Search

Both individually and cluster randomized study designs have been used for vaccine trials to assess the effects of vaccine on reducing the risk of disease or infection. The choice between individually and cluster randomized designs is often driven by the target estimand of interest (eg, direct versus total), statistical power, and, importantly, logistic feasibility. To combat emerging infectious disease threats, especially when the number of events from one single trial may not be adequate to obtain vaccine effect estimates with a desired level of precision, it may be necessary to combine information across multiple trials. In this article, we propose a model formulation to estimate the direct, indirect, total, and overall vaccine effects combining data from trials with two types of study designs: individual-randomization and cluster-randomization, based on a Cox proportional hazards model, where the hazard of infection depends on both vaccine status of the individual as well as the vaccine status of the other individuals in the same cluster. We illustrate the use of the proposed model and assess the potential efficiency gain from combining data from multiple trials, compared to using data from each individual trial alone, through two simulation studies, one of which is designed based on a cholera vaccine trial previously carried out in Matlab, Bangladesh., (© 2024 John Wiley & Sons Ltd.)

A new mathematical model is designed and used to assess the impact of the newly-released Dengvaxia vaccine on the transmission dynamics of two co-circulating dengue strains (where strain 1 consists of dengue serotypes 1, 3 and 4; and strain 2 consists of dengue serotype 2). It is shown that the model exhibits the phenomenon of backward bifurcation when the disease-induced mortality in the host population exceeds a certain threshold value or if the vaccine does not provide perfect protection against infection with the two strains. In the absence of backward bifurcation, the disease-free equilibrium of the model is shown to be globally-asymptotically stable whenever the associated reproduction number is less than unity. It is shown that the community-wide use of the vaccine could induce positive, negative or no population-level impact, depending on the sign of a certain epidemiological threshold quantity (known as the vaccine impact factor). Simulations of the model, using data from Oaxaca, Mexico, show that, although the community-wide use of the vaccine will significantly reduce dengue burden in the community, it is unable to lead to the elimination of the two dengue strains. It is further shown that the use of Dengvaxia vaccine in dengue-naive populations may induce increased risk of severe disease in these populations., (Copyright © 2018 Elsevier Inc. All rights reserved.)

In this work we present a mathematical model that incorporates two Dengue serotypes. The model has been constructed to study both the epidemiological trends of the disease and conditions that allow coexistence in competing strains under vaccination. We consider two viral strains and temporary cross-immunity with one vector mosquito population. Results suggest that vaccination scenarios will not only reduce disease incidence but will also modify the transmission dynamics. Indeed, vaccination and cross immunity period are seen to decrease the frequency and magnitude of outbreaks but in a differentiated manner with specific effects depending upon the interaction vaccine and strain type., (Copyright © 2016 Elsevier Inc. All rights reserved.)

To estimate causal effects of vaccine on post-infection outcomes, Hudgens and Halloran (2006) defined a post-infection causal vaccine efficacy estimand VEI based on the principal stratification framework. They also derived closed forms for the maximum likelihood estimators of the causal estimand under some assumptions. Extending their research, we propose a Bayesian approach to estimating the causal vaccine effects on binary post-infection outcomes. The identifiability of the causal vaccine effect VEI is discussed under different assumptions on selection bias. The performance of the proposed Bayesian method is compared with the maximum likelihood method through simulation studies and two case studies - a clinical trial of a rotavirus vaccine candidate and a field study of pertussis vaccination. For both case studies, the Bayesian approach provided similar inference as the frequentist analysis. However, simulation studies with small sample sizes suggest that the Bayesian approach provides smaller bias and shorter confidence interval length., (Copyright © 2015 John Wiley & Sons, Ltd.)

Dengue vaccine development efforts have focused on the development of tetravalent vaccines. However, a recent Phase IIb trial of a tetravalent vaccine indicates a protective effect against only 3 of the 4 serotypes. While vaccines effective against a subset of serotypes may reduce morbidity and mortality, particular profiles could result in an increased number of cases due to immune enhancement and other peculiarities of dengue epidemiology. Here, we use a compartmental transmission model to assess the impact of partially effective vaccines in a hyperendemic Thai population. Crucially, we evaluate the effects that certain serotype heterogeneities may have in the presence of mass-vaccination campaigns. In the majority of scenarios explored, partially effective vaccines lead to 50% or greater reductions in the number of cases. This is true even of vaccines that we would not expect to proceed to licensure due to poor or incomplete immune responses. Our results show that a partially effective vaccine can have significant impacts on serotype distribution and mean age of cases., (Copyright © 2013 The Authors. Published by Elsevier Ltd.. All rights reserved.)