Despite being similar in structure, functioning, and size, viral pathogens enjoy very different, usually well-defined ways of life. They occupy their hosts for a few days (influenza), for a few weeks (measles), or even lifelong (HCV), which manifests in acute or chronic infections. The various transmission routes (airborne, via direct physical contact, etc.), degrees of infectiousness (referring to the viral load required for transmission), antigenic variation/immune escape and virulence define further aspects of pathogenic lifestyles. To survive, pathogens must infect new hosts; the success determines their fitness. Infection happens with a certain likelihood during contact of hosts, where contact can also be mediated by vectors. Besides structural aspects of the host-contact network, three parameters appear to be key: the contact rate and the infectiousness during contact, which encode the mode of transmission, and third the immunity of susceptible hosts. On these grounds, what can be said about the reproductive success of viral pathogens? This is the biological question addressed in this paper. The answer extends earlier results of the author and makes explicit connection to another basic work on the evolution of pathogens. A mathematical framework is presented that models intra- and inter-host dynamics in a minimalistic but unified fashion covering a broad spectrum of viral pathogens, including those that cause flu-like infections, childhood diseases, and sexually transmitted infections. These pathogens turn out as local maxima of numerically simulated fitness landscapes. The models involve differential and integral equations, agent-based simulation, networks, and probability.

中文翻译：

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预测病毒病原体生活方式的数学框架

尽管在结构、功能和大小上相似，但病毒病原体的生活方式却截然不同，通常是明确定义的。它们占据宿主几天（流感）、几周（麻疹），甚至终生（HCV），表现为急性或慢性感染。各种传播途径（空气传播、直接身体接触等）、传染性程度（指传播所需的病毒载量）、抗原变异/免疫逃逸和毒力定义了致病生活方式的其他方面。为了生存，病原体必须感染新的宿主；成功决定了他们的健康。感染在宿主接触期间有一定的可能性，其中接触也可以通过载体介导。除了主机接触网络的结构方面，三个参数似乎是关键：接触率和接触过程中的传染性，编码传播方式，第三是易感宿主的免疫力。基于这些理由，病毒病原体的繁殖成功可以说什么呢？这是本文要解决的生物学问题。答案扩展了作者的早期结果，并与另一项关于病原体进化的基础工作有明确的联系。提出了一个数学框架，该框架以简约但统一的方式对宿主内和宿主间动态进行建模，涵盖广泛的病毒病原体，包括导致流感样感染、儿童疾病和性传播感染的病原体。这些病原体被证明是数值模拟的适应度景观的局部最大值。模型涉及微分和积分方程、基于代理的模拟、