Virtual epidemic in a virtual city; simulating the spread of influenza in a US metropolitan area
Virtual epidemic in a virtual city; simulating the spread of influenza in a US metropolitan area
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DOI：
10.1016/j.trsl.2008.02.004
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发表时间：
2008-06-01
影响因子：
7.8
通讯作者：
Carley, Kathleen M.
中科院分区：
文献类型：
--
作者：
Lee, Bruce Y.;Bedford, Virginia L.;Carley, Kathleen M.
多种生物、生理、社会、经济和地理因素可能影响流感的传播、扩散和影响。最近对即将到来的流感流行的担忧产生了对预测性计算机模拟模型的需求，以预测流感的传播以及预防和控制策略的有效性。我们设计了一个基于代理的计算机模拟的理论流感流行在诺福克，弗吉尼亚州，其中包括广泛的城市级别的细节和计算机表示的每一个诺福克公民，包括他们的预期行为和社会交往。模拟在2002年11月27日（第87天）引入了200例感染病例，并跟踪了疫情的进展。平均而言，患病率在第178天达到峰值（占人群的12.2%）。我们的模型显示，流感病例在一周中的每一天都有周期性变化，周末接触的人较少，急诊室和诊所就诊的差异，流感病例的高峰期提前，65岁或以上人群的持续高流行率以及卫生保健工作者的每日感染率。我们的模拟模型中包含的详细程度使这些发现成为可能。与其他现有模型相比，我们的模型具有非常广泛和详细的社交网络，这可能很重要，因为具有更多社交互动和广泛社交网络的个体可能更容易传播流感。我们的模拟可以作为一个虚拟实验室，以更好地了解不同因素和干预措施影响流感传播的方式。
A wide variety of biologic, physiologic, social, economic, and geographic factors may affect the transmission, spread, and impact of influenza. Recent concerns about an impending influenza epidemic have generated a need for predictive computer simulation models to forecast the spread of influenza and the effectiveness of prevention and control strategies. We designed an agent-based computer simulation of a theoretical influenza epidemic in Norfolk, Va, that included extensive city-level details and computer representations of every Norfolk citizen, including their expected behavior and social interactions. The simulation introduced 200 infected cases on November 27, 2002 (day 87), and tracked the progress of the epidemic. On average, the prevalence peaked on day 178 (12.2% of the population). Our model showed a cyclical variation in influenza cases by day of the week with fewer people being exposed on weekends, differences in emergency room and clinic visits by day of the week, an earlier peak in influenza cases, and persistent high prevalence among people age 65 or older and the daily prevalence of infection among health-care workers. The level of detail included in our simulation model made these findings possible. Compared with other existing models, our model has a very extensive and detailed social network, which may be important because individuals with more social interactions and extensive social networks may be more likely to spread influenza. Our simulation may serve as a virtual laboratory to better understand the way different factors and interventions affect the spread of influenza.