Laboratoire de Biomathématiques et d'Estimations Forestières, Faculté des Sciences Agronomiques, Université d'Abomey-Calavi, Abomey-Calavi, Bénin.
PLoS One. 2020 Oct 20;15(10):e0240578. doi: 10.1371/journal.pone.0240578. eCollection 2020.
The initial phase dynamics of an epidemic without containment measures is commonly well modelled using exponential growth models. However, in the presence of containment measures, the exponential model becomes less appropriate. Under the implementation of an isolation measure for detected infectives, we propose to model epidemic dynamics by fitting a flexible growth model curve to reported positive cases, and to infer the overall epidemic dynamics by introducing information on the detection/testing effort and recovery and death rates. The resulting modelling approach is close to the Susceptible-Infectious-Quarantined-Recovered model framework. We focused on predicting the peaks (time and size) in positive cases, active cases and new infections. We applied the approach to data from the COVID-19 outbreak in Italy. Fits on limited data before the observed peaks illustrate the ability of the flexible growth model to approach the estimates from the whole data.
在没有遏制措施的情况下，传染病的初始阶段动态通常可以很好地用指数增长模型来模拟。然而，在存在遏制措施的情况下，指数模型变得不太适用。在对已发现的感染者实施隔离措施的情况下，我们建议通过将灵活的增长模型曲线拟合到报告的阳性病例来模拟传染病动态，并通过引入有关检测/测试工作以及康复和死亡率的信息来推断总体传染病动态。由此产生的建模方法接近于易感染者-感染者-隔离者-康复者模型框架。我们专注于预测阳性病例、活跃病例和新感染病例的峰值（时间和大小）。我们将该方法应用于意大利 COVID-19 疫情的数据。在观察到的峰值之前对有限数据的拟合说明了灵活增长模型能够接近整个数据的估计值。
