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Sci Rep. 2019 Dec 17;9(1):19289. doi: 10.1038/s41598-019-55689-4.

SimpactCyan 1.0: An Open-source Simulator for Individual-Based Models in HIV Epidemiology with R and Python Interfaces.

Author information

1
Expertise Centre for Digital Media, Hasselt University - tUL, Diepenbeek, Belgium.
2
Center for Statistics, I-BioStat, Hasselt University, Diepenbeek, Belgium.
3
IDLab, University of Antwerp, Antwerp, Belgium.
4
Centre for Health Economics Research and Modelling Infectious Diseases and Centre for the Evaluation of Vaccination, Vaccine & Infectious Disease Institute, University of Antwerp, Antwerp, Belgium.
5
The South African Department of Science and Technology-National Research Foundation (DST-NRF) Centre of Excellence in Epidemiological Modelling and Analysis (SACEMA), Stellenbosch University, Stellenbosch, South Africa.
6
Department of Global Health, Faculty of Medicine and Health, Stellenbosch University, Stellenbosch, South Africa.
7
Center for Statistics, I-BioStat, Hasselt University, Diepenbeek, Belgium. wimdelva@gmail.com.
8
The South African Department of Science and Technology-National Research Foundation (DST-NRF) Centre of Excellence in Epidemiological Modelling and Analysis (SACEMA), Stellenbosch University, Stellenbosch, South Africa. wimdelva@gmail.com.
9
Department of Global Health, Faculty of Medicine and Health, Stellenbosch University, Stellenbosch, South Africa. wimdelva@gmail.com.
10
International Centre for Reproductive Health, Ghent University, Ghent, Belgium. wimdelva@gmail.com.
11
Rega Institute for Medical Research, KU Leuven, Leuven, Belgium. wimdelva@gmail.com.
12
School for Data Science and Computational Thinking, Stellenbosch University, Stellenbosch, South Africa. wimdelva@gmail.com.

Abstract

SimpactCyan is an open-source simulator for individual-based models in HIV epidemiology. Its core algorithm is written in C++ for computational efficiency, while the R and Python interfaces aim to make the tool accessible to the fast-growing community of R and Python users. Transmission, treatment and prevention of HIV infections in dynamic sexual networks are simulated by discrete events. A generic "intervention" event allows model parameters to be changed over time, and can be used to model medical and behavioural HIV prevention programmes. First, we describe a more efficient variant of the modified Next Reaction Method that drives our continuous-time simulator. Next, we outline key built-in features and assumptions of individual-based models formulated in SimpactCyan, and provide code snippets for how to formulate, execute and analyse models in SimpactCyan through its R and Python interfaces. Lastly, we give two examples of applications in HIV epidemiology: the first demonstrates how the software can be used to estimate the impact of progressive changes to the eligibility criteria for HIV treatment on HIV incidence. The second example illustrates the use of SimpactCyan as a data-generating tool for assessing the performance of a phylodynamic inference framework.

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