Abstract
Simple SummaryThis article aims to study the COVID-19 data for New York City. We use both the daily number of second dose vaccination and the daily number of reported cases for New York City. This article provides a method to combine an epidemic model and such data. We explore the influence of vaccine efficacy on our results.In this article we study the efficacy of vaccination in epidemiological reconstructions of COVID-19 epidemics from reported cases data. Given an epidemiological model, we developed in previous studies a method that allowed the computation of an instantaneous transmission rate that produced an exact fit of reported cases data of the COVID-19 outbreak. In this article, we improve the method by incorporating vaccination data. More precisely, we develop a model in which vaccination is variable in its effectiveness. We develop a new technique to compute the transmission rate in this model, which produces an exact fit to reported cases data, while quantifying the efficacy of the vaccine and the daily number of vaccinated. We apply our method to the reported cases data and vaccination data of New York City.
Highlights
Developing vaccines against an infectious agent often requires years of research and testing to ensure efficacy and safety
This rapid development has left many open questions whose answers may affect the usefulness of the epidemiological models proposed for COVID-19 outbreak
The blue background color regions correspond to epidemic ην phases, and the yellow background color regions to endemic phases
Summary
Developing vaccines against an infectious agent often requires years of research and testing to ensure efficacy and safety. In the case of COVID-19, the vaccines took less than a year to develop and deploy. This rapid development has left many open questions whose answers may affect the usefulness of the epidemiological models proposed for COVID-19 outbreak. Mathematical modeling has been used since the beginning of the COVID-19 pandemic to investigate the validity of parameters, to predict its development, and to compare different containment scenarios. In previous studies [5,6,7], we developed a new method to identify the transmission rate and the instantaneous reproduction number for various
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