Course Level: Beginner
Course Limit: 20
Format Requirements: This course will use a variety of formats including didactic lecture, individual and group work, problem solving and discussion of real world examples. Most of the time during the course will be spent on hands-on work: instructors will assist students to develop and manipulate pre-prepared models of disease transmission as described above. Students will gain insights into model construction and disease dynamics by changing model parameters and model structures, modifying population and pathogen characteristics, and adding simulated interventions to control diseases. This course is aimed at beginners. We assume no prior familiarity with modelling software. Material for the course will be provided electronically, including step-by-step guidance on model construction. Participants should bring a USB storage device. Participants will have access to a computational laboratory, in which the short course will be held.
Background: Control of infectious diseases poses an important challenge to healthcare and public health decision-makers. However, traditional “static” models for evaluation of infectious disease interventions are not always appropriate: they ignore the fundamentally transmissible nature of these diseases, which can lead to distorted estimates of program effectiveness and cost-effectiveness. In such cases, it may be preferable to employ transmission (or “dynamic”) models. These can capture effects such as the way that force of infection changes over time once an intervention is introduced, the tendency of infectious disease incidence to exhibit seasonal patterns, and the potential benefits associated with herd immunity. Transmission models are a broad class of models, including classical deterministic compartmental models, agent-based models, and other types of stochastic models. This course will introduce investigators to the construction, simulation, and interpretation of such models. Investigators will also learn about two widely used software platforms for developing and analyzing transmission models.
Description and Objectives: This course will review key concepts in infectious disease epidemiology such as the natural history of infection, the basic reproductive number, and herd immunity. This will be followed by a description of how dynamic models differ from static models; an outline of when dynamic models are more appropriate than static models; and an overview of standard “compartmental” transmission models. In the hands-on section, participants will construct and analyze a simple compartmental model of influenza transmission and control, using the Anylogic software package. The morning session will conclude with a case study of HPV vaccine cost-effectiveness based on a compartmental model.
In the afternoon, participants will learn about agent-based models (ABMs). The session will start with a description of ABMs, their relation to compartmental models, and an outline of situations where ABMs are more appropriate than compartmental models. The session will continue with an overview of agent-based infectious disease models. In the hands-on section, participants will learn how to construct, simulate, and analyze ABM models by manipulating an agent-based model of influenza transmission and control. Participants will again be using AnyLogic. The resulting model will be compared and contrasted to the compartmental model developed in the morning.
The objectives are to:
- Review infectious disease epidemiology
- Describe infectious disease modeling techniques in a health economic context.
- Show how to construct and analyze simple dynamic models in widely used software platforms.
- Discuss case studies of using dynamic models to support health policy decision-making.
- Understand the difference between static models and dynamic models and when dynamic models are appropriate.
- Understand the difference between various types of dynamic models: compartmental, agent-based, and other types, and which model is suitable for which problem.
- Become familiar with the role of heterogeneities in transmission of infectious diseases and their impact on intervention effectiveness.