Trend estimation and short-term forecasting of COVID-19 cases and deaths worldwide.


Journal

Proceedings of the National Academy of Sciences of the United States of America
ISSN: 1091-6490
Titre abrégé: Proc Natl Acad Sci U S A
Pays: United States
ID NLM: 7505876

Informations de publication

Date de publication:
09 08 2022
Historique:
entrez: 3 8 2022
pubmed: 4 8 2022
medline: 6 8 2022
Statut: ppublish

Résumé

Since the beginning of the COVID-19 pandemic, many dashboards have emerged as useful tools to monitor its evolution, inform the public, and assist governments in decision-making. Here, we present a globally applicable method, integrated in a daily updated dashboard that provides an estimate of the trend in the evolution of the number of cases and deaths from reported data of more than 200 countries and territories, as well as 7-d forecasts. One of the significant difficulties in managing a quickly propagating epidemic is that the details of the dynamic needed to forecast its evolution are obscured by the delays in the identification of cases and deaths and by irregular reporting. Our forecasting methodology substantially relies on estimating the underlying trend in the observed time series using robust seasonal trend decomposition techniques. This allows us to obtain forecasts with simple yet effective extrapolation methods in linear or log scale. We present the results of an assessment of our forecasting methodology and discuss its application to the production of global and regional risk maps.

Identifiants

pubmed: 35921436
doi: 10.1073/pnas.2112656119
pmc: PMC9371653
doi:

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

e2112656119

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Auteurs

Ekaterina Krymova (E)

Swiss Data Science Center, École Polytechnique Fédérale de Lausanne and Eidgenössische Technische Hochschule Zürich, 1015 Lausanne, Switzerland.

Benjamín Béjar (B)

Swiss Data Science Center, École Polytechnique Fédérale de Lausanne and Eidgenössische Technische Hochschule Zürich, 1015 Lausanne, Switzerland.

Dorina Thanou (D)

Center for Intelligent Systems, École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland.

Tao Sun (T)

Swiss Data Science Center, École Polytechnique Fédérale de Lausanne and Eidgenössische Technische Hochschule Zürich, 1015 Lausanne, Switzerland.

Elisa Manetti (E)

Institute of Global Health, Faculty of Medicine, University of Geneva, 1202 Geneva, Switzerland.

Gavin Lee (G)

Swiss Data Science Center, École Polytechnique Fédérale de Lausanne and Eidgenössische Technische Hochschule Zürich, 1015 Lausanne, Switzerland.

Kristen Namigai (K)

Institute of Global Health, Faculty of Medicine, University of Geneva, 1202 Geneva, Switzerland.

Christine Choirat (C)

Swiss Data Science Center, École Polytechnique Fédérale de Lausanne and Eidgenössische Technische Hochschule Zürich, 1015 Lausanne, Switzerland.

Antoine Flahault (A)

Institute of Global Health, Faculty of Medicine, University of Geneva, 1202 Geneva, Switzerland.

Guillaume Obozinski (G)

Swiss Data Science Center, École Polytechnique Fédérale de Lausanne and Eidgenössische Technische Hochschule Zürich, 1015 Lausanne, Switzerland.

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Classifications MeSH