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

• Published in: Scientific reports Vol. 9; no. 1; pp. 19289 - 13
• Main Authors: Liesenborgs, Jori, Hendrickx, Diana M., Kuylen, Elise, Niyukuri, David, Hens, Niel, Delva, Wim
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 17.12.2019; Nature Publishing Group
• Subjects: 639/705/794; 692/308/174; 692/699/255/1901; Algorithms; Disease prevention; Epidemiology; HIV; HIV - pathogenicity; HIV Infections - epidemiology; Human immunodeficiency virus; Humanities and Social Sciences; Humans; Infectious diseases; Interfaces; multidisciplinary; Science; Science (multidisciplinary); Simulation; Software
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-019-55689-4

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.