背景:COVID-19病例的准确数量是控制流行病的基本知识。目前,估计COVID-19患者确切人数的最重要障碍之一是大量患者缺乏典型的临床症状,称为无症状感染。在这次系统审查中,我们纳入并评估了这些研究,这些研究主要集中在预测未发现的COVID-19发病率和死亡率以及繁殖数量,利用各种数学模型。
方法:本系统评价旨在研究COVID-19爆发中未发现感染的估计方法。PubMed的数据库,WebofScience,Scopus,科克伦,和Embase,搜索关键字的组合。应用纳入/排除标准,到2022年4月7日,所有检索到的英文文献都经过两步筛选过程进行了数据提取审查;首先,标题/摘要,然后是全文。这项研究与PRISMA检查表一致。
结果:在这项研究中,使用系统的搜索策略检索了61个文档。在对检索到的文章进行初步审查后,6篇文章被排除在外,其余55篇文章符合纳入标准,被纳入最终审查。大多数研究使用数学模型来估计漏报无症状感染病例的数量,更准确地评估发病率和患病率。已经使用各种数学模型研究了COVID-19的传播。将产出统计数据与从不同国家获得的官方统计数据进行了比较。尽管报告的患者人数低于估计人数,看来,数学计算可能是预测流行病和适当计划的有用措施。
结论:结论:我们的研究证明了数学模型在更精确地揭示新冠肺炎大流行的真正负担方面的有效性,准确的感染率和死亡率,和复制数字,因此,统计数学建模可能是衡量大流行感染的有害全球负担的有效工具。此外,它们可能是未来大流行的一种非常有用的方法,并将为医疗保健和公共卫生系统提供更准确和有效的信息。
The accurate number of COVID-19 cases is essential knowledge to control an epidemic. Currently, one of the most important obstacles in estimating the exact number of COVID-19 patients is the absence of typical clinical symptoms in a large number of people, called asymptomatic infections. In this systematic
review, we included and evaluated the studies mainly focusing on the prediction of undetected COVID-19 incidence and mortality rates as well as the reproduction numbers, utilizing various mathematical models.
This systematic
review aims to investigate the estimating methods of undetected infections in the COVID-19 outbreak. Databases of PubMed, Web of Science, Scopus, Cochrane, and Embase, were searched for a combination of keywords. Applying the inclusion/exclusion criteria, all retrieved English literature by April 7, 2022, were reviewed for data extraction through a two-step screening process; first, titles/abstracts, and then full-text. This study is consistent with the PRISMA checklist.
In this study, 61 documents were retrieved using a systematic search strategy. After an initial
review of retrieved articles, 6 articles were excluded and the remaining 55 articles met the inclusion criteria and were included in the final
review. Most of the studies used mathematical models to estimate the number of underreported asymptomatic infected cases, assessing incidence and prevalence rates more precisely. The spread of COVID-19 has been investigated using various mathematical models. The output statistics were compared with official statistics obtained from different countries. Although the number of reported patients was lower than the estimated numbers, it appeared that the mathematical calculations could be a useful measure to predict pandemics and proper planning.
In conclusion, our study demonstrates the effectiveness of mathematical models in unraveling the true burden of the COVID-19 pandemic in terms of more precise, and accurate infection and mortality rates, and reproduction numbers, thus, statistical mathematical modeling could be an effective tool for measuring the detrimental global burden of pandemic infections. Additionally, they could be a really useful method for future pandemics and would assist the healthcare and public health systems with more accurate and valid information.