The Impact of COVID-19 Vaccination Delay: A Modelling Study for Chicago and NYC Data
Vinicius V. L. Albani, Jennifer Loria, Eduardo Massad, Jorge P., Zubelli

TL;DR
This study models the effects of COVID-19 vaccination delays in Chicago and NYC, showing that earlier vaccination significantly reduces cases, hospitalizations, and deaths, with impacts varying based on local infection dynamics.
Contribution
It introduces a time-dependent SEIR-like model tailored to Chicago and NYC data, forecasting vaccination scenarios and quantifying delay impacts on health outcomes.
Findings
Delayed vaccination increases mortality and hospitalizations.
Earlier vaccination campaigns significantly reduce COVID-19 cases.
Impact of delays varies with local infection patterns.
Abstract
Background: By the beginning of December 2020, some vaccines against COVID-19 already presented efficacy and security, which qualify them to be used in mass vaccination campaigns. Thus, setting up strategies of vaccination became crucial to control the COVID19 pandemic. Methods: We use daily COVID-19 reports from Chicago and NYC from 01-Mar2020 to 28- Nov-2020 to estimate the parameters of an SEIR-like epidemiological model that accounts for different severity levels. To achieve data adherent predictions, we let the model parameters to be time-dependent. The model is used to forecast different vaccination scenarios, where the campaign starts at different dates, from 01-Oct-2020 to 01-Apr-2021. To generate realistic scenarios, disease control strategies are implemented whenever the number of predicted daily hospitalizations reaches a preset threshold. Results: The model reproduces the…
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Taxonomy
TopicsCOVID-19 epidemiological studies · Vaccine Coverage and Hesitancy · Data-Driven Disease Surveillance
