Agent based modelling of blood borne viruses: a scoping review.

Seun Ale, Elizabeth Hunter, John D Kelleher
Author Information
  1. Seun Ale: School of Computer Science, Technological University Dublin, Grangegorman Lower, Dublin, D07 H6K8, Dublin, Ireland. seun.ale@tudublin.ie.
  2. Elizabeth Hunter: School of Computer Science, Technological University Dublin, Grangegorman Lower, Dublin, D07 H6K8, Dublin, Ireland.
  3. John D Kelleher: School of Computer Science and Statistics, Trinity College Dublin, College Green, Dublin, D02 PN40, Dublin, Ireland.

Abstract

BACKGROUND: The models that historically have been used to model infectious disease outbreaks are equation-based and statistical models. However, these models do not capture the impact of individual and social factors that affect the spread of common blood-borne viruses (BBVs) such as human immunodeficiency virus (HIV), hepatitis C virus (HCV), and hepatitis B virus (HBV). Agent-based modelling (ABM) is an alternative modelling approach that is gaining popularity in public health and epidemiology. As the field expands, it is important to understand how ABMs have been applied. In this context, we completed a scoping review of research that has been done on the ABM of BBVs.
METHOD: The inclusion/exclusion criteria were drafted using the idea of Population, Concept, and Context (PCC). The Preferred Reporting Item for Systematic Reviews and Meta-Analysis, an extension to scoping review (PRISMA-ScR), was employed in retrieving ABM literature that studied BBVs. Three databases (Scopus, Pubmed, and Embase) were systematically searched for article retrieval. 200 articles were retrieved from all the databases, with 10 duplicates. After removing the duplicates, 190 papers were screened for inclusion. After analysing the remaining articles, 70 were excluded during the abstract screening phase, and 32 were excluded during the full-text decision. Eighty-eight were retained for the scoping review analysis. To analyse this corpus of 88 papers, we developed a five-level taxonomy that categorised each paper based first on disease type, then transmission mechanism, then modelled population, then geographic location and finally, model outcome.
RESULTS: The result of this analysis show significant gaps in the ABM of BBV literature, particularly in the modeling of social and individual factors influencing BBV transmission.
CONCLUSION: There is a need for more comprehensive models that address various outcomes across different populations, transmission and intervention mechanisms. Although ABMs are a valuable tool for studying BBVs, further research is needed to address existing gaps and improve our understanding of individual and social factors that influence the spread and control of BBVs. This research can inform researchers, modellers, epidemiologists, and public health practitioners of the ABM research areas that need to be explored to reduce the burden of BBVs globally.

Keywords

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Grants

  1. PB04581/Technological University Dublin
  2. 13/RC/2106 P2/ADAPT Research Centre, which is funded by Science Foundation Ireland

MeSH Term

Humans
Blood-Borne Infections
Blood-Borne Pathogens
Virus Diseases
Disease Outbreaks
Hepatitis B

Word Cloud

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