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COVID-19: Forecasting confirmed cases and deaths with a simple time series model
Forecasting the outcome of outbreaks as early and as accurately as possible is crucial for decision-making and policy implementations. A significant challenge faced by forecasters is that not all outbreaks and epidemics turn into pandemics, making the prediction of their severity difficult. At the s...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
International Institute of Forecasters. Published by Elsevier B.V.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7717777/ https://www.ncbi.nlm.nih.gov/pubmed/33311822 http://dx.doi.org/10.1016/j.ijforecast.2020.11.010 |
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author | Petropoulos, Fotios Makridakis, Spyros Stylianou, Neophytos |
author_facet | Petropoulos, Fotios Makridakis, Spyros Stylianou, Neophytos |
author_sort | Petropoulos, Fotios |
collection | PubMed |
description | Forecasting the outcome of outbreaks as early and as accurately as possible is crucial for decision-making and policy implementations. A significant challenge faced by forecasters is that not all outbreaks and epidemics turn into pandemics, making the prediction of their severity difficult. At the same time, the decisions made to enforce lockdowns and other mitigating interventions versus their socioeconomic consequences are not only hard to make, but also highly uncertain. The majority of modeling approaches to outbreaks, epidemics, and pandemics take an epidemiological approach that considers biological and disease processes. In this paper, we accept the limitations of forecasting to predict the long-term trajectory of an outbreak, and instead, we propose a statistical, time series approach to modelling and predicting the short-term behavior of COVID-19. Our model assumes a multiplicative trend, aiming to capture the continuation of the two variables we predict (global confirmed cases and deaths) as well as their uncertainty. We present the timeline of producing and evaluating 10-day-ahead forecasts over a period of four months. Our simple model offers competitive forecast accuracy and estimates of uncertainty that are useful and practically relevant. |
format | Online Article Text |
id | pubmed-7717777 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | International Institute of Forecasters. Published by Elsevier B.V. |
record_format | MEDLINE/PubMed |
spelling | pubmed-77177772020-12-07 COVID-19: Forecasting confirmed cases and deaths with a simple time series model Petropoulos, Fotios Makridakis, Spyros Stylianou, Neophytos Int J Forecast Article Forecasting the outcome of outbreaks as early and as accurately as possible is crucial for decision-making and policy implementations. A significant challenge faced by forecasters is that not all outbreaks and epidemics turn into pandemics, making the prediction of their severity difficult. At the same time, the decisions made to enforce lockdowns and other mitigating interventions versus their socioeconomic consequences are not only hard to make, but also highly uncertain. The majority of modeling approaches to outbreaks, epidemics, and pandemics take an epidemiological approach that considers biological and disease processes. In this paper, we accept the limitations of forecasting to predict the long-term trajectory of an outbreak, and instead, we propose a statistical, time series approach to modelling and predicting the short-term behavior of COVID-19. Our model assumes a multiplicative trend, aiming to capture the continuation of the two variables we predict (global confirmed cases and deaths) as well as their uncertainty. We present the timeline of producing and evaluating 10-day-ahead forecasts over a period of four months. Our simple model offers competitive forecast accuracy and estimates of uncertainty that are useful and practically relevant. International Institute of Forecasters. Published by Elsevier B.V. 2022 2020-12-04 /pmc/articles/PMC7717777/ /pubmed/33311822 http://dx.doi.org/10.1016/j.ijforecast.2020.11.010 Text en © 2020 International Institute of Forecasters. Published by Elsevier B.V. All rights reserved. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. |
spellingShingle | Article Petropoulos, Fotios Makridakis, Spyros Stylianou, Neophytos COVID-19: Forecasting confirmed cases and deaths with a simple time series model |
title | COVID-19: Forecasting confirmed cases and deaths with a simple time series model |
title_full | COVID-19: Forecasting confirmed cases and deaths with a simple time series model |
title_fullStr | COVID-19: Forecasting confirmed cases and deaths with a simple time series model |
title_full_unstemmed | COVID-19: Forecasting confirmed cases and deaths with a simple time series model |
title_short | COVID-19: Forecasting confirmed cases and deaths with a simple time series model |
title_sort | covid-19: forecasting confirmed cases and deaths with a simple time series model |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7717777/ https://www.ncbi.nlm.nih.gov/pubmed/33311822 http://dx.doi.org/10.1016/j.ijforecast.2020.11.010 |
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