sid-dev 0.0.3

Simulate the spread of COVID-19 with different policies.

Simulate the spread of COVID-19 with different policies.

sid

Features

sid is a agent-based simulation-based model for infectious diseases like COVID-19.

sid’s focus is on predicting the effects of non-pharmaceutical interventions on the spread of an infectious disease. To accomplish this task it is built to capture important aspects of contacts between people. In particular, sid has the following features:

1. At the core of the model, people meet people based on a matching algorithm. We distinguish various types of contacts. Currently, these are households, leisure activities, schools, nurseries and several types of contacts at the workplace. Contact types can be random or recurrent and vary in frequency.

2. Policies can be implemented as shutting down contact types entirely or partially. The reduction of contacts can be random or systematic, e.g., to allow for essential workers.

3. Infection probabilities vary across contact types, but are invariant to policies which reduce contacts.

4. The model achieves a good fit on German infection and fatality rate data even if only the infection probabilities are fit to the data and the remaining parameters are calibrated from the medical literature and datasets on contact frequencies.

More information can be found in the discussion paper or in the documentation.

Installation

sid is available on PyPI and on Anaconda.org and can be installed with

$ pip install sid-dev

# or

$ conda install -c conda-forge sid-dev

Publications

sid has been featured in some publications which are listed here:

• Gabler, J., Raabe, T., & Röhrl, K. (2020). People Meet People: A Microlevel Approach to Predicting the Effect of Policies on the Spread of COVID-19.

• Dorn, F., Gabler, J., von Gaudecker, H. M., Peichl, A., Raabe, T., & Röhrl, K. (2020). Wenn Menschen (keine) Menschen treffen: Simulation der Auswirkungen von Politikmaßnahmen zur Eindämmung der zweiten Covid-19-Welle. ifo Schnelldienst Digital, 1(15).

• Gabler, J., Raabe, T., Röhrl, K., & Gaudecker, H. M. V. (2020). Die Bedeutung individuellen Verhaltens über den Jahreswechsel für die Weiterentwicklung der Covid-19-Pandemie in Deutschland (No. 99). Institute of Labor Economics (IZA).

• Gabler, J., Raabe, T., Röhrl, K., & Gaudecker, H. M. V. (2021). Der Effekt von Heimarbeit auf die Entwicklung der Covid-19-Pandemie in Deutschland (No. 100). Institute of Labor Economics (IZA).

Citation

If you rely on sid for your own research, please cite it with

@article{Gabler2020,
  Title = {
    People Meet People: A Microlevel Approach to Predicting the Effect of Policies
    on the Spread of COVID-19
  },
  Author = {Gabler, Janos and Raabe, Tobias and R{\"o}hrl, Klara},
  Year = {2020},
  Publisher = {IZA Discussion Paper}
}