RAPID: Creating and Analyzing Hybrid Multiscale Models for Forecasting and Mitigating an Outbreak of the Ebola Virus Disease

这项研究将创建和分析预测和减轻埃博拉病毒病传播的数学模型。研究人员建议通过开发一种新的综合模型来结合三种常见建模方法的优势。其结果将是一个更强大的模型，可用于提供有价值和及时的信息，以帮助控制当前的流行病并为未来的决策提供信息。标准的建模方法，如常微分方程模型、网络模型和基于个体的模型，都有共同的局限性。单独来看，这些模型都不足以反映2014年埃博拉疫情的复杂性。研究人员将以现有的数学分析、数据和软件为基础，将这些方法与世界卫生组织（WHO）和疾病控制中心（CDC）的数据结合起来。这种基于主体的模型将被整合到以城市和县为基础的迁移、卫生保健、应对和迁移工作网络模型中。该模型的具体目标包括预测埃博拉病毒病的发病率，量化疫情早期阶段的不确定性，估计向新区域的传播，量化干预措施和行为变化的影响，利用统计分析估计漏报程度，量化模型？S不确定性，并确定不确定性将如何影响不同的缓解方法。

This research will create and analyze mathematical models for predicting and mitigating the spread of Ebola Virus Disease. The investigators propose to combine the strengths of three common modeling approaches by developing a new, integrative model. The result will be a more robust model that can be used to provide valuable and timely information to help control the current epidemic and inform future decision making.Standard modeling approaches such as ordinary differential equation models, network models and individual based models, have common limitations. Alone, none of these models are sufficient to capture the complexity of the 2014 Ebola epidemic. The investigators will build on existing mathematical analysis, data, and software to combine these approaches on data from the World Health Organization (WHO) as well as the Centers for Disease Control (CDC). This agent-based model will be integrated in a city and county-based network model for the migration, health care, response, and migration efforts. Specific aims for the model include forecasting the incidence of Ebola virus disease, quantifying the uncertainty in the early stages of the epidemic, estimating the spread to new regions, quantifying the impact of interventions and behavior changes, utilizing statistical analysis to estimate the degree of underreporting, quantifying the model?s uncertainty, and determining how uncertainty will impact the different mitigation approaches.