Optimality of maximal-effort vaccination
It is widely acknowledged that vaccinating at maximal effort in the face of an ongoing epidemic is the best strategy to minimise infections and deaths from the disease. Despite this, no one has proved that this is guaranteed to be true if the disease follows multi-group SIR (Susceptible–Infected–Rec...
| Main Authors: | , |
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| פורמט: | Journal article |
| שפה: | English |
| יצא לאור: |
Springer Nature
2023
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| _version_ | 1826310289070489600 |
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| author | Penn, MJ Donnelly, CA |
| author_facet | Penn, MJ Donnelly, CA |
| author_sort | Penn, MJ |
| collection | OXFORD |
| description | It is widely acknowledged that vaccinating at maximal effort in the face of an ongoing epidemic is the best strategy to minimise infections and deaths from the disease. Despite this, no one has proved that this is guaranteed to be true if the disease follows multi-group SIR (Susceptible–Infected–Recovered) dynamics. This paper provides a novel proof of this principle for the existing SIR framework, showing that the total number of deaths or infections from an epidemic is decreasing in vaccination effort. Furthermore, it presents a novel model for vaccination which assumes that vaccines assigned to a subgroup are distributed randomly to the unvaccinated population of that subgroup. It suggests, using COVID-19 data, that this more accurately captures vaccination dynamics than the model commonly found in the literature. However, as the novel model provides a strictly larger set of possible vaccination policies, the results presented in this paper hold for both models. |
| first_indexed | 2024-03-07T07:48:17Z |
| format | Journal article |
| id | oxford-uuid:4a3be161-5755-4ab1-8df9-6629836137b5 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-07T07:48:17Z |
| publishDate | 2023 |
| publisher | Springer Nature |
| record_format | dspace |
| spelling | oxford-uuid:4a3be161-5755-4ab1-8df9-6629836137b52023-07-03T14:32:43ZOptimality of maximal-effort vaccinationJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:4a3be161-5755-4ab1-8df9-6629836137b5EnglishSymplectic ElementsSpringer Nature2023Penn, MJDonnelly, CAIt is widely acknowledged that vaccinating at maximal effort in the face of an ongoing epidemic is the best strategy to minimise infections and deaths from the disease. Despite this, no one has proved that this is guaranteed to be true if the disease follows multi-group SIR (Susceptible–Infected–Recovered) dynamics. This paper provides a novel proof of this principle for the existing SIR framework, showing that the total number of deaths or infections from an epidemic is decreasing in vaccination effort. Furthermore, it presents a novel model for vaccination which assumes that vaccines assigned to a subgroup are distributed randomly to the unvaccinated population of that subgroup. It suggests, using COVID-19 data, that this more accurately captures vaccination dynamics than the model commonly found in the literature. However, as the novel model provides a strictly larger set of possible vaccination policies, the results presented in this paper hold for both models. |
| spellingShingle | Penn, MJ Donnelly, CA Optimality of maximal-effort vaccination |
| title | Optimality of maximal-effort vaccination |
| title_full | Optimality of maximal-effort vaccination |
| title_fullStr | Optimality of maximal-effort vaccination |
| title_full_unstemmed | Optimality of maximal-effort vaccination |
| title_short | Optimality of maximal-effort vaccination |
| title_sort | optimality of maximal effort vaccination |
| work_keys_str_mv | AT pennmj optimalityofmaximaleffortvaccination AT donnellyca optimalityofmaximaleffortvaccination |