Spatio-temporal dynamics of random transmission events: from information sharing to epidemic spread
Spatio-temporal dynamics of random transmission events: from information sharing to epidemic spread
复制标题
随机传播事件的时空动态：从信息共享到疫情传播
DOI：
10.1088/1751-8121/ac8587
复制
发表时间：
2022-07
期刊：
影响因子：
--
通讯作者：
L. Giuggioli;Seeralan Sarvaharman
中科院分区：
文献类型：
--
作者：
L. Giuggioli;Seeralan Sarvaharman
在自然和人工系统中，个体之间发生的短尺度随机传播事件控制着更大尺度上出现的模式。例子包括传染性病原体在动物种群中的空间传播，在线社交网络中错误信息的传播，或群中机器人单元之间共享目标位置。尽管信息传输事件无处不在，量化时空传输过程的一般方法仍然难以捉摸。预测信息何时何地从一个人传递到另一个人的挑战源于分析方法的数量有限，以及随机模拟输出的大波动和固有的计算成本，这是迄今为止研究此类过程的主要理论工具。在这里，我们克服了这些限制，通过开发一个分析理论的传输动力学之间的随机移动代理在任意空间域和任意的信息传输效率。我们超越了众所周知的近似用于研究反应扩散现象，如运动和反应限制制度，通过精确量化的平均反应时间存在多个异构反应位置。为了证明我们的理论的广泛适用性，我们在不同的场景中使用它。我们展示了如何类型的空间限制可能会改变许多数量级的传输发生的时间尺度。当获取信息代表捕获能力时，我们使用我们的形式主义来揭示领土动物之间捕食者-猎物竞赛中违反直觉的回避策略。当信息传递代表传染性病原体的转移时，我们考虑一个具有易感、感染和康复个体的群体，这些个体在相遇时移动并传递感染，并分析性地预测基本繁殖数。最后，我们将展示如何应用传输理论半解析时，个人移动的拓扑结构是一个网络。
Random transmission events between individuals occurring at short scales control patterns emerging at much larger scales in natural and artificial systems. Examples range from the spatial propagation of an infectious pathogen in an animal population to the spread of misinformation in online social networks or the sharing of target locations between robot units in a swarm. Despite the ubiquity of information transfer events, a general methodology to quantify spatio-temporal transmission processes has remained elusive. The challenge in predicting when and where information is passed from one individual to another stems from the limited number of analytic approaches and from the large fluctuations and inherent computational cost of stochastic simulation outputs, the main theoretical tool available to study such processes so far. Here we overcome these limitations by developing an analytic theory of transmission dynamics between randomly moving agents in arbitrary spatial domains and with arbitrary information transfer efficiency. We move beyond well-known approximations employed to study reaction diffusion phenomena, such as the motion and reaction limited regimes, by quantifying exactly the mean reaction time in presence of multiple heterogeneous reactive locations. To demonstrate the wide applicability of our theory we employ it in different scenarios. We show how the type of spatial confinement may change by many orders of magnitude the time scale at which transmission occurs. When acquiring information represents the ability to capture, we use our formalism to uncover counterintuitive evasive strategies in a predator–prey contest between territorial animals. When information transmission represents the transfer of an infectious pathogen, we consider a population with susceptible, infected and recovered individuals that move and pass infection upon meeting and predict analytically the basic reproduction number. Finally we show how to apply the transmission theory semi-analytically when the topology of where individuals move is that of a network.