Interacting Dynamics on Networks, Applications to Epidemiology (idonate)

多发病并不是新现象，例如1918年的流感大流行（西班牙流感）造成2000万至4000万人死亡，其中大部分是细菌合并感染。一般来说，在网络上传播和增殖的病原体（例如，单个种群中个体之间的接触网络或通过运输方式耦合的种群网络）相互作用，共存和共同进化。这些相互作用要么是合作的，要么是竞争的，可以有效地改变流行病在人群中传播和扩散的方式。不同的病原体可能会促进其他病原体的动力学或以不同的程度抑制它们。地中海贫血是一种遗传性常染色体隐性血液疾病，是后一种情况的一个例子，它在一定程度上预防了疟疾。尽管如此，突发传染病通常作为单一的、自主的病原体进行分析。人们对相互作用的传染过程、其动态特征、特点和后果仍然知之甚少。基本问题仍然悬而未决。例如：合作/竞争如何改变基本的动态特性、阈值动态、相变和其他表征全球传染动态的特性？当病原体处于竞争压力下或从合作机制中受益时，选择病原体的潜在进化过程是什么？不同速率参数、合作或竞争机制的组合如何改变系统的行为？总之，我们缺乏对合并感染动态和综合性疾病的基本了解。该项目的目标是发展共同流行病的概念，产生一个全面的了解复杂网络上的相互作用的传染过程的演变。我们的初步工作表明，当两种疾病之间的合作发挥作用时，存在意想不到的爆发。在这个项目中，我们将开发一个理论框架和计算基础设施，以解决综合征的四个基本方面：1）竞争2）合作3）生态4）复杂网络上相互作用的病原体的进化过程。

Syndemic diseases are not new phenomena, for example the influenza pandemic of 1918 (Spanish flu) killed 20-40 million people mostly by bacterial co-infections. Generally pathogens that spread and proliferate on networks (e.g. contact networks between individuals in single populations or networks of populations that are coupled by means of transportation) interact, coexist and coevolve. These interactions are either cooperative or competitive and can effectively change the way syndemic diseases spread and proliferate in populations. Different pathogens may either facilitate the dynamics of other pathogens or inhibit them with different magnitudes. Thalassemia, a form of inherited autosomal recessive blood disorders, is an example of the latter case, inducing a degree of protection against malaria. Despite this, emergent infectious diseases are typically analyzed as single, autonomous agents. Interacting contagion processes, their dynamical features, characteristics and consequences are still poorly understood. Fundamental questions are still open. For instance: How does cooperation/competition change fundamental dynamic properties, threshold dynamics, phase transitions and other properties that characterize global contagion dynamics? What are potential evolutionary processes that select pathogens when they are under competitive stress or benefit from cooperative mechanisms? How does the combination of different rate parameters, cooperative or competitive mechanism change the behavior of the system? In summary, we lack an understanding of coinfection dynamics and syndemic diseases at a fundamental level. The goal of the project is to develop co-epidemic concepts that yield a comprehensive understanding of the evolution of interacting contagion processes on complex networks. Our preliminary works suggests the existence of unexpected outbreaks when cooperation between two diseases plays a role. In this project we will develop a theoretical framework and a computational infrastructure in order to address four essential aspects of syndemics: 1) competition 2) cooperation 3) ecology 4) evolution processes of interacting pathogens on complex networks.