Article ; Online: Forecasting of COVID-19 onset cases: a data-driven analysis in the early stage of delay.
Environmental science and pollution research international
2021 Volume 28, Issue 16, Page(s) 20240–20246
Abstract: ... many epidemiological models to predict the outbreak trend of COVID-19, but all use confirmed cases to predict "onset ... cases." The results not only show that our prediction shortens the delay of the second stage, but also ... a "predict-in-advance" method, used the number of "visiting hospital cases" to predict the number of "onset ...
| Abstract | The outbreak of COVID-19 has become a global public health event. Many researchers have proposed many epidemiological models to predict the outbreak trend of COVID-19, but all use confirmed cases to predict "onset cases." In this article, a total of 5434 cases were collected from National Health Commission and other provincial Health Commission in China, spanning from 1 December 2019 to 23 February 2020. We studied the delayed distribution of patients from onset to be confirmed. The delay is divided into two stages, which takes about 15 days or even longer. Therefore, considering the right truncation of the data, we proposed a "predict-in-advance" method, used the number of "visiting hospital cases" to predict the number of "onset cases." The results not only show that our prediction shortens the delay of the second stage, but also the predicted value of onset cases is quite close to the real value of onset cases, which can effectively predict the epidemic trend of sudden infectious diseases, and provide an important reference for the government to formulate control measures in advance. |
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| MeSH term(s) | COVID-19 ; China/epidemiology ; Forecasting ; Humans ; Models, Statistical ; SARS-CoV-2 | |||||
| Language | English | |||||
| Publishing date | 2021-01-06 | |||||
| Publishing country | Germany | |||||
| Document type | Journal Article | |||||
| ZDB-ID | 1178791-0 | |||||
| ISSN | 1614-7499 ; 0944-1344 | |||||
| ISSN (online) | 1614-7499 | |||||
| ISSN | 0944-1344 | |||||
| DOI | 10.1007/s11356-020-11859-w | |||||
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| Database | MEDical Literature Analysis and Retrieval System OnLINE |
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