Mitigation strategies for pandemic influenza A: balancing conflicting policy objectives.-猫眼课题宝

DOI：

10.1371/journal.pcbi.1001076

发表时间：

2011-02-10

期刊：

PLoS computational biology

影响因子：

4.3

通讯作者：

中科院分区：

生物学2区

文献类型：

作者：

Hollingsworth TD;Klinkenberg D;Heesterbeek H;Anderson RM

来源链接：

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3037387/?report=classic

通过采取一系列减少传播的干预措施，可以缓解严重的流感大流行。干预措施可以减少疫情的影响，为研制出疫苗争取时间，但它们可能会付出高昂的社会和经济代价。对流行病动态的非线性影响意味着适当的策略在很大程度上取决于干预的精确目标。国家大流行性流感计划很少明确说明政策目标或确定可能相互冲突的目标的优先次序，例如尽量减少死亡率（取决于大流行的严重程度）或高峰流行率，或限制减少接触干预措施的社会经济负担。我们使用甲型流感流行病学模型来调查减少接触的干预措施和抗病毒药物或大流行前疫苗的可用性如何有助于实现特定的政策目标。我们的分析表明，理想的战略取决于干预措施的目标，实现一个政策目标可能会妨碍其他政策目标的成功，例如，限制公共卫生资源的高峰需求可能会延长疫情的持续时间，从而延长其经济和社会影响。限制总病例数可以通过一系列策略来实现，而额外限制服务高峰需求的策略需要更复杂的干预。例如，如果在获得大流行性疫苗（即有时间限制的干预措施）之前必须实现多个目标，我们的分析表明，干预措施应该在疫情发生几周后实施，而不是在一开始就实施。这一观察结果在一系列限制条件下以及在对R0和疫苗可得时间的估计不确定的情况下都是稳健的。这些分析强调，在规划和实施减轻流感大流行影响的战略时，需要更准确地说明政策目标及其假定后果。在发生死亡率高且有可能迅速传播的流感大流行时，如1918 - 1919年大流行，可采用一些非药物公共卫生控制选择，以减少社区传播并减轻大流行的影响。这些措施包括通过关闭学校或推迟公共活动来减少社会接触，并鼓励洗手和使用口罩。这些干预措施不仅会对流行病动态产生非直观的影响，而且还会产生直接和间接的社会和经济成本，这意味着政府只希望在有限的时间内使用这些措施。我们使用模拟来显示，实现一个目标（例如，将病例总数控制在某些最大可用治疗数以下）的有限时间干预措施与实现另一个目标（例如，最小化医疗保健服务的峰值需求）的干预措施是不一样的。如果同时确定多个目标，我们经常看到，最佳干预措施不必立即开始，而可以在疫情发生几周后开始。我们的研究表明，根据确定的政策目标制定大流行计划具有一定的灵活性，以考虑到大流行特征的不确定性，这很重要。

Mitigation of a severe influenza pandemic can be achieved using a range of interventions to reduce transmission. Interventions can reduce the impact of an outbreak and buy time until vaccines are developed, but they may have high social and economic costs. The non-linear effect on the epidemic dynamics means that suitable strategies crucially depend on the precise aim of the intervention. National pandemic influenza plans rarely contain clear statements of policy objectives or prioritization of potentially conflicting aims, such as minimizing mortality (depending on the severity of a pandemic) or peak prevalence or limiting the socio-economic burden of contact-reducing interventions. We use epidemiological models of influenza A to investigate how contact-reducing interventions and availability of antiviral drugs or pre-pandemic vaccines contribute to achieving particular policy objectives. Our analyses show that the ideal strategy depends on the aim of an intervention and that the achievement of one policy objective may preclude success with others, e.g., constraining peak demand for public health resources may lengthen the duration of the epidemic and hence its economic and social impact. Constraining total case numbers can be achieved by a range of strategies, whereas strategies which additionally constrain peak demand for services require a more sophisticated intervention. If, for example, there are multiple objectives which must be achieved prior to the availability of a pandemic vaccine (i.e., a time-limited intervention), our analysis shows that interventions should be implemented several weeks into the epidemic, not at the very start. This observation is shown to be robust across a range of constraints and for uncertainty in estimates of both R0 and the timing of vaccine availability. These analyses highlight the need for more precise statements of policy objectives and their assumed consequences when planning and implementing strategies to mitigate the impact of an influenza pandemic. In the event of an influenza pandemic which has high mortality and the potential to spread rapidly, such as the 1918–19 pandemic, there are a number of non-pharmaceutical public health control options available to reduce transmission in the community and mitigate the effects of the pandemic. These include reducing social contacts by closing schools or postponing public events, and encouraging hand washing and the use of masks. These interventions will not only have a non-intuitive impact on the epidemic dynamics, but they will also have direct and indirect social and economic costs, which mean that governments will only want to use them for a limited amount of time. We use simulations to show that limited-time interventions that achieve one aim, e.g., contain the total number of cases below some maximum number of treatments available, are not the same as those that achieve another, e.g., minimize peak demand for health care services. If multiple aims are defined simultaneously, we often see that the optimal intervention need not commence immediately but can begin a few weeks into the epidemic. Our research demonstrates the importance of tailoring pandemic plans to defined policy targets with some flexibility to allow for uncertainty in the characteristics of the pandemic.