Author ORCID Identifier

ttps://orcid.org/0009-0005-3820-4695

Document Type

Technical Report

Disciplines

Applied mathematics, Computer Sciences, Behavioural sciences biology, Infectious diseases, Epidemiology

Publication Details

This work has not been published but this report serve to replicate the model of HCV transmission that incorporate both structural and behavioural heterogeneities used in the paper which will be submitted to a journal.

The baseline model (M1) that was extended in this model is described in Ale et al., 2026, https://doi.org/10.21427/810p-ke33

The baseline model (M2) that was extended in this model is described in Ale et al, 2026, https://doi.org/10.21427/6r9q-4928

The baseline model (M3) that was extended in this model is described in Ale et al, 2026,https://doi.org/10.21427/j2nj-a920

Abstract

The model described in this ODD is an agent-based model of hepatitis C virus (HCV) transmission among people who inject drugs (PWID). The model incorporates structural heterogeneity through a three-group interaction framework and behavioural heterogeneity through group-specific syringe-sharing rates. The syringe-sharing population in the model is divided into core, inner, and outer circle groups representing individuals with high, moderate, and low levels of syringe-sharing interaction intensity, respectively. In addition to differences in the number of daily interaction opportunities across groups, agents in each group are assigned distinct syringe-sharing probabilities, reflecting variation in risk-taking behaviour across structural groups within the syringe-sharing social network. While epidemiological processes remain identical across agents, both the frequency of interaction events and the likelihood of sharing during those interactions vary by group. Interactions are generated dynamically using proximity-based sampling at each timestep (one day), allowing agents to form syringe-sharing interactions based on spatial closeness. Transmission occurs through direct syringe-sharing events, with infection probability determined by accumulated daily exposure to infectious partners. The model tracks transitions between susceptible, chronic infection, treatment, cure, and resistance states, and is calibrated to Irish HCV surveillance data. This model serves to examine how combining structural and behavioural heterogeneity influences epidemic dynamics relative to simpler structural configurations and the homogeneous baseline model.

DOI

https://doi.org/10.21427/5f70-k934

Funder

Technological University Dublin

Creative Commons License

Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License
This work is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 4.0 International License.

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