An agent-based epidemics simulation to compare and explain screening and vaccination prioritisation strategies
Carole Adam, Helene Arduin
TL;DR
The paper introduces an accessible agent-based epidemic simulator designed to explain how screening and vaccination prioritisation shape outbreak dynamics rather than to perfectly predict them. By implementing a simplified NetLogo model with interactive interfaces, the authors compare screening and vaccination strategies across scenarios, focusing on how test quality, targeting, and immunity duration influence outcomes. Key findings show that predictive-value based estimations outperform simple proportionality, early and intensive screening improves epidemic curve estimation, and vaccination strategies have tradeoffs: high-risk-first reduces serious cases, while high-contact-first more effectively curbs transmission. The work provides public-facing tools and insights for understanding sanitary measures, highlighting potential improvements and directions for future integrated interventions and user studies.
Abstract
This paper describes an agent-based model of epidemics dynamics. This model is willingly simplified, as its goal is not to predict the evolution of the epidemics, but to explain the underlying mechanisms in an interactive way. This model allows to compare screening prioritisation strategies, as well as vaccination priority strategies, on a virtual population. The model is implemented in Netlogo in different simulators, published online to let people experiment with them. This paper reports on the model design, implementation, and experimentations. In particular we have compared screening strategies to evaluate the epidemics vs control it by quarantining infectious people; and we have compared vaccinating older people with more risk factors, vs younger people with more social contacts.