Infectious Respiratory Disease

Introduction and Rationale

Infectious respiratory diseases remain one of humanity’s deadliest health threats. Even after excluding COVID-19, non-COVID lower-respiratory infections (LRIs) caused an estimated 344 million illnesses and 2.18 million deaths worldwide in 2021, with half a million of those deaths occurring in children under five¹. Although global mortality rates have fallen by roughly 42% since 1990, LRIs still exact a disproportionate toll on the very young, the very old, and those in low-income areas.

Because running real-world experiments on pandemics is neither ethical nor feasible, policymakers and scientists rely on computational models to explore “what-if” scenarios before committing to costly (and sometimes inconvenient) interventions. One such model is the Susceptible Infected Recovered (SIR) model. It simulates how a disease spreads through a population by dividing everyone into three categories that change over time: Susceptible individuals who don’t have the disease and can catch it, Infectious individuals who currently have the disease and can transmit it, and Recovered (sometimes Removed) individuals who had the disease but do not have it anymore because they’ve recovered and developed immunity, or died.

This curriculum unit on infectious diseases contains three phases: Exploration, Research, and Programming. This rationale reflects a pattern seen in recent outbreaks of infectious respiratory disease where scientists gather trustworthy data, interpret it accurately, and build computational models that transform those findings into actionable insights. In the Exploration phase, students will be introduced to NetLogo. NetLogo is a programmable modeling environment specializing in simulations, making it remarkably straightforward for anyone to create customized SIR models. In the Research phase, students will select an infectious respiratory disease from a curated list and gather data on variables such as transmission rate, incubation period, and infectious period. In the Programming phase, students will create a model of their disease in NetLogo. The expectations are that they implement the unique attributes of their disease from the research phase into this model. The goal is to produce a simulation that generates a time-lapse visualization of agents moving, becoming infected, recovering, dying or gaining immunity, accompanied by a graph plotting infection numbers over time for analysis. At the conclusion of the Programming phase, students will present their findings to the class.

Through this sequence, students learn to read scientific literature critically, write code, visualize complex systems, and present evidence-based arguments that connect biology, mathematics, and computer science. By the unit’s end, students will be able to explain how transmission rate, R₀, mortality rates, and certain public health measures influence epidemic dynamics. Students will also practice communicating findings clearly, both in writing and orally.