Learning infectious disease epidemiology in a modern framework

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Authors Andreas Handel
Journal/Conference Name PLoS Computational Biology
Paper Category
Paper Abstract Modern infectious disease epidemiology makes heavy use of computational model–based approaches and a dynamical systems perspective. The importance of analyzing infectious diseases in such a way keeps increasing. However, infectious disease epidemiology is still often taught mainly from a medical and classical epidemiological study design (e.g., cohort, casecontrol) perspective. While textbooks and other resources that teach a model-based approach to infectious diseases exist, almost any such teaching material requires students to work with mathematical models and write computer code. This is a significant barrier for students who do not have a strong mathematical background or prior coding experience, which applies to many students in public health and related biomedical disciplines. It limits the number of students who can or want to engage with infectious disease epidemiology by using modern, systems modeling– based approaches. New tools and approaches are needed to reach a wider audience and allow students to learn concepts such as the reproductive number, herd immunity, critical community size, and the population-level impact of interventions from a dynamical systems and model perspective, without the obstacles of coding or having to formulate and analyze differential equations. Here, I describe a new software package for the widely used R language that allows individuals to explore and study concepts of infectious disease epidemiology by using a modern, dynamical systems model framework, without the need to read or write computer code. The package includes documentation and material to serve as a stand-alone tool—supplemented as needed with provided references—for students to get an introduction to important modern infectious disease concepts. The package is built in a modular way that allows a student to seamlessly continue on their journey of learning infectious disease modeling if they choose to do so. The different ways to use the package are described in detail, and examples are provided.
Date of publication 2017
Code Programming Language R

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