World-renowned Statistician George E. P. Box made the following statement. "All models are wrong, but some are useful." If models are wrong, why do we use them? Think about the weather where you are, behind every weather phenomenon is a mechanism. For example, low air pressure gives rise to wet and rainy weather conditions, and we can anticipate the way in which areas of low pressure move across the geography. In the same way behind every infectious disease, there is the basic mechanism of a host passing on the infection to other individuals. We call this transmission dynamics. When modeling a disease, we aim to capture these transmission dynamics in the most faithful way possible. Once we've done so, then we can use a model as a sort of experimental system. For example, we can ask what would happen to the burden of disease if we put different interventions into effect. One thing that's important to remember about infectious disease models is that we shouldn't see them as a crystal ball, allowing predictions in the same way as weather or even climate predictions. Although we have a good idea of the laws of physics that cause weather or climate, unfortunately, infectious diseases are much more complex. Instead of predicting the impact of the different interventions, a better word would be projecting. We are bringing together our best understanding of an infectious disease, and using a model to project what this information means for future scenarios. It turns out that even despite these limitations, infectious disease modeling can be very helpful for real world decision making. Infectious disease modeling is a great example of how an interdisciplinary approach to science can have significant impact on the world around us. The very background of modelers here at Imperial College and around the globe is testimony to that. Modelers come in many forms from mathematicians, physicists, and biologists, to name but a few. Each of these individuals carries with them not only a unique skill set but also a unique perspective on transmission dynamics, which allows for informed and impactful results. Undepending infectious disease modeling is mathematics. Through the use of differential equations and statistical techniques, we can project the outcome of different intervention scenarios and also study past epidemics. We can also break down complex processes and represent them in more accessible forms. Mathematics is not only essential in the building of models, it is also critical in the assessment of models and fundamental in accurately communicating underlying uncertainty found within them. In this specialization, you will learn how to develop your own infectious disease model. You will learn how to make sure your model agrees with real-world data and how to use this model to examine future intervention scenarios. In doing so, you will also learn how to interpret and critique a model that has been written by somebody else. The good news is that to do all of this, you don't need an advanced mathematical knowledge. However, it is essential that you have a good grasp of some core mathematical concepts. If you are a bit rusty in this area, please feel free to take the introductory quiz preceding this video, to check your mathematical skills before you begin. Don't let the maths scare you though. This scaffolded course will teach you how to build and assess models in a step-wise iterative manner. The emphasis is on the public health application, not the mathematics of these models. In order to effectively parameterize models, you must understand the biology and transmission dynamics behind disease, as well as the social and behavioral factors that influence disease transmission. When you do this, it becomes possible to explore a vast number of public health questions that are simply beyond traditional epidemiological field studies. A good example of this is HIV. This is a disease that can be endemic among high-risk populations and at the same time be much less prevalent throughout the rest of the population. To accurately model disease spread, we need to know any social or behavioral factors that drive transmission and the proportion of individuals that exhibit these behaviors. A process that requires good data capture. However, if done well, these data can help our understanding of transmission amongst individuals and improve our models. But more importantly provide public health professionals with answers that will directly impact HIV policy in clinical practice. As you will see, modeling can be applied to a variety of contexts, diseases, and hypothetical scenarios to explore the interactions that have occurred and those that might occur in the future. These results can inform policy change and influence our decision-making process. Provided all methods are clear, our assumption is transparent, and the conclusions well-grounded, modeling is a powerful tool that can help us tackle vital public health questions.
