Modeling Infectious Diseases (SIR)
My last post discussed how #AI, #modeling, and #analytics complement each other and synergistically drive performance and decision-making. This one illustrates their interactions through epidemiology’s SIR (Susceptible, Infected, Recovered) model. The basic structure and equations are shown in the image along with a mathematical expression describing isolation strategies.
All models are simplistic approximations, but they can help us better understand knowns and unknowns (opportunities, risks, and uncertainties), develop benchmarks, and prioritize resources. They also help engineers and analysts dynamically capture and analyze data as events unfold.
Models can help healthcare providers better understand the spread of infectious disease, including epidemics, and their impacts on capacity and other resources. State and local health departments use them for planning and preparedness. Models can help policymakers, political leaders, and legislatures understand threats and set budget priorities.
I use models like the SIR to better understand threats like the COVID pandemic on business, the economy, and supply chains. The image and process are based on training provided by Imperial College, London.