Mathematical and Statistical Modeling for Emerging and Re-emerging Infectious Diseases
Mathematical and Statistical Modeling for Emerging and Re-emerging Infectious Diseases
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DOI：
10.1007/978-3-319-40413-4
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发表时间：
2016
期刊：
影响因子：
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通讯作者：
G. Chowell;J. Hyman
中科院分区：
文献类型：
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作者：
G. Chowell;J. Hyman
数学建模者正在与生物学、流行病学、行为学和社会科学研究相结合，以更好地预测和更好地理解传染病的传播动态。他们正在与公共卫生工作者合作，创造新的工具，以制定有效的战略，最大限度地减少流行病的出现、影响和传播。为了使这些工具有用并得到使用，决策者必须充分了解用于定义模型的假设，例如流行病期间人口的任何行为变化，以及模型预测（例如感染人数）对这些假设的敏感程度。也就是说，模型公式的清晰描述和预测的敏感性分析对于量化模型预测中的不确定性都是必要的。流行病建模专家的这组文章描述了如何创建这些模型来捕捉新兴流行病的最重要方面。它提供了这些模型如何帮助公共卫生工作者更好地了解感染传播和减少疾病流行率估计的不确定性的例子。也就是说，分析和模型模拟可以量化驱动感染传播的复杂机制的相对重要性，并预测流行病的未来进程。除了专注于预测和控制感染的模型外，该卷还讨论了现代统计建模方法，以设计，实施和分析测量潜在疫苗有效性的临床试验。该卷的重点是基于传染媒介的潜在传播机制的模型，而不是对过去趋势的统计预测来预测未来的发病率。这些机制模型可以帮助预测的出现和评估的潜在有效性，使流行病得到控制的不同方法。最近，这些模型已被用于帮助理解和预测新出现和重新出现的传染病的传播，包括寨卡病毒，中东呼吸综合征，基孔肯雅病毒和埃博拉病毒。它们有助于更好地理解淋病、肺结核和支气管炎等已确立的疾病的抵抗力增加对免疫系统的影响。
Mathematical modelers are joining with biological, epidemiological, behavioral, and social science studies to produce better projections and better understanding of the transmission dynamics of infectious diseases. They are working with public health workers to create new tools for devising effective strategies to minimize the emergence, impact, and spread of epidemics. For these tools to be useful and used, the decision-makers must fully understand the assumptions, such as any behavior changes of the population during an epidemic, used in defining the model and how sensitive the model predictions, such as the number of people infected, depend upon these assumptions. That is, a clear description of the model formulation and sensitivity analysis of the predictions are both necessary to quantify the uncertainty in the model forecasts.This collection of articles by epidemic modeling experts describe how these models are created to capture the most important aspects of an emerging epidemic. It provides examples of how these models can help public health workers better understand the spread of infections and reduce the uncertainty of the estimates of disease prevalence. That is, the analysis and model simulations can quantify the relative importance of the complex mechanisms driving the spread of an infection and anticipate the future course of an epidemic. In addition to models focusing on forecasting and controlling infections, the volume contains a discussion on the modern statistical modeling methods to design, conduct, and analyze clinical trials measuring the effectiveness of potential vaccines. The focus of the volume is on models based on the underlying transmission mechanisms of an infectious agent, rather than statistical forecasting of past trends to predict future incidence. These mechanistic models can help anticipate the emergence and evaluate the potential effectiveness of different approaches for bringing an epidemic under control. Recently, the models have been used to help understand and predict the spread of emerging and re-emerging infectious diseases including Zika, Middle East Respiratory Syndrome, chikungunya, and Ebola. They have been helpful to better understand the impact of increased resistance of well-established diseases such as gonorrhea, tuberculosis, and bronchitis to the