Fakultät für Gesundheitswissenschaften, Universität Bielefeld, Bielefeld.
Dtsch Arztebl Int. 2009 Nov;106(47):777-82. doi: 10.3238/arztebl.2009.0777. Epub 2009 Nov 20.
When the first cases of a new infectious disease appear, questions arise about the further course of the epidemic and about the appropriate interventions to be taken to protect individuals and the public as a whole. Mathematical models can help answer these questions. In this article, the authors describe basic concepts in the mathematical modelling of infectious diseases, illustrate their use with a simple example, and present the results of influenza models.
Description of the mathematical modelling of infectious diseases and selective review of the literature.
The two fundamental concepts of mathematical modelling of infectious diseases-the basic reproduction number and the generation time-allow a better understanding of the course of an epidemic. Modelling studies based on past influenza epidemics suggest that the rise of the epidemic curve can be slowed at the beginning of the epidemic by isolating ill persons and giving prophylactic medications to their contacts. Later on in the course of the epidemic, restricting the number of contacts (e.g., by closing schools) may mitigate the epidemic but will only have a limited effect on the total number of persons who contract the disease.
Mathematical modelling is a valuable tool for understanding the dynamics of an epidemic and for planning and evaluating interventions.
当新传染病的首例病例出现时,会产生关于疫情进一步发展的问题,以及为保护个人和整个公众应采取的适当干预措施的问题。数学模型可以帮助回答这些问题。本文作者描述了传染病数学建模的基本概念,用一个简单的例子来说明其用法,并介绍了流感模型的结果。
传染病数学建模的描述和文献的选择性回顾。
传染病数学建模的两个基本概念——基本繁殖数和世代时间——可以帮助更好地理解疫情的发展过程。基于过去流感疫情的建模研究表明,通过隔离病人和向其接触者提供预防性药物,可以在疫情初期减缓疫情曲线的上升。在疫情的后期,限制接触人数(例如关闭学校)可能会减轻疫情,但对感染疾病的总人数的影响有限。
数学建模是理解疫情动态和规划及评估干预措施的有价值的工具。