Package: sid-dev
Featuressid is an agent-based simulation model for infectious diseases like COVID-19. It scales
from simple examples to complex models which makes it an ideal tool for prototyping,
educational purposes, and research.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:At the core of the model, people meet people based on a matching algorithm. It is
possible to represent a variety of contact types like households, leisure activities,
school classes and nurseries with teachers and several types of contacts at the
workplace. Contact types can be random or recurrent and vary in frequency.Policies allow to shut down contact types entirely or partially. The reduction of
contacts can be random or systematic, e.g., to allow for essential workers.Infection probabilities vary across contact types and depending on the age of the
susceptible individual, but are invariant to policies which reduce contacts. The
invariance is an essential property for predicting the effects of policies for which
empirical data does not exist.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 data on contact frequencies.The model allows for two testing mechanisms, representing PCR and rapid tests. PCR
tests always reveal the true health status of the tested individual after some days
which can be used for testing policies or to differentiate between known and unknown
infections.In contrast, rapid tests immediately return the test outcome and identify infected
people based on the sensitivity and specificity of the test. It is possible to
implement reactions to the outcome of the test enabling individuals to plan meetings,
test with a rapid test, and to refrain from meeting if the test is positive.Mutations may lead to multiple, prevalent virus strains with different
characteristics. For now, sid is able to model an unlimited amount of virus strains
with different degrees of infectiousness.It is possible to implement models for vaccinating people who, then, gain perfect
immunity from the disease.More information can be found in thediscussion paperor in thedocumentation.Installationsid is available onPyPIand onAnaconda.organd can be installed with$pipinstallsid-dev# or$condainstall-cconda-forgesid-devPublicationssid 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).CitationIf 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}
}