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India’s pragmatic vaccination strategy against COVID-19: a mathematical modelling-based analysis
OBJECTIVES: To investigate the impact of targeted vaccination strategies on morbidity and mortality due to COVID-19, as well as on the incidence of SARS-CoV-2, in India. DESIGN: Mathematical modelling. SETTINGS: Indian epidemic of COVID-19 and vulnerable population. DATA SOURCES: Country-specific an...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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BMJ Publishing Group
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8257292/ https://www.ncbi.nlm.nih.gov/pubmed/34215611 http://dx.doi.org/10.1136/bmjopen-2021-048874 |
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author | Mandal, Sandip Arinaminpathy, Nimalan Bhargava, Balram Panda, Samiran |
author_facet | Mandal, Sandip Arinaminpathy, Nimalan Bhargava, Balram Panda, Samiran |
author_sort | Mandal, Sandip |
collection | PubMed |
description | OBJECTIVES: To investigate the impact of targeted vaccination strategies on morbidity and mortality due to COVID-19, as well as on the incidence of SARS-CoV-2, in India. DESIGN: Mathematical modelling. SETTINGS: Indian epidemic of COVID-19 and vulnerable population. DATA SOURCES: Country-specific and age-segregated pattern of social contact, case fatality rate and demographic data obtained from peer-reviewed literature and public domain. MODEL: An age-structured dynamical model describing SARS-CoV-2 transmission in India incorporating uncertainty in natural history parameters was constructed. INTERVENTIONS: Comparison of different vaccine strategies by targeting priority groups such as keyworkers including healthcare professionals, individuals with comorbidities (24–60 years old) and all above 60. MAIN OUTCOME MEASURES: Incidence reduction and averted deaths in different scenarios, assuming that the current restrictions are fully lifted as vaccination is implemented. RESULTS: The priority groups together account for about 18% of India’s population. An infection-preventing vaccine with 60% efficacy covering all these groups would reduce peak symptomatic incidence by 20.6% (95% uncertainty intervals (UI) 16.7–25.4) and cumulative mortality by 29.7% (95% CrI 25.8–33.8). A similar vaccine with ability to prevent symptoms (but not infection) will reduce peak incidence of symptomatic cases by 10.4% (95% CrI 8.4–13.0) and cumulative mortality by 32.9% (95% CrI 28.6–37.3). In the event of insufficient vaccine supply to cover all priority groups, model projections suggest that after keyworkers, vaccine strategy should prioritise all who are >60 and subsequently individuals with comorbidities. In settings with weakest transmission, such as sparsely populated rural areas, those with comorbidities should be prioritised after keyworkers. CONCLUSIONS: An appropriately targeted vaccination strategy would witness substantial mitigation of impact of COVID-19 in a country like India with wide heterogeneity. ‘Smart vaccination’, based on public health considerations, rather than mass vaccination, appears prudent. |
format | Online Article Text |
id | pubmed-8257292 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | BMJ Publishing Group |
record_format | MEDLINE/PubMed |
spelling | pubmed-82572922021-07-09 India’s pragmatic vaccination strategy against COVID-19: a mathematical modelling-based analysis Mandal, Sandip Arinaminpathy, Nimalan Bhargava, Balram Panda, Samiran BMJ Open Health Policy OBJECTIVES: To investigate the impact of targeted vaccination strategies on morbidity and mortality due to COVID-19, as well as on the incidence of SARS-CoV-2, in India. DESIGN: Mathematical modelling. SETTINGS: Indian epidemic of COVID-19 and vulnerable population. DATA SOURCES: Country-specific and age-segregated pattern of social contact, case fatality rate and demographic data obtained from peer-reviewed literature and public domain. MODEL: An age-structured dynamical model describing SARS-CoV-2 transmission in India incorporating uncertainty in natural history parameters was constructed. INTERVENTIONS: Comparison of different vaccine strategies by targeting priority groups such as keyworkers including healthcare professionals, individuals with comorbidities (24–60 years old) and all above 60. MAIN OUTCOME MEASURES: Incidence reduction and averted deaths in different scenarios, assuming that the current restrictions are fully lifted as vaccination is implemented. RESULTS: The priority groups together account for about 18% of India’s population. An infection-preventing vaccine with 60% efficacy covering all these groups would reduce peak symptomatic incidence by 20.6% (95% uncertainty intervals (UI) 16.7–25.4) and cumulative mortality by 29.7% (95% CrI 25.8–33.8). A similar vaccine with ability to prevent symptoms (but not infection) will reduce peak incidence of symptomatic cases by 10.4% (95% CrI 8.4–13.0) and cumulative mortality by 32.9% (95% CrI 28.6–37.3). In the event of insufficient vaccine supply to cover all priority groups, model projections suggest that after keyworkers, vaccine strategy should prioritise all who are >60 and subsequently individuals with comorbidities. In settings with weakest transmission, such as sparsely populated rural areas, those with comorbidities should be prioritised after keyworkers. CONCLUSIONS: An appropriately targeted vaccination strategy would witness substantial mitigation of impact of COVID-19 in a country like India with wide heterogeneity. ‘Smart vaccination’, based on public health considerations, rather than mass vaccination, appears prudent. BMJ Publishing Group 2021-07-02 /pmc/articles/PMC8257292/ /pubmed/34215611 http://dx.doi.org/10.1136/bmjopen-2021-048874 Text en © Author(s) (or their employer(s)) 2021. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ. https://creativecommons.org/licenses/by-nc/4.0/This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/ (https://creativecommons.org/licenses/by-nc/4.0/) . |
spellingShingle | Health Policy Mandal, Sandip Arinaminpathy, Nimalan Bhargava, Balram Panda, Samiran India’s pragmatic vaccination strategy against COVID-19: a mathematical modelling-based analysis |
title | India’s pragmatic vaccination strategy against COVID-19: a mathematical modelling-based analysis |
title_full | India’s pragmatic vaccination strategy against COVID-19: a mathematical modelling-based analysis |
title_fullStr | India’s pragmatic vaccination strategy against COVID-19: a mathematical modelling-based analysis |
title_full_unstemmed | India’s pragmatic vaccination strategy against COVID-19: a mathematical modelling-based analysis |
title_short | India’s pragmatic vaccination strategy against COVID-19: a mathematical modelling-based analysis |
title_sort | india’s pragmatic vaccination strategy against covid-19: a mathematical modelling-based analysis |
topic | Health Policy |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8257292/ https://www.ncbi.nlm.nih.gov/pubmed/34215611 http://dx.doi.org/10.1136/bmjopen-2021-048874 |
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