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Description: This dataset contains mobility data collected from mobile phones in Germany during the first half of 2020 (January-July). The data als includes mobility data from March 2019, which can be used to study changes in mobility during the Covid-19 pandemic in 2020. The data consists of the daily number of trips between counties (NUTS 3) in Germany. The data is anonymized, such that each county is only listed by an anonymous ID. As additional information, the distances between each pair of counties is provided (in km, with slight multiplicative Gaussian noise to hinder de-anonymization, mu=1, sigma=0.02), and the broad category of each county (city, border, other). Also included is Python code for an epidemiological simulation of an SIR model with commuter type-mobility, which is also available at the Github repository franksh/EpiCommute An more up-to-date version of an alternative mobility dataset is also available at: https://github.com/rocs-org/covid-mobility-data This dataset encompasses a longer timeframe (Jan 2020 to Dec 2021), but not the full network, only the number of trips originating in each county and the mobility change per county. The dataset and the obfuscation process as well as the SIR model are described in detail in the SI of the following publication: Schlosser, F., Maier, B. F., Hinrichs, D., Zachariae, A., & Brockmann, D. (2020). COVID-19 lockdown induces structural changes in mobility networks--Implication for mitigating disease dynamics. arXiv preprint arXiv:2007.01583. https://arxiv.org/abs/2007.01583