Methodological Approaches to Planning and Analysis of N*

说明(申请人提供)：准备和应对新出现的和重新出现的传染病需要一系列数学模型：(A)了解和预防导致新疾病出现的最初步骤，从纯粹的人畜共患病风险到人与人之间的传播；(B)在疫情爆发的早期阶段评估疾病的关键参数，包括其繁殖数量、序列间隔、传染性的时间进程等；以及(C)提前规划控制措施，使用模型比较流行病的进程(及其在不同政策中的分布)。在这笔赠款中，我们将(A)开发控制政策模型，以最大限度地减少传染病在人类内部传播的第一个周期期间的传播，可能是在确定完全的人与人之间的传播之前；(B)开发工具，用于根据暴发早期有限的数据实时估计繁殖数量、控制措施的有效性和传染性的时机；以及(C)开发数学建模工具，以中等复杂性的模型捕捉现实疾病传播网络的关键特征--比完全的、基于个人的模拟模型复杂，但比未能捕捉人口结构的简单的微分方程式模型更复杂。这项工作的目标是设计工具，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.