| Autor: |
Komal Tanwar, Nitesh Kumawat, Jai Prakash Tripathi, Sudipa Chauhan, Anuj Mubayi |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
|
| Zdroj: |
Royal Society Open Science, Vol 11, Iss 11 (2024) |
| Druh dokumentu: |
article |
| ISSN: |
2054-5703 |
| DOI: |
10.1098/rsos.240833 |
| Popis: |
The COVID-19 vaccine has been available in India since January 2021, although many individuals have refused to take the vaccine for various reasons. Vaccination plays a crucial role in disease control by preventing a substantial number of cases and associated disabilities. However, vaccine hesitancy poses a barrier that hinders these efforts. Our article presents a novel approach by proposing a mathematical model for COVID-19 that incorporates vaccine hesitancy, vaccine efficacy and behaviour compensation post-vaccination. The model is calibrated with COVID-19 incidence data for India from 13 February 2021 to 12 January 2022, using the Markov chain Monte Carlo method. The analysis examines the effects of hesitancy and social interventions through a series of practical simulations. The simulation results show that while COVID-19-infected individuals may have natural immunity, vaccination post-recovery is crucial to reduce cases by up to 64.1%. Social interventions, such as face masks and distancing, remain essential to prevent a rise in cases and ensure effective disease control. The model demonstrates that vaccination, combined with continued social interventions, is crucial for effectively reducing COVID-19 cases and preventing future outbreaks. Addressing vaccine hesitancy and maintaining preventive measures are key to successfully controlling the pandemic. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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