Department of Public Health Sciences, University of North Carolina Charlotte, Charlotte, NC, United States of America.
School of Data Science, University of North Carolina Charlotte, Charlotte, NC, United States of America.
PLoS One. 2020 Oct 15;15(10):e0238186. doi: 10.1371/journal.pone.0238186. eCollection 2020.
Mathematical models are powerful tools to investigate, simulate, and evaluate potential interventions for infectious diseases dynamics. Much effort has focused on the Susceptible-Infected-Recovered (SIR)-type compartment models. These models consider host populations and measure change of each compartment. In this study, we propose an alternative patch dynamic modeling framework from pathogens' perspective. Each patch, the basic module of this modeling framework, has four standard mechanisms of pathogen population size change: birth (replication), death, inflow, and outflow. This framework naturally distinguishes between-host transmission process (inflow and outflow) and within-host infection process (replication) during the entire transmission-infection cycle. We demonstrate that the SIR-type model is actually a special cross-sectional and discretized case of our patch dynamics model in pathogens' viewpoint. In addition, this patch dynamics modeling framework is also an agent-based model from hosts' perspective by incorporating individual host's specific traits. We provide an operational standard to formulate this modular-designed patch dynamics model. Model parameterization is feasible with a wide range of sources, including genomics data, surveillance data, electronic health record, and from other emerging technologies such as multiomics. We then provide two proof-of-concept case studies to tackle some of the existing challenges of SIR-type models: sexually transmitted disease and healthcare acquired infections. This patch dynamics modeling framework not only provides theoretical explanations to known phenomena, but also generates novel insights of disease dynamics from a more holistic viewpoint. It is also able to simulate and handle more complicated scenarios across biological scales such as the current COVID-19 pandemic.
数学模型是研究、模拟和评估传染病动力学潜在干预措施的有力工具。人们已经投入了大量精力研究易感者-感染者-恢复者（SIR）型 compartment 模型。这些模型考虑宿主群体并测量每个 compartment 的变化。在本研究中，我们从病原体的角度提出了一种替代的斑块动态建模框架。每个斑块都是这个建模框架的基本模块，具有四种病原体种群数量变化的标准机制：出生（复制）、死亡、流入和流出。该框架自然地区分了宿主间传播过程（流入和流出）和宿主内感染过程（复制），这一过程贯穿整个传播-感染周期。我们证明，SIR 型模型实际上是我们从病原体角度的斑块动态模型的一个特殊的横截面和离散化情况。此外，该斑块动态建模框架也是一个基于宿主的 agent-based 模型，通过纳入个体宿主的特定特征。我们提供了一个操作标准来制定这个模块化设计的斑块动态模型。模型参数化是可行的，可以从多种来源获取，包括基因组学数据、监测数据、电子健康记录，以及其他新兴技术，如多组学。然后，我们提供了两个概念验证案例研究，以解决 SIR 型模型存在的一些挑战：性传播疾病和医疗机构获得性感染。这个斑块动态建模框架不仅为已知现象提供了理论解释，而且从更整体的角度产生了疾病动力学的新见解。它还能够模拟和处理更复杂的生物尺度场景，如当前的 COVID-19 大流行。
