Institute of Public Safety Research, Tsinghua University, Beijing, China.
Department of Engineering Physics, Tsinghua University, Beijing, China.
BMC Infect Dis. 2021 Jan 12;21(1):58. doi: 10.1186/s12879-020-05750-9.
Testing is one of the most effective means to manage the COVID-19 pandemic. However, there is an upper bound on daily testing volume because of limited healthcare staff and working hours, as well as different testing methods, such as random testing and contact-tracking testing. In this study, a network-based epidemic transmission model combined with a testing mechanism was proposed to study the role of testing in epidemic control. The aim of this study was to determine how testing affects the spread of epidemics and the daily testing volume needed to control infectious diseases.
We simulated the epidemic spread process on complex networks and introduced testing preferences to describe different testing strategies. Different networks were generated to represent social contact between individuals. An extended susceptible-exposed-infected-recovered (SEIR) epidemic model was adopted to simulate the spread of epidemics in these networks. The model establishes a testing preference of between 0 and 1; the larger the testing preference, the higher the testing priority for people in close contact with confirmed cases.
The numerical simulations revealed that the higher the priority for testing individuals in close contact with confirmed cases, the smaller the infection scale. In addition, the infection peak decreased with an increase in daily testing volume and increased as the testing start time was delayed. We also discovered that when testing and other measures were adopted, the daily testing volume required to keep the infection scale below 5% was reduced by more than 40% even if other measures only reduced individuals' infection probability by 10%. The proposed model was validated using COVID-19 testing data.
Although testing could effectively inhibit the spread of infectious diseases and epidemics, our results indicated that it requires a huge daily testing volume. Thus, it is highly recommended that testing be adopted in combination with measures such as wearing masks and social distancing to better manage infectious diseases. Our research contributes to understanding the role of testing in epidemic control and provides useful suggestions for the government and individuals in responding to epidemics.
检测是管理 COVID-19 大流行的最有效手段之一。然而,由于医疗保健人员和工作时间有限,以及不同的检测方法(如随机检测和接触追踪检测),每日检测量存在上限。在这项研究中,提出了一种基于网络的传染病传播模型,并结合检测机制来研究检测在传染病控制中的作用。本研究旨在确定检测如何影响传染病的传播以及控制传染病所需的每日检测量。
我们在复杂网络上模拟了传染病的传播过程,并引入了检测偏好来描述不同的检测策略。不同的网络被用来表示个体之间的社会接触。采用扩展的易感-暴露-感染-恢复(SEIR)传染病模型来模拟这些网络中传染病的传播。该模型建立了 0 到 1 之间的检测偏好;与确诊病例密切接触的人,其检测优先级越高,检测偏好越大。
数值模拟结果表明,与确诊病例密切接触的人的检测优先级越高,感染规模越小。此外,随着每日检测量的增加和检测开始时间的延迟,感染峰值降低。我们还发现,当采用检测和其他措施时,即使其他措施仅将个体的感染概率降低 10%,将感染规模控制在 5%以下所需的每日检测量也减少了 40%以上。该模型使用 COVID-19 检测数据进行了验证。
虽然检测可以有效抑制传染病和疫情的传播,但我们的结果表明,它需要巨大的每日检测量。因此,强烈建议将检测与戴口罩和保持社交距离等措施结合使用,以更好地管理传染病。我们的研究有助于理解检测在传染病控制中的作用,并为政府和个人应对疫情提供有用的建议。