Interventions and Calibration

所在平台: Coursera

课程主页: https://www.coursera.org/learn/interventions-and-calibration

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课程简介

课程名称：干预与模型校准 概述：本课程涵盖了传染病治疗建模的方法以及疫苗接种建模。建立在SIR模型的基础上，您将学习如何添加额外的系统，以表示干预的效果，例如疫苗接种在降低易感性方面的作用。您将了解“漏疫苗”及其建模方法，以及不同类型的疫苗和治疗效果。考虑模型与数据之间的基本关系很重要，因此，您将利用第一课中开发的基本SIR模型，将该模型校准到流行病数据。通过手动进行这种校准，您将获得关于如何调整模型参数以捕捉现实数据的理解。最后，在本课程中，您将学习两种基于计算机的模型校准简单方法——最小二乘法和最大似然法；您将使用R进行每种方法下的模型校准。 课程大纲： 1. **干预建模**：您将学习使用简单数学模型模拟传染病不同干预的影响，特别是疫苗接种如何减少易感性以及如何建模“漏疫苗”等不同的疫苗和治疗效果。 2. **与数据对抗 - 第一部分**：所有回答公共卫生问题的模型首先需要与现实数据进行匹配或“校准”，以确保模型模拟的动态与观察到的情况一致。您将使用目前开发的基本SIR模型，将该模型校准到流行病数据，并通过手动校准，理解如何调整模型参数。 3. **与数据对抗 - 第二部分**：在实际应用中， compartmental 模型的校准很少通过手动完成。您将学习一种构建函数的方法，该函数可以总结模型与数据之间拟合的好坏，并利用可用的计算机算法最大化这种拟合。在接下来的两个模块中，您将学习两种基于计算机的模型校准简单方法：最小二乘法和最大似然法，并在R中执行每种方法的模型校准。 4. **与数据对抗 - 第三部分**：请注意，学习成果与前一个模块相同。在这两个模块中，您将掌握使用计算机算法进行模型校准的基本方法，即最小二乘法和最大似然法，并将在R中执行相关的模型校准。

课程大纲

Name:Modelling Interventions

Description:Once you have captured the basic dynamics of transmission using simple mathematical models, it is possible to use these models to simulate the impact of different interventions. You will study approaches for modelling treatment of infectious disease, as well as for modelling vaccination. Building on the SIR model, you will learn how to incorporate additional compartments to represent the effects of interventions (for example, the effect of vaccination in reducing susceptibility). You will learn about ‘leaky’ vaccines and how to model them, as well as different types of vaccine and treatment effects.

Name:Confronting Models with Data - Part A

Description:All models answering public health questions first need to be matched, or ‘calibrated’, against real-world data to ensure that model-simulated dynamics are consistent with what is observed. In this module, you will consider basic relationships between models and data. Using the basic SIR model that you've developed so far, you will calibrate this model to epidemic data. Through performing this calibration by hand, you'll gain an understanding of how model parameters can be adjusted so as to order to capture real-world data.

Name:Confronting Models with Data - Part B

Description:In practice model calibration for compartmental models is rarely done by hand. Rather, we construct a function that summarises the goodness-of-fit between the model and the data and then use available computer algorithms to maximise this goodness-of-fit. In these next two modules, you will learn about two simple approaches to computer-based model calibration: the least-squares approach and the maximum-likelihood approach. You will perform model calibrations under each of these approaches in R.

Name:Confronting models with data – Part C

Description:Please note - learning outcomes are the same across both this and the last module. In practice, model calibration for compartmental models is rarely done by hand. Rather, we construct a function that summarises the goodness-of-fit between the model and the data and then use available computer algorithms to maximise this goodness-of-fit. In these two modules, you'll learn about two simple approaches to computer-based model calibration: the least-squares approach, and the maximum-likelihood approach. You will perform model calibrations under each of these approaches in R.

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