Methodological Approaches to Planning and Analysis of N*

描述（由申请人提供）：准备和应对新型和再出现的传染病需要一系列数学模型，以：(a)理解和防止导致新疾病从纯粹的人畜共患风险出现到人际传播的最初步骤；(b)在疾病爆发的早期阶段评估疾病的关键参数，包括其繁殖数量、连续间隔、传染性的时间进程等；(c)提前计划控制措施，使用模型比较不同政策下疫情的发展过程（及其分布）。在这笔赠款中，我们将(a)制定控制政策模型，以尽量减少传染病在人类内部传播的第一个周期中的传播，可能在建立完全的人际传播之前；(b)开发实时使用的工具，根据疫情早期可获得的有限数据估计繁殖数量、控制措施的有效性和传染性的时间；(c)开发数学建模工具，在中等复杂程度的模型中捕捉现实疾病传播网络的关键特征——比完整的、基于个体的模拟模型不那么复杂，但比无法捕捉人口结构的简单微分方程模型更复杂。这项工作的目标是设计工具，以帮助理解和推广计算复杂的疾病传播模型的输出。

DESCRIPTION (provided by applicant): Preparing for and responding to novel and re-emerging infectious diseases requires a range of mathematical models to: (a) understand and prevent the initial steps leading to emergence of a new disease from a purely zoonotic risk to human-to-human spread; (b) evaluate key parameters of the disease, including its reproduction number, serial interval, time course of infectiousness, etc. in the early phases of an outbreak; and (c) plan in advance for control measures, using models to compare the course of the epidemic (and its distribution) across various policies. In this grant, we will (a) develop models of control policies to minimize the spread of an infectious disease during the first cycles of transmission within humans, possibly before full human-to-human transmission is established; (b) develop tools for use in real time to estimate the reproductive number, effectiveness of control measures, and timing of infectiousness from the limited data available early in an outbreak; and (c) develop mathematical modeling tools that capture the key features of realistic disease transmission networks in models of intermediate complexity - less complex than full, individual-based simulation models but more complex than the simple differential equation models that fail to capture population structure. The goal of this work is to design tools that will aid in understanding and generalizing the output of computationally complex models of disease transmission.