Using discrete-event simulation for planning and managing mass vaccination centers: a comprehensive examination
Abdessalem Jerbi
What the paper says
Different studies based on discrete-event simulation have been conducted to evaluate the operation of mass vaccination centers and assist healthcare planners in improving their performance. These studies explored different parameters and scenarios for the operation of mass vaccination centers and generated several results. This review aims to assess discrete-event simulation applications in mass vaccination center management. This critical analysis concentrated on every phase of the project method for discrete-event simulation. To ensure comprehensive coverage, scholarly works about the application of discrete-event simulation in mass vaccination centers were sourced from the World Health Organization’s COVID-19 research database and other databases, including Web of Science, Scopus, MDPI, Sage, PubMed, medRxiv, and the WHO COVID-19 research database. Only English-language studies detailing the application of discrete-event simulation in mass vaccination centers were taken into account. The vaccines, simulation software, strategies, or origins were all unrestricted. Hence, the search was expanded to include other vaccination campaigns besides COVID-19. Our investigation included vaccination campaigns against H1N1 and the influenza virus. Eleven studies were selected. The results show that most simulation studies of vaccination centers have several shortcomings that are visible at every phase of the discrete-event simulation project. Through an analysis of the current state of vaccination center simulation studies, our research identifies best practices for upcoming studies. Following these recommendations ought to raise the standard of upcoming studies at large vaccination centers.
3 citations
Evidence weight
Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40
| F · citation impact | 0.32 × 0.4 = 0.13 |
| M · momentum | 0.57 × 0.15 = 0.09 |
| V · venue signal | 0.50 × 0.05 = 0.03 |
| R · text relevance † | 0.50 × 0.4 = 0.20 |
† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.