Vaccine optimization for COVID-19 : who to vaccinate first?
A vaccine, when available, will likely become our best tool to control the current COVID-19 pandemic. Even in the most optimistic scenarios, vaccine shortages will likely occur. Using an age-stratified mathematical model paired with optimization algorithms, we determined optimal vaccine allocation for four different metrics (deaths, symptomatic infections, and maximum non-ICU and ICU hospitalizations) under many scenarios. We find that a vaccine with effectiveness ≥50% would be enough to substantially mitigate the ongoing pandemic provided that a high percentage of the population is optimally vaccinated. When minimizing deaths, we find that for low vaccine effectiveness, irrespective of vaccination coverage, it is optimal to allocate vaccine to high-risk (older) age-groups first. In contrast, for higher vaccine effectiveness, there is a switch to allocate vaccine to high-transmission (younger) age-groups first for high vaccination coverage. While there are other societal and ethical considerations, this work can provide an evidence-based rationale for vaccine prioritization.
| Errataetall: | |
|---|---|
| Medienart: |
E-Artikel |
| Erscheinungsjahr: |
2020 |
|---|---|
| Erschienen: |
2020 |
| Enthalten in: |
Zur Gesamtaufnahme - year:2020 |
|---|---|
| Enthalten in: |
medRxiv : the preprint server for health sciences - (2020) vom: 15. Dez. |
| Sprache: |
Englisch |
|---|
| Beteiligte Personen: |
Matrajt, Laura [VerfasserIn] |
|---|
| Links: |
|---|
| Themen: |
|---|
| Anmerkungen: |
Date Revised 10.11.2023 published: Electronic UpdateIn: Sci Adv. 2021 Feb 3;7(6):. - PMID 33536223 Citation Status PubMed-not-MEDLINE |
|---|
| doi: |
10.1101/2020.08.14.20175257 |
|---|
| funding: |
|
|---|---|
| Förderinstitution / Projekttitel: |
|
| PPN (Katalog-ID): |
NLM313917817 |
|---|
| LEADER | 01000naa a22002652 4500 | ||
|---|---|---|---|
| 001 | NLM313917817 | ||
| 003 | DE-627 | ||
| 005 | 20231225151617.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 231225s2020 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.1101/2020.08.14.20175257 |2 doi | |
| 028 | 5 | 2 | |a pubmed24n1046.xml |
| 035 | |a (DE-627)NLM313917817 | ||
| 035 | |a (NLM)32817963 | ||
| 035 | |a (PII)2020.08.14.20175257 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 100 | 1 | |a Matrajt, Laura |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Vaccine optimization for COVID-19 |b who to vaccinate first? |
| 264 | 1 | |c 2020 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a ƒaComputermedien |b c |2 rdamedia | ||
| 338 | |a ƒa Online-Ressource |b cr |2 rdacarrier | ||
| 500 | |a Date Revised 10.11.2023 | ||
| 500 | |a published: Electronic | ||
| 500 | |a UpdateIn: Sci Adv. 2021 Feb 3;7(6):. - PMID 33536223 | ||
| 500 | |a Citation Status PubMed-not-MEDLINE | ||
| 520 | |a A vaccine, when available, will likely become our best tool to control the current COVID-19 pandemic. Even in the most optimistic scenarios, vaccine shortages will likely occur. Using an age-stratified mathematical model paired with optimization algorithms, we determined optimal vaccine allocation for four different metrics (deaths, symptomatic infections, and maximum non-ICU and ICU hospitalizations) under many scenarios. We find that a vaccine with effectiveness ≥50% would be enough to substantially mitigate the ongoing pandemic provided that a high percentage of the population is optimally vaccinated. When minimizing deaths, we find that for low vaccine effectiveness, irrespective of vaccination coverage, it is optimal to allocate vaccine to high-risk (older) age-groups first. In contrast, for higher vaccine effectiveness, there is a switch to allocate vaccine to high-transmission (younger) age-groups first for high vaccination coverage. While there are other societal and ethical considerations, this work can provide an evidence-based rationale for vaccine prioritization | ||
| 650 | 4 | |a Preprint | |
| 700 | 1 | |a Eaton, Julia |e verfasserin |4 aut | |
| 700 | 1 | |a Leung, Tiffany |e verfasserin |4 aut | |
| 700 | 1 | |a Brown, Elizabeth R |e verfasserin |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t medRxiv : the preprint server for health sciences |d 2020 |g (2020) vom: 15. Dez. |w (DE-627)NLM310900166 |7 nnns |
| 773 | 1 | 8 | |g year:2020 |g day:15 |g month:12 |
| 856 | 4 | 0 | |u http://dx.doi.org/10.1101/2020.08.14.20175257 |3 Volltext |
| 912 | |a GBV_USEFLAG_A | ||
| 912 | |a GBV_NLM | ||
| 951 | |a AR | ||
| 952 | |j 2020 |b 15 |c 12 | ||