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An Age-dependent model for the spread of COVID-19 with immune-status-dependent contact restrictions

Ein altersabhängiges Modell zur Verbreitung von COVID−19 mit Immustatusabhängigen Kontaktbeschränkungen

  • COVID-19 presented a global challenge as healthcare facilities in many nations became overburdened. Initial strategies were centered on achieving herd immunity through vaccination campaigns, with empirical studies on vaccine efficacy playing a pivotal role in informing public health initiatives, including the prioritization of high-risk individuals for vaccination and the execution of vaccination campaigns. The thesis presents a concise formulation of an age-dependent deterministic mathematical COVID-19 model that incorporates the delta variant and reflects immune-status-dependent contact restrictions, specifically the G-rules (3G-rule, 2G-rule, and 2G plus-rule). A key component of COVID-19 management in Germany is the implementation of the G-rules, which require the vaccination of individuals, and presenting regular COVID-19 test results, or a recovery certificate to access certain locations. This study investigates the impact of immune-status-dependent contact reductions in Germany during the fourth COVID-19 wave, known as the 'delta wave'. It presents a simplified yet realistic model for evaluating COVID-19 measures during this wave, with a particular emphasis on the contentious immune-status-dependent contact reductions that sparked global societal controversy and political debates due to the societal divide between vaccinated and unvaccinated individuals. Similar policies, often referred to as "health passes" or "green passes," have been implemented in various countries, such as France, Italy, and the UK.

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Metadaten
Author:Patience Bwanu Iliya
Advisor:Kristan Schneider, Baaske Franka
Document Type:Master's Thesis
Language:English
Date of Publication (online):2024/07/18
Year of first Publication:2024
Publishing Institution:Hochschule Mittweida
Granting Institution:Hochschule Mittweida
Date of final exam:2024/03/12
Release Date:2024/07/18
GND Keyword:COVID-19
Page Number:194
Institutes:Angewandte Computer‐ und Bio­wissen­schaften
DDC classes:614.5924144 COVID-19