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Tracking and forecasting milepost moments of the epidemic in the early-outbreak: framework and applications to the COVID-19
Background: The outbreak of the 2019 novel coronavirus (COVID-19) has attracted global attention. In the early stage of the outbreak, the most important question concerns some meaningful milepost moments, including the time when the number of daily confirmed cases decreases, the time when the number...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
F1000 Research Limited
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7737706/ https://www.ncbi.nlm.nih.gov/pubmed/33363716 http://dx.doi.org/10.12688/f1000research.23107.2 |
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author | Wang, Huiwen Zhang, Yanwen Lu, Shan Wang, Shanshan |
author_facet | Wang, Huiwen Zhang, Yanwen Lu, Shan Wang, Shanshan |
author_sort | Wang, Huiwen |
collection | PubMed |
description | Background: The outbreak of the 2019 novel coronavirus (COVID-19) has attracted global attention. In the early stage of the outbreak, the most important question concerns some meaningful milepost moments, including the time when the number of daily confirmed cases decreases, the time when the number of daily confirmed cases becomes smaller than that of the daily removed (recovered and death), and the time when the number of daily confirmed cases and patients treated in hospital, which can be called “active cases”, becomes zero. Unfortunately, it is extremely difficult to make right and precise prediction due to the limited amount of available data at the early stage of the outbreak. To address it, in this paper, we propose a flexible framework incorporating the effectiveness of the government control to forecast the whole process of a new unknown infectious disease in its early-outbreak. Methods: We first establish the iconic indicators to characterize the extent of epidemic spread. Then we develop the tracking and forecasting procedure with mild and reasonable assumptions. Finally we apply it to analyze and evaluate the COVID-19 outbreak using the public available data for mainland China beyond Hubei Province from the China Centers for Disease Control (CDC) during the period of Jan 29th, 2020, to Feb 29th, 2020, which shows the effectiveness of the proposed procedure. Results: Forecasting results indicate that the number of newly confirmed cases will become zero in the mid-early March, and the number of patients treated in the hospital will become zero between mid-March and mid-April in mainland China beyond Hubei Province. Conclusions: The framework proposed in this paper can help people get a general understanding of the epidemic trends in countries where COVID-19 are raging as well as any other outbreaks of new and unknown infectious diseases in the future. |
format | Online Article Text |
id | pubmed-7737706 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | F1000 Research Limited |
record_format | MEDLINE/PubMed |
spelling | pubmed-77377062020-12-23 Tracking and forecasting milepost moments of the epidemic in the early-outbreak: framework and applications to the COVID-19 Wang, Huiwen Zhang, Yanwen Lu, Shan Wang, Shanshan F1000Res Research Article Background: The outbreak of the 2019 novel coronavirus (COVID-19) has attracted global attention. In the early stage of the outbreak, the most important question concerns some meaningful milepost moments, including the time when the number of daily confirmed cases decreases, the time when the number of daily confirmed cases becomes smaller than that of the daily removed (recovered and death), and the time when the number of daily confirmed cases and patients treated in hospital, which can be called “active cases”, becomes zero. Unfortunately, it is extremely difficult to make right and precise prediction due to the limited amount of available data at the early stage of the outbreak. To address it, in this paper, we propose a flexible framework incorporating the effectiveness of the government control to forecast the whole process of a new unknown infectious disease in its early-outbreak. Methods: We first establish the iconic indicators to characterize the extent of epidemic spread. Then we develop the tracking and forecasting procedure with mild and reasonable assumptions. Finally we apply it to analyze and evaluate the COVID-19 outbreak using the public available data for mainland China beyond Hubei Province from the China Centers for Disease Control (CDC) during the period of Jan 29th, 2020, to Feb 29th, 2020, which shows the effectiveness of the proposed procedure. Results: Forecasting results indicate that the number of newly confirmed cases will become zero in the mid-early March, and the number of patients treated in the hospital will become zero between mid-March and mid-April in mainland China beyond Hubei Province. Conclusions: The framework proposed in this paper can help people get a general understanding of the epidemic trends in countries where COVID-19 are raging as well as any other outbreaks of new and unknown infectious diseases in the future. F1000 Research Limited 2020-09-18 /pmc/articles/PMC7737706/ /pubmed/33363716 http://dx.doi.org/10.12688/f1000research.23107.2 Text en Copyright: © 2020 Wang H et al. http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Wang, Huiwen Zhang, Yanwen Lu, Shan Wang, Shanshan Tracking and forecasting milepost moments of the epidemic in the early-outbreak: framework and applications to the COVID-19 |
title | Tracking and forecasting milepost moments of the epidemic in the early-outbreak: framework and applications to the COVID-19 |
title_full | Tracking and forecasting milepost moments of the epidemic in the early-outbreak: framework and applications to the COVID-19 |
title_fullStr | Tracking and forecasting milepost moments of the epidemic in the early-outbreak: framework and applications to the COVID-19 |
title_full_unstemmed | Tracking and forecasting milepost moments of the epidemic in the early-outbreak: framework and applications to the COVID-19 |
title_short | Tracking and forecasting milepost moments of the epidemic in the early-outbreak: framework and applications to the COVID-19 |
title_sort | tracking and forecasting milepost moments of the epidemic in the early-outbreak: framework and applications to the covid-19 |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7737706/ https://www.ncbi.nlm.nih.gov/pubmed/33363716 http://dx.doi.org/10.12688/f1000research.23107.2 |
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