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Trend estimation and short-term forecasting of COVID-19 cases and deaths worldwide
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 t...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
National Academy of Sciences
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9371653/ https://www.ncbi.nlm.nih.gov/pubmed/35921436 http://dx.doi.org/10.1073/pnas.2112656119 |
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author | Krymova, Ekaterina Béjar, Benjamín Thanou, Dorina Sun, Tao Manetti, Elisa Lee, Gavin Namigai, Kristen Choirat, Christine Flahault, Antoine Obozinski, Guillaume |
author_facet | Krymova, Ekaterina Béjar, Benjamín Thanou, Dorina Sun, Tao Manetti, Elisa Lee, Gavin Namigai, Kristen Choirat, Christine Flahault, Antoine Obozinski, Guillaume |
author_sort | Krymova, Ekaterina |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-9371653 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | National Academy of Sciences |
record_format | MEDLINE/PubMed |
spelling | pubmed-93716532023-02-03 Trend estimation and short-term forecasting of COVID-19 cases and deaths worldwide Krymova, Ekaterina Béjar, Benjamín Thanou, Dorina Sun, Tao Manetti, Elisa Lee, Gavin Namigai, Kristen Choirat, Christine Flahault, Antoine Obozinski, Guillaume Proc Natl Acad Sci U S A Physical Sciences 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. National Academy of Sciences 2022-08-03 2022-08-09 /pmc/articles/PMC9371653/ /pubmed/35921436 http://dx.doi.org/10.1073/pnas.2112656119 Text en Copyright © 2022 the Author(s). Published by PNAS. https://creativecommons.org/licenses/by-nc-nd/4.0/This article is distributed under Creative Commons Attribution-NonCommercial-NoDerivatives License 4.0 (CC BY-NC-ND) (https://creativecommons.org/licenses/by-nc-nd/4.0/) . |
spellingShingle | Physical Sciences Krymova, Ekaterina Béjar, Benjamín Thanou, Dorina Sun, Tao Manetti, Elisa Lee, Gavin Namigai, Kristen Choirat, Christine Flahault, Antoine Obozinski, Guillaume Trend estimation and short-term forecasting of COVID-19 cases and deaths worldwide |
title | Trend estimation and short-term forecasting of COVID-19 cases and deaths worldwide |
title_full | Trend estimation and short-term forecasting of COVID-19 cases and deaths worldwide |
title_fullStr | Trend estimation and short-term forecasting of COVID-19 cases and deaths worldwide |
title_full_unstemmed | Trend estimation and short-term forecasting of COVID-19 cases and deaths worldwide |
title_short | Trend estimation and short-term forecasting of COVID-19 cases and deaths worldwide |
title_sort | trend estimation and short-term forecasting of covid-19 cases and deaths worldwide |
topic | Physical Sciences |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9371653/ https://www.ncbi.nlm.nih.gov/pubmed/35921436 http://dx.doi.org/10.1073/pnas.2112656119 |
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