Chowell Gerardo, Viboud Cécile
School of Public Health, Georgia State University, Atlanta, GA, USA; Division of International Epidemiology and Population Studies, Fogarty International Center, National Institutes of Health, Bethesda, MD, USA.
Division of International Epidemiology and Population Studies, Fogarty International Center, National Institutes of Health, Bethesda, MD, USA.
Infect Dis Model. 2016 Oct;1(1):71-78. doi: 10.1016/j.idm.2016.07.004. Epub 2016 Sep 3.
The increasing use of mathematical models for epidemic forecasting has highlighted the importance of designing models that capture the baseline transmission characteristics in order to generate reliable epidemic forecasts. Improved models for epidemic forecasting could be achieved by identifying signature features of epidemic growth, which could inform the design of models of disease spread and reveal important characteristics of the transmission process. In particular, it is often taken for granted that the early growth phase of different growth processes in nature follow early exponential growth dynamics. In the context of infectious disease spread, this assumption is often convenient to describe a transmission process with mass action kinetics using differential equations and generate analytic expressions and estimates of the reproduction number. In this article, we carry out a simulation study to illustrate the impact of incorrectly assuming an exponential-growth model to characterize the early phase (e.g., 3-5 disease generation intervals) of an infectious disease outbreak that follows near-exponential growth dynamics. Specifically, we assess the impact on: 1) goodness of fit, 2) bias on the growth parameter, and 3) the impact on short-term epidemic forecasts. Designing transmission models and statistical approaches that more flexibly capture the profile of epidemic growth could lead to enhanced model fit, improved estimates of key transmission parameters, and more realistic epidemic forecasts.
数学模型在疫情预测中的应用日益广泛，这凸显了设计能够捕捉基线传播特征的模型以生成可靠疫情预测的重要性。通过识别疫情增长的标志性特征，可以改进疫情预测模型，这些特征可为疾病传播模型的设计提供信息，并揭示传播过程的重要特征。特别是，人们常常理所当然地认为，自然界中不同增长过程的早期增长阶段遵循早期指数增长动态。在传染病传播的背景下，这个假设通常便于使用微分方程来描述具有质量作用动力学的传播过程，并生成繁殖数的解析表达式和估计值。在本文中，我们进行了一项模拟研究，以说明错误地假设指数增长模型来表征遵循近似指数增长动态的传染病爆发早期阶段（例如，3 - 5个疾病代间隔）的影响。具体而言，我们评估对以下方面的影响：1）拟合优度，2）增长参数的偏差，以及3）对短期疫情预测的影响。设计能够更灵活地捕捉疫情增长特征的传播模型和统计方法，可能会提高模型拟合度，改进关键传播参数的估计，并做出更现实的疫情预测。
