Kelso Joel K, Milne George J, Kelly Heath
School of Computer Science and Software Engineering, University of Western Australia, Perth, WA, Australia.
BMC Public Health. 2009 Apr 29;9:117. doi: 10.1186/1471-2458-9-117.
Social distancing interventions such as school closure and prohibition of public gatherings are present in pandemic influenza preparedness plans. Predicting the effectiveness of intervention strategies in a pandemic is difficult. In the absence of other evidence, computer simulation can be used to help policy makers plan for a potential future influenza pandemic. We conducted simulations of a small community to determine the magnitude and timing of activation that would be necessary for social distancing interventions to arrest a future pandemic.
We used a detailed, individual-based model of a real community with a population of approximately 30,000. We simulated the effect of four social distancing interventions: school closure, increased isolation of symptomatic individuals in their household, workplace nonattendance, and reduction of contact in the wider community. We simulated each of the intervention measures in isolation and in several combinations; and examined the effect of delays in the activation of interventions on the final and daily attack rates.
For an epidemic with an R0 value of 1.5, a combination of all four social distancing measures could reduce the final attack rate from 33% to below 10% if introduced within 6 weeks from the introduction of the first case. In contrast, for an R0 of 2.5 these measures must be introduced within 2 weeks of the first case to achieve a similar reduction; delays of 2, 3 and 4 weeks resulted in final attack rates of 7%, 21% and 45% respectively. For an R0 of 3.5 the combination of all four measures could reduce the final attack rate from 73% to 16%, but only if introduced without delay; delays of 1, 2 or 3 weeks resulted in final attack rates of 19%, 35% or 63% respectively. For the higher R0 values no single measure has a significant impact on attack rates.
Our results suggest a critical role of social distancing in the potential control of a future pandemic and indicate that such interventions are capable of arresting influenza epidemic development, but only if they are used in combination, activated without delay and maintained for a relatively long period.
诸如学校关闭和禁止公众集会等社交距离干预措施已被纳入大流行性流感防范计划中。预测大流行期间干预策略的有效性很困难。在缺乏其他证据的情况下，计算机模拟可用于帮助政策制定者为未来可能发生的流感大流行制定计划。我们对一个小社区进行了模拟，以确定社交距离干预措施阻止未来大流行所需的启动规模和时机。
我们使用了一个基于个体的详细模型，该模型模拟的是一个约有30000人口的真实社区。我们模拟了四种社交距离干预措施的效果：学校关闭、增加有症状个体在其家中的隔离、不上班以及减少更广泛社区内的接触。我们分别模拟了每种干预措施及其几种组合；并研究了干预措施启动延迟对最终发病率和每日发病率的影响。
对于R0值为1.5的疫情，如果在首例病例出现后的6周内采取所有四种社交距离措施，其组合可将最终发病率从33%降至10%以下。相比之下，对于R0值为2.5的疫情，这些措施必须在首例病例出现后的2周内实施才能实现类似的降低效果；延迟2周、3周和4周导致的最终发病率分别为7%、21%和45%。对于R0值为3.5的疫情，所有四种措施的组合可将最终发病率从73%降至16%，但前提是必须立即实施；延迟1周、2周或3周导致的最终发病率分别为19%、35%或63%。对于较高的R0值，没有单一措施对发病率有显著影响。
我们的结果表明社交距离在未来大流行的潜在控制中起着关键作用，并表明此类干预措施能够阻止流感疫情的发展，但前提是它们要联合使用、立即启动并持续较长时间。
