Novel statistics predict the COVID‐19 pandemic could terminate in 2022
Many people want to know when the COVID‐19 pandemic will end and life will return to normal. This question is highly elusive and distinct predictions have been proposed. In this study, the global mortality and case fatality rate of COVID‐19 were analyzed using nonlinear regression. The analysis show...
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Published in | Journal of medical virology Vol. 94; no. 6; pp. 2845 - 2848 |
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Main Author | |
Format | Journal Article |
Language | English |
Published |
United States
Wiley Subscription Services, Inc
01.06.2022
John Wiley and Sons Inc |
Subjects | |
Online Access | Get full text |
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Summary: | Many people want to know when the COVID‐19 pandemic will end and life will return to normal. This question is highly elusive and distinct predictions have been proposed. In this study, the global mortality and case fatality rate of COVID‐19 were analyzed using nonlinear regression. The analysis showed that the COVID‐19 pandemic could terminate in 2022, but COVID‐19 could be one or two times more deadly than seasonal influenza by 2023. The prediction considered the possibility of the emergence of new variants of SARS‐CoV‐2 and was supported by the features of the Omicron variant and other facts. As the herd immunity against COVID‐19 established through natural infections and mass vaccination is distinct among countries, COVID‐19 could be more or less deadly in some countries in the coming years than the prediction. Although the future of COVID‐19 will have multiple possibilities, this statistics‐based prediction could aid to make proper decisions and establish an example on the prediction of infectious diseases.
Highlights
The global mortality and case fatality rate of COVID‐19 were statistically analyzed.
The analysis provided novel statistics for the prediction of the COVID‐19 pandemic.
The COVID‐19 pandemic could terminate in 2022.
COVID‐19 could be one or two times more deadly than seasonal influenza by 2023. |
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Bibliography: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 |
ISSN: | 0146-6615 1096-9071 1096-9071 |
DOI: | 10.1002/jmv.27661 |