Santillana Mauricio, Tuite Ashleigh, Nasserie Tahmina, Fine Paul, Champredon David, Chindelevitch Leonid, Dushoff Jonathan, Fisman David
Computation Health Informatics Program, Boston Children's Hospital, Boston, MA, USA.
Department of Pediatrics, Harvard Medical School, Bonton, MA, USA.
Infect Dis Model. 2018 Mar 9;3:1-12. doi: 10.1016/j.idm.2018.03.001. eCollection 2018.
Mathematical models are often regarded as recent innovations in the description and analysis of infectious disease outbreaks and epidemics, but simple mathematical expressions have been in use for projection of epidemic trajectories for more than a century. We recently introduced a single equation model (the incidence decay with exponential adjustment, or IDEA model) that can be used for short-term epidemiological forecasting. In the mid-19th century, Dr. William Farr made the observation that epidemic events rise and fall in a roughly symmetrical pattern that can be approximated by a bell-shaped curve. He noticed that this time-evolution behavior could be captured by a single mathematical formula ("Farr's law") that could be used for epidemic forecasting. We show here that the IDEA model follows Farr's law, and show that for intuitive assumptions, Farr's Law can be derived from the IDEA model. Moreover, we show that both mathematical approaches, Farr's Law and the IDEA model, resemble solutions of a susceptible-infectious-removed (SIR) compartmental differential-equation model in an asymptotic limit, where the changes of disease transmission respond to control measures, and not only to the depletion of susceptible individuals. This suggests that the concept of the reproduction number was implicitly captured in Farr's (pre-microbial era) work, and also suggests that control of epidemics, whether via behavior change or intervention, is as integral to the natural history of epidemics as is the dynamics of disease transmission.
数学模型通常被视为传染病爆发和流行描述与分析中的最新创新,但简单的数学表达式已被用于预测流行轨迹一个多世纪了。我们最近引入了一个单方程模型(发病率指数调整衰减模型,即IDEA模型),可用于短期流行病学预测。在19世纪中叶,威廉·法尔博士观察到,流行事件以大致对称的模式起伏,可用钟形曲线近似。他注意到这种随时间演变的行为可用一个单一的数学公式(“法尔定律”)来描述,该公式可用于疫情预测。我们在此表明IDEA模型遵循法尔定律,并表明在直观假设下,法尔定律可从IDEA模型推导得出。此外,我们表明,法尔定律和IDEA模型这两种数学方法在渐近极限下类似于易感-感染-康复(SIR) compartmental微分方程模型的解,在这种情况下,疾病传播的变化对控制措施作出反应,而不仅仅是对易感个体的减少作出反应。这表明繁殖数的概念在法尔(微生物学时代之前)的工作中已被隐含地捕捉到,也表明无论是通过行为改变还是干预来控制疫情,与疾病传播动态一样,都是疫情自然史不可或缺的一部分。