Evidence map›Paper›PMID 28570660›Full record

ArticlePLoS computational biology2017

Epidemiological and economic impact of pandemic influenza in Chicago: Priorities for vaccine interventions.

Nargesalsadat Dorratoltaj, Achla Marathe, Bryan L Lewis, Samarth Swarup, Stephen G Eubank, Kaja M Abbas

Open access · goldAbstract read
In one paragraph

Article in PLoS computational biology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed
2.4field-weighted citation impact, top 11% 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

14 citing papers in PubMed, 22 citations in OpenAlex.

  1. Article
  2. Review
  3. COVID-19-What Price Do Children Pay? An Analysis of Economic and Social Policy Factors.International journal of environmental research and public health · 2022
    Review
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors at 2 institutions in 1 country.

Nargesalsadat DorratoltajDepartment of Population Health Sciences, Virginia Tech, Blacksburg, VA, United States of America.
Achla MaratheNetwork Dynamics and Simulation Science Lab, Biocomplexity Institute, Virginia Tech, Blacksburg, VA, United States of America.
Bryan L LewisNetwork Dynamics and Simulation Science Lab, Biocomplexity Institute, Virginia Tech, Blacksburg, VA, United States of America.ORCID http://orcid.org/0000-0003-0793-6082
Samarth SwarupNetwork Dynamics and Simulation Science Lab, Biocomplexity Institute, Virginia Tech, Blacksburg, VA, United States of America.
Stephen G EubankDepartment of Population Health Sciences, Virginia Tech, Blacksburg, VA, United States of America.
Kaja M AbbasDepartment of Population Health Sciences, Virginia Tech, Blacksburg, VA, United States of America.ORCID http://orcid.org/0000-0003-0563-1576
Biocom · USVirginia Tech · US

Funding

Systems Analysis of Social Pathways of Epidemics to Reduce Health DisparitiesR01GM109718 · NIGMS · VIRGINIA POLYTECHNIC INST AND ST UNIV · PI MARATHE, ACHLA, VULLIKANTI, ANIL · 2014 to 2022
$3.6M
NIGMS NIH HHS R01 GM109718
6 · The paper itself

Abstract

The study objective is to estimate the epidemiological and economic impact of vaccine interventions during influenza pandemics in Chicago, and assist in vaccine intervention priorities. Scenarios of delay in vaccine introduction with limited vaccine efficacy and limited supplies are not unlikely in future influenza pandemics, as in the 2009 H1N1 influenza pandemic. We simulated influenza pandemics in Chicago using agent-based transmission dynamic modeling. Population was distributed among high-risk and non-high risk among 0-19, 20-64 and 65+ years subpopulations. Different attack rate scenarios for catastrophic (30.15%), strong (21.96%), and moderate (11.73%) influenza pandemics were compared against vaccine intervention scenarios, at 40% coverage, 40% efficacy, and unit cost of $28.62. Sensitivity analysis for vaccine compliance, vaccine efficacy and vaccine start date was also conducted. Vaccine prioritization criteria include risk of death, total deaths, net benefits, and return on investment. The risk of death is the highest among the high-risk 65+ years subpopulation in the catastrophic influenza pandemic, and highest among the high-risk 0-19 years subpopulation in the strong and moderate influenza pandemics. The proportion of total deaths and net benefits are the highest among the high-risk 20-64 years subpopulation in the catastrophic, strong and moderate influenza pandemics. The return on investment is the highest in the high-risk 0-19 years subpopulation in the catastrophic, strong and moderate influenza pandemics. Based on risk of death and return on investment, high-risk groups of the three age group subpopulations can be prioritized for vaccination, and the vaccine interventions are cost saving for all age and risk groups. The attack rates among the children are higher than among the adults and seniors in the catastrophic, strong, and moderate influenza pandemic scenarios, due to their larger social contact network and homophilous interactions in school. Based on return on investment and higher attack rates among children, we recommend prioritizing children (0-19 years) and seniors (65+ years) after high-risk groups for influenza vaccination during times of limited vaccine supplies. Based on risk of death, we recommend prioritizing seniors (65+ years) after high-risk groups for influenza vaccination during times of limited vaccine supplies.

Indexed as

Influenza, HumanPandemicsAdolescentAdultAgedChicagoChildChild, PreschoolComputational BiologyHumansInfantInfant, NewbornInfluenza VaccinesMiddle AgedModels, StatisticalVaccinationInfluenza Vaccines

Identifiers

PMID28570660
PMCPMC5453424
OpenAlexW2620700860

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.