Department of Biostatistics and Epidemiology, College of Public Health, University of Oklahoma Health Sciences Center, Oklahoma City, Oklahoma, USA.
Am J Med Sci. 2010 Sep;340(3):181-6. doi: 10.1097/MAJ.0b013e3181e937ca.
Factors associated with the emergence and transmission of infectious diseases often do not follow the assumptions of traditional statistical models such as linearity and independence of outcomes. Transmission dynamics models are well suited to address infectious disease scenarios that do not conform to these assumptions. For example, these models easily account for changes in the incidence rates of infection as the proportions of susceptible and infectious persons change in the population. Fundamental concepts relating to these methods, such as the basic reproductive number, the effective reproductive number and the susceptible-infected-recovered compartmental models, are reviewed. In addition, comparisons and contrasts are made between the following concepts: microparasites and macroparasites, deterministic and stochastic models, difference and differential equations and homogeneous and heterogeneous mixing patterns. Finally, examples of how transmission dynamics models are being applied to factors associated with emerging infectious diseases, such as zoonotic origins, microbial adaption and change, human susceptibility and climate change, are reviewed.
与传染病的出现和传播相关的因素通常不符合传统统计模型（如线性和结果独立性）的假设。传染病传播动力学模型非常适合解决不符合这些假设的传染病情况。例如，这些模型可以轻松地根据易感人群和感染人群在人群中的比例变化来计算感染发生率的变化。本文回顾了与这些方法相关的基本概念，例如基本繁殖数、有效繁殖数和易感-感染-恢复 compartmental 模型。此外，还对以下概念进行了比较和对比：微寄生虫和宏寄生虫、确定性和随机模型、差分和微分方程以及同质和异质混合模式。最后，还回顾了传染病传播动力学模型如何应用于与新发传染病相关的因素，如人畜共患病起源、微生物适应和变化、人类易感性和气候变化。
