Information diffusion, network overlap and the modelling of epidemics

信息扩散、网络重叠和流行病建模

• 批准号: EP/H001085/1
• 负责人: Istvan Kiss
• 金额: $ 13.01万
• 依托单位: University of Sussex
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2010
• 资助国家: 英国
• 起止时间: 2010 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FH001085%2F1
• 关键词: Information; diffusion; network; overlap; modelling

Many mathematical models of infectious disease spread assume a 'passive' population where individuals' probability of becoming infected and their contact pattern is not altered in any way by the presence of the disease. However, in a realistic scenario, due to the diffusion of the information triggered by the disease, individuals within a population react and can lower their probability of becoming infected. Therefore, disease transmission and the diffusion of information are interlinked and need to be considered simultaneously. In this project, we propose to develop and to analyse a class of novel mathematical and simulation models that allow us to capture the concurrent spread of the disease and information that is generated by the presence of the disease.The project will start by using a simple model formulation in terms of differential equations (i.e. pair-approximation models) to understand and uncover key interactions between the parallel spread of the disease and information. Building on this, we will generalize results for networks of arbitrary size and consider the case when disease and information spread through networks of contacts that are completely overlapping.Using a continuous Markov chain formulation, we will derive analytical results on simple model networks and determine conditions needed to reduce or stop disease transmission. By using network overlap or similarity measures from graph theory, we will adapt and develop custom made overlap measures that are meaningful when disease spread and the diffusion of information are concurrently considered.

许多传染病传播的数学模型假设一个“被动”的群体，其中个体被感染的概率和他们的接触模式不会因疾病的存在而以任何方式改变。然而，在现实的情况下，由于疾病引发的信息传播，人口中的个体会做出反应，从而降低感染的可能性。因此，疾病传播和信息传播是相互联系的，需要同时考虑。在这个项目中，我们建议开发和分析一类新的数学和模拟模型，使我们能够捕捉疾病的同时传播和疾病存在所产生的信息。该项目将从使用微分方程的简单模型公式开始（即对近似模型），以了解和揭示疾病的平行传播和信息之间的关键相互作用。在此基础上，我们将推广任意规模网络的结果，并考虑疾病和信息通过完全重叠的接触网络传播的情况。使用连续马尔可夫链公式，我们将得出简单模型网络的分析结果，并确定减少或阻止疾病传播所需的条件。通过使用图论中的网络重叠或相似性度量，我们将适应和开发定制的重叠度量，当同时考虑疾病传播和信息扩散时，这些重叠度量是有意义的。

项目成果

Exact epidemic models on graphs using graph-automorphism driven lumping.

• DOI: 10.1007/s00285-010-0344-x
• 发表时间: 2011-04
• 期刊: Journal of mathematical biology
• 影响因子: 1.9
• 作者: Simon PL;Taylor M;Kiss IZ
• 通讯作者: Kiss IZ

Modeling the Interplay Between Human Behavior and the Spread of Infectious Diseases

模拟人类行为与传染病传播之间的相互作用

• DOI: 10.1007/978-1-4614-5474-8_8
• 发表时间: 2013
• 作者: Kiss I

Modelling approaches for simple dynamic networks and applications to disease transmission models

• DOI: 10.1098/rspa.2011.0349
• 发表时间: 2012-05-08
• 期刊: PROCEEDINGS OF THE ROYAL SOCIETY A-MATHEMATICAL PHYSICAL AND ENGINEERING SCIENCES
• 影响因子: 3.5
• 作者: Kiss, Istvan Z.;Berthouze, Luc;Simon, Peter L.
• 通讯作者: Simon, Peter L.

From exact stochastic to mean-field ODE models: a new approach to prove convergence results

从精确随机模型到平均场 ODE 模型：证明收敛结果的新方法

• DOI: 10.1093/imamat/hxs001
• 发表时间: 2012
• 期刊: IMA Journal of Applied Mathematics
• 影响因子: 1.2
• 作者: Simon P

Identification of Criticality in Neuronal Avalanches: II. A Theoretical and Empirical Investigation of the Driven Case.

神经元雪崩危险性的识别：II。

• DOI: 10.1186/2190-8567-4-9
• 发表时间: 2014
• 期刊: Journal of mathematical neuroscience
• 影响因子: 2.3
• 作者: Hartley C