Modeling the spread of infectious diseases on contact networks

拟议的研究计划旨在开发现实的数学模型，以研究艾滋病毒和流感等疾病及其干预策略。对于探索人类接触异质性的干预策略（例如优先接种疫苗和接触者追踪），经典流行病模型很难应用，因为它们不切实际地假设社区中的每对个体都可以以相同的概率进行接触和传播疾病。另一方面，现实的人类接触可以由网络来表示，其中节点表示个体，边表示接触。然而，目前的疾病动力学的数学模型，这种接触网络仅限于静态网络，忽略了免疫力的损失。因此，它们不适用于流感（丧失免疫力）和艾滋病毒（出生，死亡和迁移）等疾病及其干预策略（通过隔离个人和消除接触来改变接触网络）。我将开发接触网络上疾病传播的数学模型，包括出生，死亡，迁移和免疫力丧失。决定一种疾病是否能在人群中出现的条件将被推导出来。这些条件对于设计最佳干预策略至关重要。提出的模型将被应用于回答诸如大流行性流感病毒株如何成为季节性的问题;我们应该针对谁进行抗病毒治疗;以及我们应该首先给谁接种疫苗。

The proposed research program aims to develop realistic mathematical models to study diseases such as HIV and influenza and their intervention strategies. For intervention strategies that explore the heterogeneity in human contacts (e.g. prioritized vaccination and contact tracing), classical epidemic models are difficult to apply because they unrealistically assume that every pair of individuals in a community can make contacts and transmit diseases with the same probability. On the other hand, realistic human contacts can be represented by networks where nodes represent individuals and edges represent contacts. However, current mathematical models for disease dynamics on such contact networks are limited to static networks, and neglect loss of immunity. Thus they are not suitable for diseases like influenza (with loss of immunity) and HIV (with births, deaths and migration), and their intervention strategies (altering contact networks by isolating individuals and removing contacts). I will develop mathematical models for disease spread on contact networks, incorporating births, deaths, migration, and loss of immunity. The conditions that determine whether a disease can emerge in a population will be derived. These conditions are crucial to designing optimal intervention strategies. The proposed models will be applied to answer such questions as how pandemic influenza strains become seasonal; whom we should target for antiviral treatment; and whom we should first vaccinate.