Applying a Probabilistic Infection Model for studying contagion processes in contact networks
Applying a Probabilistic Infection Model for studying contagion processes in contact networks
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DOI：
10.1016/j.jocs.2021.101419
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发表时间：
2021-07-27
影响因子：
3.3
通讯作者：
Mikler, Armin R.
中科院分区：
文献类型：
--
作者：
Qian, William;Bhowmick, Sanjukta;Mikler, Armin R.
传染病传播的模型是计算流行病学领域的核心。模拟传染病传播过程的两种主要方法包括：(I)通过蒙特卡罗过程模拟接触网络中的传播；(Ii)使用集合种群模型跟踪疾病动态。在这两种情况下，个体被显式地(接触网络)或隐式地(元种群)假定属于恰好属于一种疾病状态(例如，易感、感染等)。在现实中，个人的疾病状态很少被如此清晰地划分。一个特定的病原体可以以不同的概率同时存在于多种疾病状态(如感染和暴露)中。为了模拟这种随机性，我们提出了一种新的方法，我们称之为概率感染模型(PIM)。与在每个时间步长为每个代理分配一个状态的传统模型不同，PIM计算每个代理处于每个感染状态的概率。我们提出的PIM通过允许用户(I)估计单个顶点的R0值和(Ii)而不是ALL或NONE值，提供了代理人的每个感染状态的概率，从而提供了对个别水平上的爆发动态的更分层的理解。此外，使用我们的概率方法，可以在一次模拟中计算疫情的总体轨迹，而不是蒙特卡罗过程所需的多次(数百次)重复模拟。通过将PIM模拟结果与随机SEIR模型的模拟结果以及模拟所需的时间进行比较，证明了PIM的有效性。我们介绍了三种疾病在系统和个人层面的结果：麻疹和两种流感病毒株。我们演示了如何使用PIM来研究改变新冠肺炎的传递性对其爆发的影响。这篇论文是发表在2020年国际计算科学会议(ICCS)[30]论文集上的手稿的扩展版本。这些扩展主要在第4节(图表结构和感染概率之间的关系)和第5节(不同的新冠肺炎传播率对疫情动态的影响)中进行。
Modeling the spread of infectious diseases is central to the field of computational epidemiology. Two prominent approaches to modeling the contagion process include (i) simulating the spread in contact networks through Monte-Carlo processes and (ii) tracking the disease dynamics using meta-population models. In both cases, the individuals are explicitly (contact networks) or implicitly (meta-population) assumed to belong to exactly one disease state (e.g., susceptible, infected, etc.). In reality, the disease states of individuals are rarely so cleanly compartmentalized. A particular agent can exist in multiple disease states (such as infected and exposed) concurrently with varying probability. To model this stochasticity, we present a new method, that we term as the Probabilistic Infection Model (PIM). Unlike traditional models that assign exactly one state to each agent at each time step, the PIM computes the probability of each agent being in each of the infectious states. Our proposed PIM provides a more layered understanding of the dynamics of the outbreak at individual levels, by allowing the users to (i) estimate the value of R0 at individual vertices and (ii) instead of an all or none value, provides the probability of each infected state of an agent. Additionally, using our probabilistic approach the overall trajectories of the outbreaks can be computed in one simulation, as opposed to the numerous (order of hundreds) repeated simulations required for the Monte Carlo process. We demonstrate the efficacy of PIM by comparing the results of the PIM simulations with those obtained by simulating stochastic SEIR models, as well as the time required for the simulations. We present results at the system and at the individual levels for three diseases; measles and two strains of influenza. We demonstrate how the PIM can be used to study the effect of varying the transimissibility of COVID-19 on its outbreak. This paper is an extended version of a manuscript published in the proceedings of the 2020 International Conference on Computational Science (ICCS)[30]. These extensions are primarily within Sections 4 (Relationship between graph structure and probability of infection) and 5 (Effect of varying COVID-19 transmissibility on outbreak dynamics).