Vaccine and Immunisation Research Group, Murdoch Childrens Research Institute and Melbourne School of Population Health, The University of Melbourne, Parkville, Australia.
PLoS One. 2011 Feb 4;6(2):e14505. doi: 10.1371/journal.pone.0014505.
Many countries have amassed antiviral stockpiles for pandemic preparedness. Despite extensive trial data and modelling studies, it remains unclear how to make optimal use of antiviral stockpiles within the constraints of healthcare infrastructure. Modelling studies informed recommendations for liberal antiviral distribution in the pandemic phase, primarily to prevent infection, but failed to account for logistical constraints clearly evident during the 2009 H1N1 outbreaks. Here we identify optimal delivery strategies for antiviral interventions accounting for logistical constraints, and so determine how to improve a strategy's impact.
We extend an existing SEIR model to incorporate finite diagnostic and antiviral distribution capacities. We evaluate the impact of using different diagnostic strategies to decide to whom antivirals are delivered. We then determine what additional capacity is required to achieve optimal impact. We identify the importance of sensitive and specific case ascertainment in the early phase of a pandemic response, when the proportion of false-positive presentations may be high. Once a substantial percentage of ILI presentations are caused by the pandemic strain, identification of cases for treatment on syndromic grounds alone results in a greater potential impact than a laboratory-dependent strategy. Our findings reinforce the need for a decentralised system capable of providing timely prophylaxis.
We address specific real-world issues that must be considered in order to improve pandemic preparedness policy in a practical and methodologically sound way. Provision of antivirals on the scale proposed for an effective response is infeasible using traditional public health outbreak management and contact tracing approaches. The results indicate to change the transmission dynamics of an influenza epidemic with an antiviral intervention, a decentralised system is required for contact identification and prophylaxis delivery, utilising a range of existing services and infrastructure in a "whole of society" response.
许多国家为应对大流行做好了储备抗病毒药物的准备。尽管有广泛的试验数据和模型研究,但在医疗保健基础设施的限制内,如何最优地使用抗病毒药物储备仍不清楚。模型研究为大流行阶段的自由分发抗病毒药物提供了建议,主要是为了预防感染,但没有明确考虑到 2009 年 H1N1 爆发期间明显存在的后勤限制。在这里,我们确定了考虑后勤限制的抗病毒干预措施的最佳交付策略,从而确定如何提高策略的影响。
我们扩展了现有的 SEIR 模型,纳入了有限的诊断和抗病毒分配能力。我们评估了使用不同的诊断策略来决定向谁提供抗病毒药物的影响。然后,我们确定了实现最佳效果所需的额外能力。我们确定了在大流行应对的早期阶段,即假阳性出现率可能较高时,敏感和特异的病例确定的重要性。一旦相当比例的 ILI 表现是由大流行株引起的,仅基于综合征的病例识别进行治疗就会产生更大的潜在影响,而不是基于实验室的策略。我们的研究结果强化了需要建立一个能够及时提供预防措施的去中心化系统。
我们解决了为以实际和方法上合理的方式改进大流行准备政策而必须考虑的具体现实问题。按照有效的应对措施提出的规模提供抗病毒药物是不可行的,因为传统的公共卫生爆发管理和接触者追踪方法无法实现。结果表明,要通过抗病毒干预改变流感流行的传播动态,需要一个去中心化的系统,用于识别和提供接触者预防,利用各种现有的服务和基础设施,以“全社会”的方式做出反应。