利用变异效应评估 2022 年中国 SARS-CoV-2 症状比例的飙升:模型分析

IF 8.8 3区 医学 Q1 Medicine Infectious Disease Modelling Pub Date : 2024-03-11 DOI:10.1016/j.idm.2024.02.011
Salihu S. Musa , Shi Zhao , Ismail Abdulrashid , Sania Qureshi , Andrés Colubri , Daihai He
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引用次数: 0

摘要

尽管大多数 COVID-19 感染者并无症状,但 2022 年底,中国大陆的有症状病例却大幅增加。在本研究中,我们使用基于 SIR 的概念模型研究了中国 COVID-19 无症状病例的突然激增。我们的模型考虑了 SARS-CoV-2 的流行病学特征,尤其是非药物干预(面部遮蔽和社交距离)、人口统计学和中国大陆的疾病死亡率带来的变异。中国出现症状的比例增加可能是由于:(1) 冬季的敏感性和易感性较高;(2) SARS-CoV-2 感染高峰(高传播性)导致病毒吸入增加。这两个原因可以解释 2022 年 12 月中国 COVID-19 的高症状比例。因此,我们的研究可作为加强 SARS-CoV-2 防控工作的决策支持工具。因此,我们强调面罩诱发的变异有可能降低感染者的传染性而非严重性。然而,要了解变异对疾病严重程度的影响还需要进一步的研究。
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Evaluating the spike in the symptomatic proportion of SARS-CoV-2 in China in 2022 with variolation effects: a modeling analysis

Despite most COVID-19 infections being asymptomatic, mainland China had a high increase in symptomatic cases at the end of 2022. In this study, we examine China's sudden COVID-19 symptomatic surge using a conceptual SIR-based model. Our model considers the epidemiological characteristics of SARS-CoV-2, particularly variolation, from non-pharmaceutical intervention (facial masking and social distance), demography, and disease mortality in mainland China. The increase in symptomatic proportions in China may be attributable to (1) higher sensitivity and vulnerability during winter and (2) enhanced viral inhalation due to spikes in SARS-CoV-2 infections (high transmissibility). These two reasons could explain China's high symptomatic proportion of COVID-19 in December 2022. Our study, therefore, can serve as a decision-support tool to enhance SARS-CoV-2 prevention and control efforts. Thus, we highlight that facemask-induced variolation could potentially reduces transmissibility rather than severity in infected individuals. However, further investigation is required to understand the variolation effect on disease severity.

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来源期刊
Infectious Disease Modelling
Infectious Disease Modelling Mathematics-Applied Mathematics
CiteScore
17.00
自引率
3.40%
发文量
73
审稿时长
17 weeks
期刊介绍: Infectious Disease Modelling is an open access journal that undergoes peer-review. Its main objective is to facilitate research that combines mathematical modelling, retrieval and analysis of infection disease data, and public health decision support. The journal actively encourages original research that improves this interface, as well as review articles that highlight innovative methodologies relevant to data collection, informatics, and policy making in the field of public health.
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