Evidence map›Paper›PMID 38572058›Full record

ArticleInfectious Disease Modelling2024

Estimating geographic variation of infection fatality ratios during epidemics.

Joshua Ladau, Eoin L Brodie, Nicola Falco, Ishan Bansal, Elijah B Hoffman, Marcin P Joachimiak, Ana M Mora, Angelica M Walker, Haruko M Wainwright, Yulun Wu and 5 more

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Infectious Disease Modelling, 2024. 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, top 97% 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

0 citing papers in PubMed, 0 citations in OpenAlex.

No citing paper in PubMed yet.

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

15 authors at 8 institutions in 1 country.

Joshua LadauDepartments of Computational Precision Health and Dermatology, University of California, San Francisco, CA, 94115, USA.
Eoin L BrodieEarth and Environmental Sciences Area, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA.
Nicola FalcoEarth and Environmental Sciences Area, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA.
Ishan BansalComputational Biosciences Group, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA.
Elijah B HoffmanArva Intelligence, Inc., Salt Lake City, UT, 84101, USA.
Marcin P JoachimiakBiosystems Data Science, Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA.
Ana M MoraCenter for Environmental Research and Community Health (CERCH), School of Public Health, University of California, Berkeley, CA, 94720, USA.
Angelica M WalkerBredesen Center for Interdisciplinary Research and Graduate Education, University of Tennessee, Knoxville, TN, 37996, USA.
Haruko M WainwrightDepartment of Nuclear Science and Engineering, Massachusetts Institute of Technology, Boston, MA, 02139, USA.
Yulun WuGraduate Group in Biostatistics, University of California, Berkeley, CA, 94720, USA.
Mirko PavicicBiosciences, Oak Ridge National Laboratory, Oak Ridge, TN, 37830, USA.
Daniel JacobsonBiosciences, Oak Ridge National Laboratory, Oak Ridge, TN, 37830, USA.
Matthias HessUniversity of California, Davis, CA, 95616, USA.
James B BrownArva Intelligence, Inc., Salt Lake City, UT, 84101, USA.
Katrina AbuabaraDepartments of Computational Precision Health and Dermatology, University of California, San Francisco, CA, 94115, USA.
Lawrence Berkeley National Laboratory · USOak Ridge National Laboratory · USUniversity of California, Berkeley · USCenter for Environmental Health · USMassachusetts Institute of Technology · USUniversity of California, Davis · USUniversity of California, San Francisco · USUniversity of Tennessee at Knoxville · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objectives: We aim to estimate geographic variability in total numbers of infections and infection fatality ratios (IFR; the number of deaths caused by an infection per 1,000 infected people) when the availability and quality of data on disease burden are limited during an epidemic. Methods: We develop a noncentral hypergeometric framework that accounts for differential probabilities of positive tests and reflects the fact that symptomatic people are more likely to seek testing. We demonstrate the robustness, accuracy, and precision of this framework, and apply it to the United States (U.S.) COVID-19 pandemic to estimate county-level SARS-CoV-2 IFRs. Results: The estimators for the numbers of infections and IFRs showed high accuracy and precision; for instance, when applied to simulated validation data sets, across counties, Pearson correlation coefficients between estimator means and true values were 0.996 and 0.928, respectively, and they showed strong robustness to model misspecification. Applying the county-level estimators to the real, unsimulated COVID-19 data spanning April 1, 2020 to September 30, 2020 from across the U.S., we found that IFRs varied from 0 to 44.69, with a standard deviation of 3.55 and a median of 2.14. Conclusions: The proposed estimation framework can be used to identify geographic variation in IFRs across settings.

Indexed as

COVID-19Infection fatality rateInfection fatality ratioNoncentral hypergeometric distributionSARS-CoV-2

Identifiers

PMID38572058
PMCPMC10990719
OpenAlexW4392368656

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.