Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA 30332-0205, USA.
Epidemiol Infect. 2010 Oct;138(10):1472-81. doi: 10.1017/S0950268810000300. Epub 2010 Feb 17.
As the 2009 H1N1 influenza pandemic (H1N1) has shown, public health decision-makers may have to predict the subsequent course and severity of a pandemic. We developed an agent-based simulation model and used data from the state of Georgia to explore the influence of viral mutation and seasonal effects on the course of an influenza pandemic. We showed that when a pandemic begins in April certain conditions can lead to a second wave in autumn (e.g. the degree of seasonality exceeding 0.30, or the daily rate of immunity loss exceeding 1% per day). Moreover, certain combinations of seasonality and mutation variables reproduced three-wave epidemic curves. Our results may offer insights to public health officials on how to predict the subsequent course of an epidemic or pandemic based on early and emerging viral and epidemic characteristics and what data may be important to gather.
正如 2009 年 H1N1 流感大流行（H1N1）所表明的那样，公共卫生决策者可能不得不预测大流行的后续进程和严重程度。我们开发了一个基于代理的仿真模型，并使用佐治亚州的数据来探索病毒突变和季节性效应对流感大流行进程的影响。我们表明，当大流行在 4 月开始时，某些条件可能会导致秋季出现第二波疫情（例如，季节性超过 0.30，或者每天的免疫力丧失率超过 1%）。此外，某些季节性和突变变量的组合再现了三波流行曲线。我们的研究结果可以为公共卫生官员提供有关如何根据早期和新兴的病毒和流行特征以及哪些数据可能很重要来预测疫情或大流行的后续进程的见解。
