Bradford Infection Group, School of Engineering, Design and Technology, University of Bradford, Bradford, BD7 1DP, UK.
BMC Infect Dis. 2010 Aug 20;10:247. doi: 10.1186/1471-2334-10-247.
Although many infections that are transmissible from person to person are acquired through direct contact between individuals, a minority, notably pulmonary tuberculosis (TB), measles and influenza are known to be spread by the airborne route. Airborne infections pose a particular threat to susceptible individuals whenever they are placed together with the index case in confined spaces. With this in mind, waiting areas of healthcare facilities present a particular challenge, since large numbers of people, some of whom may have underlying conditions which predispose them to infection, congregate in such spaces and can be exposed to an individual who may be shedding potentially pathogenic microorganisms. It is therefore important to understand the risks posed by infectious individuals in waiting areas, so that interventions can be developed to minimise the spread of airborne infections.
A stochastic Monte Carlo model was constructed to analyse the transmission of airborne infection in a hypothetical 132 m3 hospital waiting area in which occupancy levels, waiting times and ventilation rate can all be varied. In the model the Gammaitoni-Nucci equation was utilized to predict probability of susceptible individuals becoming infected. The model was used to assess the risk of transmission of three infectious diseases, TB, influenza and measles. In order to allow for stochasticity a random number generator was applied to the variables in the model and a total of 10000 individual simulations were undertaken. The mean quanta production rates used in the study were 12.7, 100 and 570 per hour for TB, influenza and measles, respectively.
The results of the study revealed the mean probability of acquiring a TB infection during a 30-minute stay in the waiting area to be negligible (i.e. 0.0034), while that for influenza was an order of magnitude higher at 0.0262. By comparison the mean probability of acquiring a measles infection during the same period was 0.1349. If the duration of the stay was increased to 60 minutes then these values increased to 0.0087, 0.0662 and 0.3094, respectively.
Under normal circumstances the risk of acquiring a TB infection during a visit to a hospital waiting area is minimal. Likewise the risks associated with the transmission of influenza, although an order of magnitude greater than those for TB, are relatively small. By comparison, the risks associated with measles are high. While the installation of air disinfection may be beneficial, when seeking to prevent the transmission of airborne viral infection it is important to first minimize waiting times and the number of susceptible individuals present before turning to expensive technological solutions.
虽然许多可在人与人之间传播的感染是通过个体之间的直接接触获得的,但少数感染,特别是肺结核(TB)、麻疹和流感,已知通过空气传播途径传播。当易感个体与索引病例一起处于封闭空间时,空气传播感染对他们构成了特别的威胁。考虑到这一点,医疗机构的等候区特别具有挑战性,因为大量的人,其中一些人可能有潜在的条件使他们容易感染,聚集在这样的空间中,并且可能接触到可能正在释放潜在致病微生物的个体。因此,了解等候区中传染性个体带来的风险非常重要,以便制定干预措施,最大限度地减少空气传播感染的传播。
构建了一个随机的蒙特卡罗模型来分析在一个假设的 132m3 医院等候区中空气传播感染的传播情况,其中可以改变占用水平、等候时间和通风率。在模型中,利用 Gammaitoni-Nucci 方程来预测易感个体感染的概率。该模型用于评估三种传染病(肺结核、流感和麻疹)的传播风险。为了允许随机性,模型中的变量应用了随机数生成器,并进行了总共 10000 次个体模拟。本研究中使用的平均量子产生率分别为 12.7、100 和 570 个/小时,用于肺结核、流感和麻疹。
研究结果表明,在等候区停留 30 分钟感染肺结核的平均概率可忽略不计(即 0.0034),而流感的概率则高一个数量级,为 0.0262。相比之下,在同一时期感染麻疹的平均概率为 0.1349。如果停留时间延长至 60 分钟,这些值分别增加到 0.0087、0.0662 和 0.3094。
在正常情况下,在医院等候区就诊感染肺结核的风险很小。同样,与流感传播相关的风险虽然比肺结核高一个数量级,但相对较小。相比之下,麻疹相关的风险较高。虽然安装空气消毒设备可能会有所帮助,但在寻求预防空气传播病毒感染时,首先要尽量减少等候时间和在场的易感个体数量,然后再转向昂贵的技术解决方案,这一点很重要。