Evidence map›Paper›PMID 33571534›Full record

ArticleMathematical biosciences2021

Measuring differences between phenomenological growth models applied to epidemiology.

Raimund Bürger, Gerardo Chowell, Leidy Yissedt Lara-Díaz

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Mathematical biosciences, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Article
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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

3 authors.

Raimund BürgerCI(2)MA and Departamento de Ingeniería Matemática, Facultad de Ciencias Físicas y Matemáticas, Universidad de Concepción, Casilla 160-C, Concepción, Chile. Electronic address: rburger@ing-mat.udec.cl.
Gerardo ChowellSchool of Public Health, Georgia State University, Atlanta, GA, USA; Simon A. Levin Mathematical and Computational Modeling Sciences Center, School of Human Evolution and Social Change, Arizona State University, Tempe, AZ 85287, USA; Division of International Epidemiology and Population Studies, Fogarty International Center, National Institutes of Health, Bethesda, MD 20892, USA.
Leidy Yissedt Lara-DíazCI(2)MA and Departamento de Ingeniería Matemática, Facultad de Ciencias Físicas y Matemáticas, Universidad de Concepción, Casilla 160-C, Concepción, Chile. Electronic address: llara@ing-mat.udec.cl.

Funding

Ebola modeling: behavior, asymptomatic infection, and contacts (2019-nCoV Admin Supplement)R01GM130900 · NIGMS · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI PORCO, TRAVIS CHRISTIAN · 2019 to 2022
$1.6M
National Science Foundation 2026797National Science Foundation 2034003NIGMS NIH HHS R01 GM130900
6 · The paper itself

Abstract

Phenomenological growth models (PGMs) provide a framework for characterizing epidemic trajectories, estimating key transmission parameters, gaining insight into the contribution of various transmission pathways, and providing long-term and short-term forecasts. Such models only require a small number of parameters to describe epidemic growth patterns. They can be expressed by an ordinary differential equation (ODE) of the type C

Indexed as

Epidemiologic MethodsModels, TheoreticalCOVID-19Disease OutbreaksHemorrhagic Fever, EbolaHumansInfluenza, HumanModels, StatisticalDistanceGompertz modelLogistic modelPhenomenological growth modelRichards modelSimulated annealing

Identifiers

PMID33571534
PMCPMC8054577

What OpenQuestion holds

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Registered trials

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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.