Evidence map›Paper›PMID 35139067›Full record

ArticlePLoS computational biology2022

There are no equal opportunity infectors: Epidemiological modelers must rethink our approach to inequality in infection risk.

Jon Zelner, Nina B Masters, Ramya Naraharisetti, Sanyu A Mojola, Merlin Chowkwanyun, Ryan Malosh

Open access · goldAbstract read
In one paragraph

Article in PLoS computational biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.

0numbers the graph read from it
0cells of the map it votes in
29citing papers in PubMed
–field-weighted citation impact, top 100% 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

29 citing papers in PubMed, 0 citations in OpenAlex.

  1. Review
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  7. Closing the Gap in Race-based Inequities for Seasonal Influenza Hospitalizations: A Modeling Study.Clinical infectious diseases : an official publication of the Infectious Diseases Society of America · 2025
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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 3 institutions in 1 country.

Jon ZelnerDept. of Epidemiology, University of Michigan School of Public Health, Ann Arbor, Michigan, United States of America.ORCID 0000-0002-0708-2008
Nina B MastersDept. of Epidemiology, University of Michigan School of Public Health, Ann Arbor, Michigan, United States of America.ORCID 0000-0002-3155-6058
Ramya NaraharisettiDept. of Epidemiology, University of Michigan School of Public Health, Ann Arbor, Michigan, United States of America.ORCID 0000-0001-7324-8195
Sanyu A MojolaDept. of Sociology, School of Public and International Affairs & Office of Population Research, Princeton University, Princeton, New Jersey, United States of America.ORCID 0000-0002-2610-2625
Merlin ChowkwanyunDept. of Sociomedical Sciences, Mailman School of Public Health, Columbia University, New York, New York, United States of America.
Ryan MaloshDept. of Epidemiology, University of Michigan School of Public Health, Ann Arbor, Michigan, United States of America.ORCID 0000-0003-3546-5935
University of Michigan · USColumbia University · USPrinceton University · US

Funding

IP20-003, Data driven transmission models to optimize influenza vaccination and pandemic mitigation strategies - COVID-19 SupplementU01IP001138 · IP · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI ZELNER, JONATHAN L · 2020 to 2024
$3.4M
NCIRD CDC HHS U01 IP001138
6 · The paper itself

Abstract

Mathematical models have come to play a key role in global pandemic preparedness and outbreak response: helping to plan for disease burden, hospital capacity, and inform nonpharmaceutical interventions. Such models have played a pivotal role in the COVID-19 pandemic, with transmission models-and, by consequence, modelers-guiding global, national, and local responses to SARS-CoV-2. However, these models have largely not accounted for the social and structural factors, which lead to socioeconomic, racial, and geographic health disparities. In this piece, we raise and attempt to clarify several questions relating to this important gap in the research and practice of infectious disease modeling: Why do epidemiologic models of emerging infections typically ignore known structural drivers of disparate health outcomes? What have been the consequences of a framework focused primarily on aggregate outcomes on infection equity? What should be done to develop a more holistic approach to modeling-based decision-making during pandemics? In this review, we evaluate potential historical and political explanations for the exclusion of drivers of disparity in infectious disease models for emerging infections, which have often been characterized as "equal opportunity infectors" despite ample evidence to the contrary. We look to examples from other disease systems (HIV, STIs) and successes in including social inequity in models of acute infection transmission as a blueprint for how social connections, environmental, and structural factors can be integrated into a coherent, rigorous, and interpretable modeling framework. We conclude by outlining principles to guide modeling of emerging infections in ways that represent the causes of inequity in infection as central rather than peripheral mechanisms.

Indexed as

Health EquityInfectionsModels, StatisticalSocioeconomic FactorsComputational BiologyCOVID-19Disease OutbreaksHumansSARS-CoV-2

Identifiers

PMID35139067
PMCPMC8827449
OpenAlexW3196658365

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.