SARS‑CoV‑2 Epidemiology

Epidemiology with agent-based models

Abstract

This project aims to present a computational simulation of disease transmission using agent based modeling techniques implemented in Python using the Mesa package. By developing a custom disease model, we investigate the effects of various factors such as infection rates, recovery types, age and vaccination availability on the dynamics of an infectious disease. The findings from our simulation aim to contribute to the broader understanding of disease transmission dynamics, which has been a major focus in various fields over the last several years. More specifically, this project exemplifies the power of agent based modeling in capturing complex phenomena, demonstrating to researchers how to explore and evaluate various scenarios in a controlled and computationally efficient manner, as well as informing public health interventions. Overall, this project demonstrates the successful application of agent based modeling techniques using the Mesa package in Python for simulating disease transmission. Through the utilization of various classes, the analysis of simulation outputs, and the exploration of different modeling methods, we aim to contribute to the growing body of knowledge in the field of data-driven epidemiology and provide valuable insights into disease dynamics.

See the full report and Jupyter notebook for further details.

References

Kermack, W.O., and McKendrick, A.G. 1927. “A Contribution to the Mathematical Theory of Epidemics.” Royal Society of London 115 (772): 700–721. https://doi.org/10.1098/rspa.1927.0118.

Miller, Thomas. 2022. “Model of an Epidemic.” Data Science Quarterly 1 (1/2).

Miller, Tom. “Queuing.” MSDS 460: Decision Analytics. Course at Northwestern University, Chicago, IL, June 4, 2023.