Related papers: Symmetric Vaccine Efficacy
We introduce an extension of nonparametric DS inference for arbitrary univariate CDFs to the case in which some failure times are (right)-censored, and then apply this to the problem of assessing evidence regarding assertions about relative…
Previous work on causal inference has primarily focused on averages and conditional averages of treatment effects, with significantly less attention on variability and uncertainty in individual treatment responses. In this paper, we…
Off-policy evaluation (OPE) constructs confidence intervals for the value of a target policy using data generated under a different behavior policy. Most existing inference methods focus on fixed target policies and may fail when the target…
We consider the problem of optimal allocation strategies for a (perfect) vaccine in an infinite-metapopulation model (including SIS, SIR, SEIR,. . .), when the loss function is given by the effective reproduction number Re, which is defined…
The Shapley value (SV) is adopted in various scenarios in machine learning (ML), including data valuation, agent valuation, and feature attribution, as it satisfies their fairness requirements. However, as exact SVs are infeasible to…
The advent and subsequent widespread availability of preventive vaccines has altered the course of public health over the past century. Despite this success, effective vaccines to prevent many high-burden diseases, including HIV, have been…
The rapid outbreak of bird flu challenges the outcome of effective vaccine for the upcoming years. The recent research established different norms to eliminate flu pandemics. This can be made possible with skilled experimental analyses and…
A full Bayesian approach to the estimation of Vaccine Efficacy is presented, which is an improvement over the currently used exact method conditional on the total number of cases. As an example, we reconsider the statistical sections of the…
To understand the impact of vaccination, we consider a SIVR (susceptible-infected-vaccinated-recovered) model which combines impulsive vaccination into the classical SIR model. The final size is firstly defined and estimated, and then the…
We introduce a new measure of antigenic distance between influenza A vaccine and circulating strains. The measure correlates well with efficacies of the H3N2 influenza A component of the annual vaccine between 1971 and 2004, as do results…
With the emergence and spread of infectious diseases with pandemic potential, such as COVID- 19, the urgency for vaccine development have led to unprecedented compressed and accelerated schedules that shortened the standard development…
Post-market safety surveillance is an integral part of mass vaccination programs. Typically relying on sequential analysis of real-world health data as they accrue, safety surveillance is challenged by the difficulty of sequential multiple…
Foundation models have become a dominant paradigm in machine learning, achieving remarkable performance across diverse tasks through large-scale pretraining. However, these models often yield overconfident, uncalibrated predictions. The…
The Sliced-Wasserstein (SW) distance between probability measures is defined as the average of the Wasserstein distances resulting for the associated one-dimensional projections. As a consequence, the SW distance can be written as an…
Real-world vaccine effectiveness has increasingly been studied using matching-based approaches, particularly in observational cohort studies following the target trial emulation framework. Although matching is appealing in its simplicity,…
Background: Despite lacking scientific support, vaccine hesitancy is widespread. While vaccine damage as a scientific fact is statistically highly uncommon, emerging social and technological forces have converted probable vaccine damage…
Two observational methods are currently being used to monitor post-deployment vaccine effectiveness: the obvious crude method comparing rate testing positive per head of vaccinated population with that rate per head of unvaccinated…
Randomization inference is a powerful tool in early phase vaccine trials when estimating the causal effect of a regimen against a placebo or another regimen. Randomization-based inference often focuses on testing either Fisher's sharp null…
We exploit the asymptotic normality of the extreme value theory (EVT) based estimators of the parameters of a symmetric L\'evy-stable distribution, to construct confidence intervals. The accuracy of these intervals is evaluated through a…
Explicit knowledge of total community-level immune seroprevalence is critical to developing policies to mitigate the social and clinical impact of SARS-CoV-2. Publicly available vaccination data are frequently cited as a proxy for…