Predicting the Effects of Waning Vaccine Immunity Against COVID-19 through High-Resolution Agent-Based Modeling

Agnieszka Truszkowska, Lorenzo Zino, Sachit Butail, Emanuele Caroppo, Zhong Ping Jiang, Alessandro Rizzo, Maurizio Porfiri

Research output: Contribution to journalArticlepeer-review

Abstract

The potential waning of the vaccination immunity to COVID-19 could pose threats to public health, as it is tenable that the timing of such waning would synchronize with the near-complete restoration of normalcy. Should also testing be relaxed, a resurgent COVID-19 wave in winter 2021/2022 might be witnessed. In response to this risk, an additional vaccine dose, the booster shot, is being administered worldwide. A projected study with an outlook of 6 months explores the interplay between the rate at which boosters are distributed and the extent to which testing practices are implemented, using a highly granular agent-based model tuned on a medium-sized US town. Theoretical projections indicate that the administration of boosters at the rate at which the vaccine is currently administered could yield a severe resurgence of the pandemic. Projections suggest that the peak levels of mid-spring 2021 in the vaccination rate may prevent such a scenario to occur, although exact agreement between observations and projections should not be expected due to the continuously evolving nature of the pandemic. This study highlights the importance of testing, especially to detect asymptomatic individuals in the near future, as the release of the booster reaches full speed.

Original languageEnglish (US)
JournalAdvanced Theory and Simulations
DOIs
StateAccepted/In press - 2022

Keywords

  • COVID-19
  • agent-based model
  • epidemiology
  • urban science
  • vaccination

ASJC Scopus subject areas

  • Statistics and Probability
  • Numerical Analysis
  • Modeling and Simulation
  • General

Fingerprint

Dive into the research topics of 'Predicting the Effects of Waning Vaccine Immunity Against COVID-19 through High-Resolution Agent-Based Modeling'. Together they form a unique fingerprint.

Cite this