Estimating the impact of social structure on epidemics and predicting the impact of targeted interventions

估计社会结构对流行病的影响并预测有针对性的干预措施的影响

• 批准号: G0800596/1
• 负责人: Christophe Fraser
• 金额: $ 73.01万
• 依托单位: Imperial College London
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2009
• 资助国家: 英国
• 起止时间: 2009 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=G0800596%2F1
• 关键词: Estimating; impact; social; structure; epidemics

Communicable diseases like flu or SARS transmit through close inter-personal contacts. So, the risk that you get ill during an outbreak is likely to be influenced by who you mix with. Understanding how people interact with each other may therefore be the key to designing efficient control policies. Examples of public health interventions which are triggered by the structure of the social network include those which target households (for example, all household members are treated with antivirals such as Tamiflu when a member is sick) or specific age groups. Consider for example vaccination against seasonal influenza. A first strategy is to vaccinate the elderly as they constitute the main risk group for severe disease and mortality. However, vaccination of children has been suggested as a better policy to minimise mortality overall, since children are the most important transmitters of flu. To assess whether vaccinating children or the elderly is likely to be more effective, it is important to precisely assess how children interact with each other and with other age groups. If you had flu, would you be able to say where and by whom you got infected? The frequent difficulty in answering this question is what makes it so challenging to determine ?who acquires infection from whom? and thus assess the effectiveness of the different public health strategies. A first aim of this study will be to develop a set of relatively simple mathematical and statistical tools that can be used to make such an evaluation. On an operational level, those tools will make it possible to gain insight on the potential impact of interventions, to monitor the efficacy of control measures and to support decision making in real-time during an outbreak. This work could for example feed the UK surveillance system designed for pandemic influenza; and should increase the capacity of politicians and other decision makers to make the correct choices at the appropriate moments.By comparing the route of transmission (?who acquires infection from whom?) for different infections and to other sociological indicators (?who talks to whom?), we will also try to answer fundamental questions on the complex nature of transmission. For example, what types of social contact can lead to transmission? Talking? Hugging? Answers to those seemingly simple questions may have major implications in terms of future disease prevention.

流感或SARS等传染病通过密切的人际接触传播。因此，您在疫情爆发期间生病的风险可能会受到您与谁接触的影响。因此，了解人们如何相互作用可能是设计有效控制政策的关键。由社会网络结构引发的公共卫生干预措施的例子包括针对家庭（例如，当家庭成员生病时，所有家庭成员都接受达菲等抗病毒药物治疗）或特定年龄组的干预措施。例如，考虑接种季节性流感疫苗。第一项战略是为老年人接种疫苗，因为他们是严重疾病和死亡的主要风险群体。然而，由于儿童是流感最重要的传播者，因此建议儿童接种疫苗是降低总体死亡率的更好政策。为了评估接种儿童或老年人疫苗是否可能更有效，重要的是要准确评估儿童之间以及与其他年龄组之间的相互作用。如果您感染了流感，您能说出您在哪里、被谁感染了吗？在回答这个问题时经常遇到的困难是，是什么让它如此难以确定？谁从谁那里感染？从而评估不同公共卫生策略的有效性。这项研究的第一个目标是开发一套相对简单的数学和统计工具，可用于进行这种评估。在业务层面上，这些工具将使人们能够深入了解干预措施的潜在影响，监测控制措施的效力，并在疫情爆发期间支持实时决策。例如，这项工作可以为英国为大流行性流感设计的监测系统提供信息，并应提高政治家和其他决策者在适当时刻做出正确选择的能力。谁从谁那里感染？对于不同的感染和其他社会学指标（？谁跟谁说话？）我们还将试图回答有关传播的复杂性质的基本问题。例如，什么样的社会接触会导致传播？说话？拥抱？这些看似简单的问题的答案可能对未来的疾病预防具有重大意义。