Interventions and Calibration

Go to Course: https://www.coursera.org/learn/interventions-and-calibration

Introduction

### Course Review and Recommendation: Interventions and Calibration **Course Name:** Interventions and Calibration **Platform:** Coursera **Overview:** The "Interventions and Calibration" course offers a profound dive into the mathematical modeling techniques pertinent to infectious diseases, particularly focusing on how interventions like vaccinations influence disease dynamics. This course is ideal for those interested in epidemiology, public health, and biostatistics, providing a solid mathematical foundation and practical skills in model calibration. #### Course Structure and Content: 1. **Modelling Interventions:** The course begins with an introduction to the SIR (Susceptible, Infected, Recovered) model and expands on it by incorporating additional compartments to better understand the dynamics of infectious disease interventions. Here, learners will investigate how vaccines can alter disease susceptibility and learn about "leaky" vaccines—those which reduce, but don’t completely eliminate, susceptibility to infection. This foundational module sets the stage for understanding how to simulate the impact of various public health interventions. 2. **Confronting Models with Data - Part A:** One of the critical aspects of effective modeling is calibration against real-world data. In this section, students will grapple with the basic relationships between mathematical models and observed data. By calibrating the basic SIR model to real epidemic data through hands-on exercises, learners gain personal insight into the process of modeling infectious diseases accurately. 3. **Confronting Models with Data - Part B & C:** Moving beyond manual calibration, these parts of the course delve into more sophisticated computational techniques. Participants will explore two prominent calibration approaches—least-squares and maximum-likelihood methods—using R programming. Practical exercises will enable learners to develop their ability to construct goodness-of-fit functions and implement algorithms to maximize these fits, ensuring their models align closely with empirical data. #### Learning Experience: The course employs a practical approach, combining theoretical discussions with hands-on computational work. By reinforcing the concepts with data calibration exercises, learners can appreciate the significance of model accuracy in public health decision-making. The structured syllabus ensures that participants build upon their knowledge gradually, from basic modeling concepts to complex data calibration methods. #### Target Audience: This course is perfect for those who have a background in public health, epidemiology, or data science, especially those looking to improve their quantitative analysis skills. Whether you are a student, a researcher, or a healthcare professional, you will find relevant applications of the knowledge gained in your respective fields. #### Recommendation: I highly recommend the "Interventions and Calibration" course on Coursera for anyone interested in the intersection of mathematics and public health. The blend of theoretical instruction and practical application provides invaluable insights into how interventions can shape the management of infectious diseases. The ability to use R for model calibration not only enhances your technical skillset but also prepares you to tackle real-world challenges in epidemic management. In summary, this course is an excellent investment for those eager to understand the intricacies of infectious disease dynamics and the critical role of mathematical modeling in public health policy.

Syllabus

Modelling Interventions

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.

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.

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.

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.