Studying Disease Reinfection Rates, Vaccine Efficacy, and the Timing of Vaccine Rollout in the Context of Infectious Diseases: A COVID-19 Case Study

Elizabeth B. Amona, Indranil Sahoo (), Edward L. Boone and Ryad Ghanam
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Elizabeth B. Amona: Department of Statistical Sciences and Operations Research, Virginia Commonwealth University, Richmond, VA 23284, USA
Indranil Sahoo: Department of Statistical Sciences and Operations Research, Virginia Commonwealth University, Richmond, VA 23284, USA
Edward L. Boone: Department of Statistical Sciences and Operations Research, Virginia Commonwealth University, Richmond, VA 23284, USA
Ryad Ghanam: Department of Liberal Arts and Sciences, Virginia Commonwealth University in Qatar, Education City, Doha P.O. Box 8095, Qatar

IJERPH, 2025, vol. 22, issue 5, 1-21

Abstract: The COVID-19 pandemic has highlighted the intricate nature of disease dynamics, extending beyond transmission patterns to the complex interplay of intervention strategies. In the post-COVID-19 era, reinfection has emerged as a critical factor, shaping how we model disease progression, evaluate immunity, and assess the effectiveness of public health interventions. This research uniquely explores the varied efficacy of existing vaccines and the pivotal role of vaccination timing in the context of COVID-19. Departing from conventional modeling, we introduce two models that account for the impact of vaccines on infections, reinfections, and deaths. We estimate model parameters under the Bayesian framework, specifically utilizing the Metropolis–Hastings Sampler. We conduct data-driven scenario analyses for the State of Qatar, quantifying the potential duration during which the healthcare system could have been overwhelmed by an influx of new COVID-19 cases surpassing available hospital beds. Additionally, the research explores similarities in predictive probability distributions of cumulative infections, reinfections, and deaths, employing the Hellinger distance metric. Comparative analysis, utilizing the Bayes factor, underscores the plausibility of a model assuming a different susceptibility rate to reinfection, as opposed to assuming the same susceptibility rate for both infections and reinfections. Results highlight the adverse outcomes associated with delayed vaccination, emphasizing the efficacy of early vaccination in reducing infections, reinfections, and deaths. Our research advocates for prioritization of early vaccination as a key strategy in effectively combating future pandemics, thereby providing vital insights for evidence-based public health interventions.

Keywords: Bayes factor; compartmental models; COVID-19; epidemiology; Hellinger distance; kernel density estimation (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2025
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