Department of Infectious Disease Epidemiology, TB Centre, The London School of Hygiene & Tropical Medicine, London, United Kingdom.
Department of Medicine, University of Cape Town, Cape Town, South Africa.
PLoS One. 2021 Jun 24;16(6):e0253096. doi: 10.1371/journal.pone.0253096. eCollection 2021.
In light of the role that airborne transmission plays in the spread of SARS-CoV-2, as well as the ongoing high global mortality from well-known airborne diseases such as tuberculosis and measles, there is an urgent need for practical ways of identifying congregate spaces where low ventilation levels contribute to high transmission risk. Poorly ventilated clinic spaces in particular may be high risk, due to the presence of both infectious and susceptible people. While relatively simple approaches to estimating ventilation rates exist, the approaches most frequently used in epidemiology cannot be used where occupancy varies, and so cannot be reliably applied in many of the types of spaces where they are most needed.
The aim of this study was to demonstrate the use of a non-steady state method to estimate the absolute ventilation rate, which can be applied in rooms where occupancy levels vary. We used data from a room in a primary healthcare clinic in a high TB and HIV prevalence setting, comprising indoor and outdoor carbon dioxide measurements and head counts (by age), taken over time. Two approaches were compared: approach 1 using a simple linear regression model and approach 2 using an ordinary differential equation model.
The absolute ventilation rate, Q, using approach 1 was 2407 l/s [95% CI: 1632-3181] and Q from approach 2 was 2743 l/s [95% CI: 2139-4429].
We demonstrate two methods that can be used to estimate ventilation rate in busy congregate settings, such as clinic waiting rooms. Both approaches produced comparable results, however the simple linear regression method has the advantage of not requiring room volume measurements. These methods can be used to identify poorly-ventilated spaces, allowing measures to be taken to reduce the airborne transmission of pathogens such as Mycobacterium tuberculosis, measles, and SARS-CoV-2.
鉴于空气传播在 SARS-CoV-2 传播中所起的作用,以及众所周知的空气传播疾病(如结核病和麻疹)造成的高死亡率,我们迫切需要找到实用的方法来识别人群聚集场所,这些场所通风不良,导致传播风险较高。通风不良的诊所空间尤其风险较高,因为那里既有感染源,又有易感人群。虽然存在一些相对简单的估算通风率的方法,但在人员占用率变化的情况下,流行病学中最常使用的方法无法使用,因此无法在许多最需要它们的场所类型中可靠地应用。
本研究旨在展示使用非稳态方法来估算绝对通风率,该方法可应用于人员占用率变化的房间。我们使用了在高结核病和 HIV 流行地区的一个初级保健诊所房间的数据,这些数据包括室内和室外二氧化碳测量值以及(按年龄)人头计数,随时间记录。比较了两种方法:方法 1 使用简单线性回归模型,方法 2 使用常微分方程模型。
使用方法 1 的绝对通风率 Q 为 2407 l/s [95% CI:1632-3181],使用方法 2 的 Q 为 2743 l/s [95% CI:2139-4429]。
我们展示了两种可用于估计繁忙人群聚集场所(如诊所候诊室)通风率的方法。两种方法都产生了类似的结果,但简单线性回归方法具有不需要测量房间体积的优点。这些方法可用于识别通风不良的空间,以便采取措施减少空气传播病原体(如结核分枝杆菌、麻疹和 SARS-CoV-2)的传播。