinische Friedrich-Wilhelms-University, Germany
Cite this article
as: L. Heger, K. Akdogan, F. Huwer, M. Schott; 2025; Can a Mayor Change the Course of a Pandemic? An Agent-Based Study on the COVID Spread on Local Level in Germany; International Journal of Microsimulation; 18(2); 70-78.
doi: 10.34196/ijm.00322
Figures
Figure 1
Hospitalization rate of the last 7 days (red line) for the age group 35-59 (left) and ≥ 60 (right), together with the June-Germany simulation with the best fitting parameters (blue line), its statistical uncertainty (shaded blue) and the simulated curves of alternative sets of model parameters tested during the fitting procedure (gray). The vertical line indicates the date until when the data was fitted.
Figure 2
Comparison of plausible simulations of the hospitalisation rate with and without any state policies (left) as well as with and without any schools closing policies (right). The hospitalisations are averaged over a seven day window.
Figure 3
Overall number of infections for all 36 districts of Rhineland Palatinate between October 2020 and February 2021, once for the official reported cases and once by the June-Germany simulation.
Figure 4
Normalized number of infections per 10k inhabitants for all 36 districts of Rhineland Palatinate between October 2020 and February 2021, once for the official reported cases and once by the June-Germany simulation.
Figure 5
Spread of the overall number of infections (a) and spread of the number of infections per 10k inhabitants (b) in all 36 districts of Rhineland Palatinate between October 2020 and February 2021, once for the official reported cases and once by the June-Germany simulation.
Figure 6
Correlation between the observed and simulated overall number of infections per district (left) and the number of infections per 10k inhabitants between October 2020 and February 2021.
Tables
Table 1
Overview of the most relevant contact intensity parameter of June framework. Together with the infectiousness of the infectors at given time t, the susceptibility of the potential infectee as well as the exposure time interval when two or more agents meet in the simulation, they build the basis for the probability that an agent gets infected. Technical details on the implementation are discussed in Bullock (2021). In addition, their central values used in the final simulation as well as their 95% CL interval are given.
| Contact intensity parameter | Mean-Value | 95% CL interval | Contact intensity parameter | Mean-Value | 95% CL interval |
|---|---|---|---|---|---|
| care home | 0.28 | [0.16,0.40] | hospital | 0.19 | [0.11,0.27] |
| care visits | 6.13 | [3.60,8.66] | household | 0.30 | [0.42,0.18] |
| cinema | 0.52 | [0.30,0.73] | household visits | 0.55 | [0.32,0.78] |
| city transport | 0.17 | [0.10,0.24] | inter city transport | 0.17 | [0.10,0.24] |
| company | 0.32 | [0.19,0.45] | pub | 0.42 | [0.24,0.60] |
| grocery | 0.48 | [0.28,0.67] | school | 0.32 | [0.19,0.45] |
| gym | 0.40 | [0.23,0.57] | university | 0.17 | [0.10,0.24] |
Data and code availability
The simulation code is available at https://github.com/fneuhaus/JUNE_germany/
https://github.com/lheger6626/june_mayors The underlying data is available at https://www.corona-datenplattform.de.
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