Evidence map›Paper›PMID 34253200›Full record

ArticleBMC medicine2021

Optimal SARS-CoV-2 vaccine allocation using real-time attack-rate estimates in Rhode Island and Massachusetts.

Thu Nguyen-Anh Tran, Nathan B Wikle, Emmy Albert, Haider Inam, Emily Strong, Karel Brinda, Scott M Leighow, Fuhan Yang, Sajid Hossain, Justin R Pritchard and 4 more

Open access · goldAbstract read
In one paragraph

Article in BMC medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
31citing papers in PubMed, 2 pooled it
2.4field-weighted citation impact, top 9% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

31 citing papers in PubMed, 2 syntheses or guidelines pooled it, 41 citations in OpenAlex.

  1. Pooled it
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  10. Influenza vaccination allocation in tropical settings under constrained resources.medRxiv : the preprint server for health sciences · 2024
    Article
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

14 authors at 4 institutions in 1 country.

Thu Nguyen-Anh TranCenter for Infectious Disease Dynamics, Department of Biology, Pennsylvania State University, University Park, PA, USA. tnt9@psu.edu.ORCID 0000-0001-6789-5900
Nathan B WikleCenter for Infectious Disease Dynamics, Department of Statistics, Pennsylvania State University, University Park, PA, USA.
Emmy AlbertDepartment of Physics, Pennsylvania State University, University Park, PA, USA.
Haider InamCenter for Infectious Disease Dynamics, Department of Bioengineering, Pennsylvania State University, University Park, PA, USA.
Emily StrongCenter for Infectious Disease Dynamics, Department of Statistics, Pennsylvania State University, University Park, PA, USA.
Karel BrindaCenter for Communicable Disease Dynamics, Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, MA, USA.
Scott M LeighowCenter for Infectious Disease Dynamics, Department of Bioengineering, Pennsylvania State University, University Park, PA, USA.
Fuhan YangCenter for Infectious Disease Dynamics, Department of Biology, Pennsylvania State University, University Park, PA, USA.
Sajid HossainYale School of Medicine, Yale University, New Haven, CT, USA.
Justin R PritchardCenter for Infectious Disease Dynamics, Department of Bioengineering, Pennsylvania State University, University Park, PA, USA.
Philip ChanDepartment of Medicine, Brown University, Providence, RI, USA.
William P HanageCenter for Communicable Disease Dynamics, Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, MA, USA.
Ephraim M HanksCenter for Infectious Disease Dynamics, Department of Statistics, Pennsylvania State University, University Park, PA, USA.
Maciej F BoniCenter for Infectious Disease Dynamics, Department of Biology, Pennsylvania State University, University Park, PA, USA.
Pennsylvania State University · USCenter for Disease Dynamics, Economics & Policy · USBrown University · USYale University · US

Funding

Understanding and controlling antibiotic resistanceU54GM088558 · NIGMS · HARVARD SCHOOL OF PUBLIC HEALTH · PI LIPSITCH, MARC · 2009 to 2018
$26.4M
Short-Term Reserch Training: Students in Health SchoolsT35HL007649 · NHLBI · YALE UNIVERSITY · PI Sarwat I Chaudhry, Erica L Herzog · 1987 to 2026
$5.7M
Epidemiology of Infectious Diseases and BiodefenseT32AI007535 · NIAID · HARVARD UNIVERSITY (SCH OF PUBLIC HLTH) · PI GRAD, YONATAN H, MURRAY, MEGAN B · 1998 to 2025
$4.8M
Evolutionary Tradeoffs in Antibiotic ResistanceR35GM133700 · NIGMS · HARVARD MEDICAL SCHOOL · PI BAYM, MICHAEL · 2019 to 2023
$2.4M
NHLBI NIH HHS T35 HL007649NIAID NIH HHS T32 AI007535NIGMS NIH HHS R35 GM133700NIGMS NIH HHS R35GM133700NIGMS NIH HHS U54 GM088558
6 · The paper itself

Abstract

backgroundWhen three SARS-CoV-2 vaccines came to market in Europe and North America in the winter of 2020-2021, distribution networks were in a race against a major epidemiological wave of SARS-CoV-2 that began in autumn 2020. Rapid and optimized vaccine allocation was critical during this time. With 95% efficacy reported for two of the vaccines, near-term public health needs likely require that distribution is prioritized to the elderly, health care workers, teachers, essential workers, and individuals with comorbidities putting them at risk of severe clinical progression.

methodsWe evaluate various age-based vaccine distributions using a validated mathematical model based on current epidemic trends in Rhode Island and Massachusetts. We allow for varying waning efficacy of vaccine-induced immunity, as this has not yet been measured. We account for the fact that known COVID-positive cases may not have been included in the first round of vaccination. And, we account for age-specific immune patterns in both states at the time of the start of the vaccination program. Our analysis assumes that health systems during winter 2020-2021 had equal staffing and capacity to previous phases of the SARS-CoV-2 epidemic; we do not consider the effects of understaffed hospitals or unvaccinated medical staff.

resultsWe find that allocating a substantial proportion (>75%) of vaccine supply to individuals over the age of 70 is optimal in terms of reducing total cumulative deaths through mid-2021. This result is robust to different profiles of waning vaccine efficacy and several different assumptions on age mixing during and after lockdown periods. As we do not explicitly model other high-mortality groups, our results on vaccine allocation apply to all groups at high risk of mortality if infected. A median of 327 to 340 deaths can be avoided in Rhode Island (3444 to 3647 in Massachusetts) by optimizing vaccine allocation and vaccinating the elderly first. The vaccination campaigns are expected to save a median of 639 to 664 lives in Rhode Island and 6278 to 6618 lives in Massachusetts in the first half of 2021 when compared to a scenario with no vaccine. A policy of vaccinating only seronegative individuals avoids redundancy in vaccine use on individuals that may already be immune, and would result in 0.5% to 1% reductions in cumulative hospitalizations and deaths by mid-2021.

conclusionsAssuming high vaccination coverage (>28%) and no major changes in distancing, masking, gathering size, hygiene guidelines, and virus transmissibility between 1 January 2021 and 1 July 2021 a combination of vaccination and population immunity may lead to low or near-zero transmission levels by the second quarter of 2021.

Indexed as

COVID-19VaccinationVaccination CoverageAge FactorsCommunicable Disease ControlCOVID-19 VaccinesHealth Care RationingHumansIncidenceMassachusettsModels, TheoreticalPublic HealthResource AllocationRhode IslandSARS-CoV-2COVID-19 VaccinesMathematical modelingOptimal vaccine allocationReal-time seroprevalenceSARS-CoV-2Vaccination

Identifiers

PMID34253200
PMCPMC8275456
OpenAlexW3178105258

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.