Vaccination, asymptomatics and public health information in COVID-19

Grinfeld, Michael and Mulheran, Paul A (2024) Vaccination, asymptomatics and public health information in COVID-19. Journal of Physics A: Mathematical and Theoretical, 57 (8). 085601. ISSN 0305-4470 (https://doi.org/10.1088/1751-8121/ad242f)

[thumbnail of Grinfeld-etal-JPAMT-2024-Vaccination-asymptomatics-and-public-health]
Preview
Text. Filename: Grinfeld-etal-JPAMT-2024-Vaccination-asymptomatics-and-public-health.pdf
Final Published Version
License: Creative Commons Attribution 4.0 logo

Download (415kB)| Preview

Abstract

The dynamics of the COVID-19 pandemic is greatly influenced by vaccine quality, as well as by vaccination rates and the behaviour of infected individuals, both of which reflect public health policies. We develop a model for the dynamics of relevant cohorts within a fixed population, taking extreme care to model the reduced social contact of infected individuals in a rigorous self-consistent manner. The basic reproduction number R 0 is then derived in terms of the parameters of the model. Analysis of R 0 reveals two interesting possibilities, both of which are plausible based on known characteristics of COVID-19. Firstly, if the population in general moderates social contact, while infected individuals who display clinical symptoms tend not to isolate, then increased vaccination can drive the epidemic towards a disease-free equilibrium (DFE). However, if the reverse is true, then increased vaccination can destabilise the DFE and yield an endemic state. This surprising result is due to the fact that the vaccines are leaky, and can lead to an increase in asymptomatic individuals who unknowingly spread the disease. Therefore, this work shows that public policy regarding the monitoring and release of health data should be combined judiciously with modeling-informed vaccination policy to control COVID-19.