Disease Dynamics and Epidemic Control on Complex Networks

复杂网络上的疾病动态和流行控制

• 批准号: RGPIN-2020-06153
• 负责人: Rutherford, Alexander
• 金额: $ 1.31万
• 依托单位: Simon Fraser University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=718249
• 关键词: Disease; Dynamics; Epidemic; Control; Complex

Human social and sexual network structure play an important role in the dynamics of disease epidemics. This has significant implications for strategies to control or prevent epidemics. Epidemic prevention and control relies on three processes: vaccination, testing, and treatment. We will develop new and innovative computational methods for optimal control of epidemics on complex networks. An operations research approach will be used to incorporate resource constraints and model processes for implementing control strategies. Antibiotic-resistant infections are of increasing concern in public health. Drug-resistant strains of pneumonia, gonorrhoea, and tuberculosis are becoming more prevalent. In many instances, developing effective control strategies requires disease models which capture coexistence of drug-susceptible and drug-resistant strains in the context of population structure, geographic variability, and differences in public health response. These factors can be captured using network models and we will investigate multi-strain disease models on networks to determine how drug-resistant strains arise and coexist with other strains. A better understanding of epidemic control on complex networks requires models which are both heterogeneous and dynamic. Heterogeneous networks have two or more type of edges with different characteristics. Disease propagates on the network by contact processes on each of the edge types. An example application is HIV transmission on an injection drug user network, in which infection occurs either through sexual contact or by sharing syringes. The sexual and syringe sharing networks have different topology. Dynamic networks evolve according to stochastic processes, which in general are coupled to the disease process and state changes of the nodes. The edges connected to a node may dynamically re-wire to other nodes. These models have many applications, because in general the disease states of individuals impact their social behaviour. Furthermore, epidemic prevention and control programs can be designed to alter the network structure and interdict disease spread. Determining the optimal allocation of vaccination, testing and treatment resources for these models is challenging, because it is a constrained optimization problems with a computationally expensive stochastic objective function. An important aspect of this research program will be to use real-world data to inform both the network models and implementation of public health interventions. This will not only lead to more realistic models, but foster collaborations to enhance the impact of the research. Although motivated by applications in public health, this research also has implications for many other fields, such as information spread on social networks, cyber-security, criminal networks, social network marketing, and substance use epidemics.

人类的社会和性网络结构在疾病流行的动力学中起着重要作用。这对控制或预防流行病的战略具有重大意义。 疫情防控靠三个过程：接种、检测、救治。我们将开发新的和创新的计算方法，用于复杂网络上流行病的最佳控制。一个业务研究的方法将被用来纳入资源的限制和模型的过程中实施控制战略。 抗生素耐药性感染在公共卫生中日益受到关注。肺炎、淋病和肺结核的耐药菌株越来越普遍。在许多情况下，制定有效的控制策略需要疾病模型，这些模型在人口结构、地理变异性和公共卫生反应差异的背景下捕获药物敏感和耐药菌株的共存。这些因素可以使用网络模型捕获，我们将研究网络上的多菌株疾病模型，以确定耐药菌株如何产生并与其他菌株共存。 为了更好地理解复杂网络上的流行病控制，需要同时具有异构性和动态性的模型。异构网络具有两种或两种以上具有不同特征的边。疾病在网络上通过每个边类型上的接触过程传播。一个示例应用是注射毒品使用者网络上的艾滋病毒传播，其中感染通过性接触或共用注射器发生。性和注射器共享网络具有不同的拓扑结构。动态网络根据随机过程演化，这些随机过程通常与节点的疾病过程和状态变化相耦合。连接到节点的边可以动态地重新布线到其他节点。这些模型有许多应用，因为一般来说，个人的疾病状态会影响他们的社会行为。此外，可以设计流行病预防和控制方案，以改变网络结构，阻断疾病传播。确定这些模型的疫苗接种、检测和治疗资源的最优分配是具有挑战性的，因为它是具有计算昂贵的随机目标函数的约束优化问题。 这项研究计划的一个重要方面是使用真实世界的数据来为网络模型和公共卫生干预措施的实施提供信息。这不仅会导致更现实的模型，而且会促进合作，以提高研究的影响力。虽然在公共卫生应用的动机，这项研究也有许多其他领域的影响，如信息传播的社交网络，网络安全，犯罪网络，社交网络营销和物质使用流行病。