Jennifer Loria, Vinicius V. L. Albani, Francisco A. B. Coutinho, Dimas T. Covas, Claudio J. Struchiner, Jorge P. Zubelli, Eduardo Massad.
Abstract
In this paper we calculate the variation of the estimated vaccine efficacy (VE) due to the time-dependent force of infection resulting from the difference between the moment the Clinical Trial (CT) begins and the peak in the outbreak intensity. Using a simple mathematical model we tested the hypothesis that the time difference between the moment the CT begins and the peak in the outbreak intensity determines substantially different values for VE. We exemplify the method with the case of the VE efficacy estimation for one of the vaccines against the new coronavirus SARS-CoV-2.
Introduction
Vaccines play a central role in our aptitude to prevent diseases. Indeed, in a highly connected world, it acts as a fundamental tool to decrease the multiscale interaction that enhances the propagation of viruses between individuals and populations. For the last couple of centuries, vaccine campaigns have proved to be the most effective medical strategy to reduce death and morbidity caused by infectious diseases.
Material and Methods:
One possible way to explain vaccine efficacy
In this section we propose a simple model to explain what vaccine efficacy is. This model considers that a test is performed ending with INFECTED-VACCINATED individuals and INFECTED-NOT-VACCINATED individuals.
Discussion
In this paper we tested the hypothesis that the time difference between the moment the CT begins and the peak in the outbreak intensity results in substantially different values for VE. We tested this hypothesis with a mathematical model and exemplify the method with the case of the VE efficacy estimation for one of the vaccines against the new coronavirus SARS-CoV-2.
Citation: Loria J, Albani VVL, Coutinho FAB, Covas DT, Struchiner CJ, Zubelli JP, et al. (2023) Time-dependent vaccine efficacy estimation quantified by a mathematical model. PLoS ONE 18(5): e0285466. https://doi.org/10.1371/journal.pone.0285466
Editor: Dimitris A. Goussis, Khalifa University of Science Technology - Abu Dhabi Campus: Khalifa University of Science and Technology, UNITED ARAB EMIRATES
Received: March 3, 2023; Accepted: April 23, 2023; Published: May 11, 2023.
Copyright: © 2023 Loria et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: The code and data used in the article are available in https://github.com/JennySorio/Vaccine_Efficacy.
Funding: JPZ & EM were supported by the Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq http://www.cnpq.br) [grant numbers 305544/2011-0 and 307873/2013-7], EM, VA, DC, JL, FC were supported the Fundação Butantan (https://fundacaobutantan.org.br) [grant number 01/2020], JPZ the Fundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro (http://www.faperj.gov.br) [grant number E-26/202.927/2017], VA was supported by the Fundação de Amparo à Pesquisa e Inovação do Estado de Santa Catarina (https://www.fapesc.sc.gov.br) [grant number 00002847/2021], and JL was supported by the Universidad de Costa Rica (UCR https://www.ucr.ac.cr/) [grant number OAICE-CAB-02-022-2016]. Except for Fundação Butantan, the funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Fundação Butantan provided the data, but had no other role in study design, analysis, decision to publish, or preparation of the manuscript.
Competing interests: The authors have declared that no competing interests exist.
