Quantifying the Public Health Effects of Vaccine Hesitancy and Delays in Screening Clinically Infected Patients: Insights From a COVID-19 Transmission Model
Loading...
Date
2025-09-20
Journal Title
Journal ISSN
Volume Title
Publisher
International Journal of Mathematical Sciences and Optimization
Abstract
Motivated by the recent COVID-19 outbreak, we develop a time delay infectious disease
model that incorporates vaccination and screening of clinically infected patients and calibrate
it using Chinese data to understand the quantitative implications of vaccine hesitancy and
delay in the screening of clinically infected patients. Vaccine hesitancy refers to the denial or
delay in acceptance of vaccines despite their availability. Understanding the implications of
vaccine hesitancy is therefore essential for designing public health interventions. Analysis of
the model revealed that whenever R0 ≤ 1, there exists a globally asymptotically disease-free
equilibrium. However, whenever R0 > 1, there exists a unique endemic equilibrium which is
globally asymptotically stable. In addition, results also show that vaccine hesitancy and delay
in hospitalizing clinically infected patients have a stronger impact on the deaths toll and new
infections generated [1,2]. Vaccine hesitancy and delayed screening of clinically infected patients
lead to harmonic oscillations in deaths and new cases, which, however, die out over time. Our
findings underscore the importance of including vaccine hesitancy and delay in hospitalizing
clinically infected patients in the design of control strategies for infectious diseases.
Description
This Journal Article was published by International Journal of Mathematical Sciences and
Optimization in 2025.