Network epidemiology and the quantification of behavioral interventions

网络流行病学和行为干预的量化

• 批准号: 10369064
• 负责人: Laurent Hébert-Dufresne
• 金额: $ 21.86万
• 依托单位: UNIVERSITY OF VERMONT & ST AGRIC COLLEGE
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-15 至 2023-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10369064
• 关键词: Network; epidemiology; quantification; behavioral; interventions

Emerging epidemics are a constant threat to global health. The recent Ebola outbreak in West Africa alone took over ten thousand lives despite international help nearing $5 billion from 70 countries. In retrospect, two factors stand out: (i) the local health system was illprepared, with Ebola-affected countries falling about 80% short of WHO recommendations for numbers of doctors and nurses per capita (223/100,000); (ii) the declaration of the Public Health Emergency of International Concern came late, over 4 months after the first international transmission event. These apparent systemic failures, however, likely reflect the fact that Ebola outbreaks in Africa are so highly unpredictable and variable. Outbreaks prior to the West African Ebola Outbreak occurred in the Democratic Republic of Congo in 2017, 2014 and 2012, as well as Uganda in 2007, but these outbreaks never exceeded 500 cases. In stark contrast, there were nearly 30,000 cases caused by the West African epidemic. The dynamics of an Ebola outbreak are shaped not only by the biology of the virus, but also in large part by societal and behavioral factors. Both factors are heterogeneous and highly variable, as evidenced by contact tracing data from the West African Ebola outbreak. These data suggest that less than 5% of Ebola cases caused more than one secondary infection, yet some individuals transmit the virus to dozens of others. Theoretical models of disease spread are thus becoming increasingly based on stochastic network representations of epidemiological data rather than conventional collections of deterministic well-mixed compartments. We postulate that heterogeneity in behavior is not only an important feature of an emerging epidemic, its presence implies that behavioral interventions can be more important than biomedical interventions in shaping the spread of disease. Here, we propose the development of a modeling framework that combines a stochastic network model with an agent-based model to allow the effects of both behavioral and biomedical interventions on a disease epidemic to be investigated. The network model will provide stochastic forecasting in the form of distributions of possible disease spread outcomes as functions of patterns of behavior. At the same time, agent-based simulations imposed on the network will allow us to quantify the effects of interventions that aim to mitigate disease transmission through alterations in either the behavior of individuals or the biology of the virus itself. These different modeling approaches are thus complementary. Furthermore, each can be independently validated against available data sets. The results of this study will advance our understanding of the modeling and surveillance required in managing infectious diseases, and will have significant implications for public health policy by helping to identify improved strategies for responding to emerging pandemics.

新出现的流行病是对全球健康的持续威胁。仅西非最近爆发的埃博拉病毒就夺走了1万人的生命 尽管70个国家提供了近50亿美元的国际援助。回顾过去，有两个因素突出：(1)当地卫生系统准备不足， 受埃博拉影响的国家比世卫组织建议的人均医生和护士数量低约80% (223/100,000)；(2)国际关注的突发公共卫生事件的宣布较第一次事件晚了4个多月 国际传播盛会。然而，这些明显的系统性故障可能反映了这样一个事实，即非洲的埃博拉疫情 高度不可预测和多变的。年，刚果民主共和国爆发西非埃博拉疫情之前的疫情。 2017年、2014年和2012年，以及2007年在乌干达，但这些疫情从未超过500例。与之形成鲜明对比的是， 西非疫情造成的3万例病例。 埃博拉疫情的动态不仅受到病毒生物学的影响，而且在很大程度上也受到社会和行为的影响 各种因素。正如西非埃博拉疫情的接触者追踪数据所证明的那样，这两个因素都是不同的，变数很大。 这些数据表明，不到5%的埃博拉病例导致不止一次继发感染，但仍有一些人传播病毒 给其他几十个人。因此，疾病传播的理论模型越来越多地基于随机网络表示法 流行病学数据，而不是确定性的混合良好的隔间的传统收集。我们假设，在 行为不仅是新出现的流行病的重要特征，它的存在意味着行为干预可以更多 在形成疾病传播方面比生物医学干预更重要。 在这里，我们建议开发一个将随机网络模型和基于代理的模型相结合的建模框架，以 允许调查行为干预和生物医学干预对疾病流行的影响。网络模型将 以可能的疾病传播结果作为行为模式的函数的分布的形式提供随机预测。在 同时，施加在网络上的基于代理的模拟将使我们能够量化旨在缓解 通过改变个体的行为或病毒本身的生物学来传播疾病。这些不同的建模 因此，方法是相辅相成的。此外，每一个都可以根据可用的数据集进行独立验证。这样做的结果 这项研究将促进我们对管理传染病所需的建模和监测的理解，并将具有重大意义 通过帮助确定改进的应对新出现的流行病的战略，对公共卫生政策产生影响。