Version 1
: Received: 25 November 2022 / Approved: 30 November 2022 / Online: 30 November 2022 (03:07:20 CET)
Version 2
: Received: 30 November 2022 / Approved: 1 December 2022 / Online: 1 December 2022 (02:09:32 CET)
How to cite:
Viard, F.; Acheson, E.; Allibert, A.; Sauve, C.; Leighton, P. SamPy: A New Python Library for Stochastic Spatial Agent-Based Modeling in Epidemiology of Infectious Diseases. Preprints2022, 2022110556. https://doi.org/10.20944/preprints202211.0556.v2
Viard, F.; Acheson, E.; Allibert, A.; Sauve, C.; Leighton, P. SamPy: A New Python Library for Stochastic Spatial Agent-Based Modeling in Epidemiology of Infectious Diseases. Preprints 2022, 2022110556. https://doi.org/10.20944/preprints202211.0556.v2
Viard, F.; Acheson, E.; Allibert, A.; Sauve, C.; Leighton, P. SamPy: A New Python Library for Stochastic Spatial Agent-Based Modeling in Epidemiology of Infectious Diseases. Preprints2022, 2022110556. https://doi.org/10.20944/preprints202211.0556.v2
APA Style
Viard, F., Acheson, E., Allibert, A., Sauve, C., & Leighton, P. (2022). SamPy: A New Python Library for Stochastic Spatial Agent-Based Modeling in Epidemiology of Infectious Diseases. Preprints. https://doi.org/10.20944/preprints202211.0556.v2
Chicago/Turabian Style
Viard, F., Caroline Sauve and Patrick Leighton. 2022 "SamPy: A New Python Library for Stochastic Spatial Agent-Based Modeling in Epidemiology of Infectious Diseases" Preprints. https://doi.org/10.20944/preprints202211.0556.v2
Abstract
Agent-based models (ABMs) are computational models for simulating the actions and interactions of autonomous agents in time and space. These models allow users to simulate the complex interactions between individual agents and the landscapes they inhabit and are increasingly used in epidemiology to understand complex phenomena and make predictions. However, as the complexity of the simulated systems increases, notably when disease control interventions are considered, model flexibility and processing speed can become limiting. Here we introduce SamPy, an open-source Python library for stochastic agent-based modeling of epidemics. SamPy is a modular toolkit for model development, providing adaptable modules that capture host movement, disease dynamics, and disease control interventions. Memory optimization and design provide high computational efficiency allowing modelling of large, spatially-explicit populations of agents over extensive geographical areas. In this article, we demonstrate the high flexibility and processing speed of this new library. The version of SamPy considered in this paper is available at https://github.com/sampy-project/sampy-paper .
Medicine and Pharmacology, Epidemiology and Infectious Diseases
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Commenter: Francois Viard
Commenter's Conflict of Interests: Author