Evidence map›Paper›PMID 33469599›Full record

ArticlemedRxiv : the preprint server for health sciences2021

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

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

Open access · greenAbstract readPreprint
PubMed Full text at PMC Publisher
In one paragraph

Article in medRxiv : the preprint server for health sciences, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
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

0 citing papers in PubMed, 10 citations in OpenAlex.

No citing paper in PubMed yet.

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.
Nathan WikleCenter for Infectious Disease Dynamics, Department of Statistics, Pennsylvania State University, University Park, PA.
Joseph AlbertDepartment of Physics, Pennsylvania State University, University Park, PA.
Haider InamCenter for Infectious Disease Dynamics, Department of Bioengineering, Pennsylvania State University, University Park, PA.
Emily StrongCenter for Infectious Disease Dynamics, Department of Statistics, Pennsylvania State University, University Park, PA.
Karel BrindaCenter for Communicable Disease Dynamics, Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, MA.
Scott M LeighowCenter for Infectious Disease Dynamics, Department of Bioengineering, Pennsylvania State University, University Park, PA.
Fuhan YangCenter for Infectious Disease Dynamics, Department of Biology, Pennsylvania State University, University Park, PA.
Sajid HossainYale School of Medicine, Yale University, New Haven, CT.
Justin R PritchardCenter for Infectious Disease Dynamics, Department of Bioengineering, Pennsylvania State University, University Park, PA.
Philip ChanDepartment of Medicine, Brown University, Providence, RI.
William P HanageCenter for Communicable Disease Dynamics, Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, MA.
Ephraim M HanksCenter for Infectious Disease Dynamics, Department of Statistics, Pennsylvania State University, University Park, PA.
Maciej F BoniCenter for Infectious Disease Dynamics, Department of Biology, Pennsylvania State University, University Park, PA.
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
Evolutionary Tradeoffs in Antibiotic ResistanceR35GM133700 · NIGMS · HARVARD MEDICAL SCHOOL · PI BAYM, MICHAEL · 2019 to 2023
$2.4M
NIAID NIH HHS HHSN272201400007CNIGMS NIH HHS R35 GM133700NIGMS NIH HHS U54 GM088558
6 · The paper itself

Abstract

As three SARS-CoV-2 vaccines come to market in Europe and North America in the winter of 2020-2021, distribution networks will be in a race against a major epidemiological wave of SARS-CoV-2 that began in autumn 2020. Rapid and optimized vaccine allocation is critical during this time. With 95% efficacy reported for two of the vaccines, near-term public health needs require that distribution is prioritized to the elderly, health-care workers, teachers, essential workers, and individuals with co-morbidities putting them at risk of severe clinical progression. Here, we 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 be included in the first round of vaccination. And, we account for current age-specific immune patterns in both states. We find that allocating a substantial proportion (

Identifiers

PMID33469599
PMCPMC7814845
OpenAlexW3119106910

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.