Disease Forecasting and Modeling
Many lessons have been learned from the COVID-19 pandemic. For public health workers, preparation, coordination, and communication have been recognized as critical steps to prepare for what comes next.
Among the many approaches to disease prevention and outbreak preparedness, many have turned to advanced data science and analysis methods to detect early signals of disease outbreaks. Specifically, disease modeling and forecasting have become en-vogue among public health professionals. During the COVID-19 pandemic, teams around the world used advanced statistics and mathematical modeling to identify trends and predict outcomes by employing disease interventions such as masking, isolation, and vaccination.
As a public health professional, you may ask yourself if you should start learning and applying these skills to your work. The Cornell Health Impacts Core would like to provide an introduction to disease modeling and forecasting to help you become more acquainted with the terminology, what is involved, and how these methods are used. However, it is imperative to understand that the people using these methods have trained for a long time to be able to forecast diseases using statistical or mathematical models. Developing, training, and trusting these models, along with machine learning, requires practice, training, and expertise that take a long time to develop, especially if you want to use these techniques for making decisions that impact people’s lives.
Explore the guides to learn more about the methods being used for disease forecasting and modeling. We also provide links to courses, readings, and additional references to help you learn more about this emerging field.
We recommend starting with this guide to think about which methods are most appropriate for the questions you have or objectives you are trying to fulfill. The Data and decision-making guide will walk you through basic models used in disease forecasting and modeling.
Data and decision-making guide
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I have access to Excel. What can I do with Excel that can help me with outbreak detection, prevention, and response?
Excel for Outbreak Management: Quick Guide
When and why would I use Python vs. R vs. Tableau to help with my public health decision-making?
Python vs. R vs. Tableau Quick Guide
EpiEstim sounds interesting. How does this work? What data do I need to actually make the outputs at least somewhat reliable?
Estimating Reproduction Number with EpiEstim: Quick Guide
Machine learning sounds cool. What is machine learning, and how can I use it for forecasting?
Machine Learning in Epidemics: Quick Guide
Ensemble modeling: What is it, and how can I do it?
What about TB Modeling and resources?
Examples of tools that you might introduce:
Resources the NACCHO Demo Site Teams are using to build their skills
Modeling and forecasting resources
Applied Epi – hands-on learning for using R for epidemiology.
Modeling 101 For Public Health on CSTE Learn
Introduction to Infectious Disease Modeling – Center for Communicable Disease Dynamics, Harvard University
Infectious Disease Transmission Models for Decision-Makers – Johns Hopkins University
Infectious Disease Modelling Specialization – Imperial College London
ASTHO Disease Forecasting Learning Series
Yale Summer Course in Public Health Modeling – June 9-14, 2024 at Yale University
Johns Hopkins has released an additional Intermediate level Modeling Course: Infectious Disease Modeling in Practice.