Estimates Probabilities of Success for Covid-19 Vaccines Using Mathematical Models

DOI

10.2991/assehr.k.220504.148How to use a DOI?

Keywords

Covid-19; Vaccines; Gender; Age; Probit Model

Abstract

To combat the COVID-19 pandemic, COVID-19 vaccine was approved for emergency use before the clinical trial phase was completed, but there has been no long-term and comprehensive review of safety data reported from vaccine trials. Therefore, this paper aims to use mathematical models to analyse resource reports of severe adverse events or deaths after vaccination in the United States. By October 2021, Vaccine side effects data reported by the Vaccine Adverse Event Reporting System were retrieved for severity or death after vaccination from Pfizer, Moderna, and Janssen. When the dependent variable is a binary variable (survival or death) and the sample size follows a normal distribution, the Probit model was used to compare the effects of gender, age, and type of vaccine on severe illness death after vaccination. Gender differences influence the frequency and severity of vaccine side effects, and it is necessary to analyze data by sex differences to ensure a specific and robust evidence base for efficacy and safety data. In addition, the safety and efficacy of COVID-19 vaccines in specific subpopulations, such as children and adolescents, pregnant women, and people with multiple underlying diseases, have not been thoroughly studied, and vaccinators should be strongly encouraged to provide more relevant data directly to drug regulatory authorities to calculate more accurate models and risk estimates.

Copyright

© 2022 The Authors. Published by Atlantis Press SARL.

Open Access

This is an open access article distributed under the CC BY-NC 4.0 license.

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TY  - CONF
AU  - Chenwei Wang
PY  - 2022
DA  - 2022/06/01
TI  - Estimates Probabilities of Success for Covid-19 Vaccines Using Mathematical Models
BT  - Proceedings of the 2022 8th International Conference on Humanities and Social Science Research (ICHSSR 2022)
PB  - Atlantis Press
SP  - 811
EP  - 814
SN  - 2352-5398
UR  - https://doi.org/10.2991/assehr.k.220504.148
DO  - 10.2991/assehr.k.220504.148
ID  - Wang2022
ER  -