Projecting the potential impact of an Omicron XBB.1.5 wave in Shanghai, China
Abstract China experienced a major nationwide wave of SARS-CoV-2 infections in December 2022, immediately after lifting strict interventions, despite the majority of the population having already received inactivated COVID-19 vaccines. Due to the rapid waning of protection and the emergence of Omicron XBB.1.5, the risk of another COVID-19 wave remains high. It is still unclear whether the health care system will be able to manage the demand during this potential XBB.1.5 wave and if the number of associated deaths can be reduced to a level comparable to that of seasonal influenza. Thus, we developed a mathematical model of XBB.1.5 transmission using Shanghai as a case study. We found that a potential XBB.1.5 wave is less likely to overwhelm the health care system and would result in a death toll comparable to that of seasonal influenza, albeit still larger, especially among elderly individuals. Our analyses show that a combination of vaccines and antiviral drugs can effectively mitigate an XBB.1.5 epidemic, with a projected number of deaths of 2.08 per 10,000 individuals.This figure corresponds to a 70–80% decrease compared to the previous Omicron wave and is comparable to the level of seasonal influenza. The peak prevalence of hospital admissions and ICU admissions are projected at 28.89 and 2.28 per 10,000 individuals, respectively, suggesting the need for a moderate increase in the capacity of the health care system. Our findings emphasize the importance of improving vaccination coverage, particularly among the older population, and the use of antiviral treatments..
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Preprint| Erscheinungsjahr: |
2023 |
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| Erschienen: |
2023 |
| Enthalten in: |
bioRxiv.org - (2023) vom: 16. Mai Zur Gesamtaufnahme - year:2023 |
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| Sprache: |
Englisch |
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| Beteiligte Personen: |
Liu, Hengcong [VerfasserIn] |
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| Links: |
Volltext [kostenfrei] |
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| doi: |
10.1101/2023.05.10.23289761 |
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| PPN (Katalog-ID): |
XBI039570800 |
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| 520 | |a Abstract China experienced a major nationwide wave of SARS-CoV-2 infections in December 2022, immediately after lifting strict interventions, despite the majority of the population having already received inactivated COVID-19 vaccines. Due to the rapid waning of protection and the emergence of Omicron XBB.1.5, the risk of another COVID-19 wave remains high. It is still unclear whether the health care system will be able to manage the demand during this potential XBB.1.5 wave and if the number of associated deaths can be reduced to a level comparable to that of seasonal influenza. Thus, we developed a mathematical model of XBB.1.5 transmission using Shanghai as a case study. We found that a potential XBB.1.5 wave is less likely to overwhelm the health care system and would result in a death toll comparable to that of seasonal influenza, albeit still larger, especially among elderly individuals. Our analyses show that a combination of vaccines and antiviral drugs can effectively mitigate an XBB.1.5 epidemic, with a projected number of deaths of 2.08 per 10,000 individuals.This figure corresponds to a 70–80% decrease compared to the previous Omicron wave and is comparable to the level of seasonal influenza. The peak prevalence of hospital admissions and ICU admissions are projected at 28.89 and 2.28 per 10,000 individuals, respectively, suggesting the need for a moderate increase in the capacity of the health care system. Our findings emphasize the importance of improving vaccination coverage, particularly among the older population, and the use of antiviral treatments. | ||
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| 700 | 1 | |a Deng, Xiaowei |4 aut | |
| 700 | 1 | |a Hu, Zexin |4 aut | |
| 700 | 1 | |a Sun, Ruijia |4 aut | |
| 700 | 1 | |a Zou, Junyi |4 aut | |
| 700 | 1 | |a Dong, Jiayi |4 aut | |
| 700 | 1 | |a Wu, Qianhui |4 aut | |
| 700 | 1 | |a Chen, Xinhua |4 aut | |
| 700 | 1 | |a Yi, Lan |4 aut | |
| 700 | 1 | |a Cai, Jun |0 (orcid)0000-0001-9495-1226 |4 aut | |
| 700 | 1 | |a Zhang, Juanjuan |4 aut | |
| 700 | 1 | |a Ajelli, Marco |4 aut | |
| 700 | 1 | |a Yu, Hongjie |4 aut | |
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