Quantifying the Public Health Effects of Vaccine Hesitancy and Delays in Screening Clinically Infected Patients: Insights From a COVID-19 Transmission Model

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dc.contributor.authorLolika, Paride O,
dc.contributor.authorMlyashimbi Helikumi,
dc.contributor.authorKenneth Sube,
dc.contributor.authorSteady Mushayabasa
dc.date.accessioned2026-05-21T17:46:36Z
dc.date.issued2025-05-14
dc.description.abstractMotivated 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
dc.identifier.urihttps://repository.must.ac.tz/handle/123456789/617
dc.language.isoen
dc.publisherIJMSO
dc.subjectCoronavirus
dc.subjectMathematical Model
dc.subjectTime Delay
dc.subjectVaccine Hesitancy
dc.subjectScreening Delay
dc.titleQuantifying the Public Health Effects of Vaccine Hesitancy and Delays in Screening Clinically Infected Patients: Insights From a COVID-19 Transmission Model
dc.typeArticle

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