Evidence map›Paper›PMID 38730261›Full record

ArticleScientific reports2024

Country-report pattern corrections of new cases allow accurate 2-week predictions of COVID-19 evolution with the Gompertz model.

I Villanueva, D Conesa, M Català, C López Cano, A Perramon-Malavez, D Molinuevo, V L de Rioja, D López, S Alonso, P J Cardona and 3 more

Abstract read
In one paragraph

Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

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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

13 authors.

I Villanueva *Department of Physics, Universitat Politècnica de Catalunya (BarcelonaTech), 08860, Castelldefels, Spain.
D Conesa *Department of Physics, Universitat Politècnica de Catalunya (BarcelonaTech), 08860, Castelldefels, Spain.
M CatalàNuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences (NDORMS), University of Oxford, Oxford, UK.
C López CanoDepartment of Physics, Universitat Politècnica de Catalunya (BarcelonaTech), 08860, Castelldefels, Spain.
A Perramon-MalavezDepartment of Physics, Universitat Politècnica de Catalunya (BarcelonaTech), 08860, Castelldefels, Spain.
D MolinuevoMedical Image Processing Lab, École Polytechnique Fédérale de Laussane, Geneva, Switzerland.
V L de RiojaDepartment of Physics, Universitat Politècnica de Catalunya (BarcelonaTech), 08860, Castelldefels, Spain.
D LópezDepartment of Physics, Universitat Politècnica de Catalunya (BarcelonaTech), 08860, Castelldefels, Spain.
S AlonsoDepartment of Physics, Universitat Politècnica de Catalunya (BarcelonaTech), 08860, Castelldefels, Spain.ORCID 0000-0002-3989-8757
P J CardonaMicrobiology Department, Laboratori Clínic Metropolitana Nord, Hospital Universitari Germans Trias i Pujol, Institut Universitari Germans Trias i Pujol (IGTP), Badalona, Catalonia, Spain.
C Montañola-SalesDepartment of Quantitative Methods, IQS School of Management, Universitat Ramon Llull, 08017, Barcelona, Spain.
C PratsDepartment of Physics, Universitat Politècnica de Catalunya (BarcelonaTech), 08860, Castelldefels, Spain.
E Alvarez-LacalleDepartment of Physics, Universitat Politècnica de Catalunya (BarcelonaTech), 08860, Castelldefels, Spain. enric.alvarez@upc.edu.ORCID 0000-0001-6824-6857

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Accurate short-term predictions of COVID-19 cases with empirical models allow Health Officials to prepare for hospital contingencies in a two-three week window given the delay between case reporting and the admission of patients in a hospital. We investigate the ability of Gompertz-type empiric models to provide accurate prediction up to two and three weeks to give a large window of preparation in case of a surge in virus transmission. We investigate the stability of the prediction and its accuracy using bi-weekly predictions during the last trimester of 2020 and 2021. Using data from 2020, we show that understanding and correcting for the daily reporting structure of cases in the different countries is key to accomplish accurate predictions. Furthermore, we found that filtering out predictions that are highly unstable to changes in the parameters of the model, which are roughly 20%, reduces strongly the number of predictions that are way-off. The method is then tested for robustness with data from 2021. We found that, for this data, only 1-2% of the one-week predictions were off by more than 50%. This increased to 3% for two-week predictions, and only for three-week predictions it reached 10%.

Indexed as

COVID-19SARS-CoV-2HumansModels, StatisticalTime Factors

Identifiers

PMID38730261
PMCPMC11087483

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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.