Smirnova Alexandra, Chowell Gerardo
Department of Mathematics and Statistics, Georgia State University, Atlanta, USA.
School of Public Health, Georgia State University, Atlanta, USA.
Infect Dis Model. 2017 May 25;2(2):268-275. doi: 10.1016/j.idm.2017.05.004. eCollection 2017 May.
Public health officials are increasingly recognizing the need to develop disease-forecasting systems to respond to epidemic and pandemic outbreaks. For instance, simple epidemic models relying on a small number of parameters can play an important role in characterizing epidemic growth and generating short-term epidemic forecasts. In the absence of reliable information about transmission mechanisms of emerging infectious diseases, phenomenological models are useful to characterize epidemic growth patterns without the need to explicitly model transmission mechanisms and the natural history of the disease. In this article, our goal is to discuss and illustrate the role of regularization methods for estimating parameters and generating disease forecasts using the generalized Richards model in the context of the 2014-15 Ebola epidemic in West Africa.
公共卫生官员越来越认识到开发疾病预测系统以应对流行病和大流行病爆发的必要性。例如，依赖少量参数的简单流行病模型在刻画疫情增长和生成短期疫情预测方面可以发挥重要作用。在缺乏关于新兴传染病传播机制的可靠信息的情况下，现象学模型有助于刻画疫情增长模式，而无需明确模拟传播机制和疾病的自然史。在本文中，我们的目标是讨论并说明正则化方法在使用广义理查兹模型估计参数和生成疾病预测方面的作用，该模型应用于2014 - 15年西非埃博拉疫情的背景下。
