SEIR-GA: A Dynamic Model for Unmasking Gerrymandering’s Impact on Voter Turnout and Representation

Abstract

This project introduces the SEIR-GA model (Susceptible, Exposed, Infected, Recovered, Gerrymandering, Activism), a new mathematical framework exploring how gerrymandered electoral districts affect voter turnout and democratic representation over time. Inspired by epidemiological models, SEIR-GA adapts these concepts to reflect the social and political processes that influence civic participation. The intercompartmental flow is governed by a coupled system of Ordinary Differential Equations (ODEs). In the model, individuals transition through various stages based on their awareness and response to gerrymandering. They begin in a "Susceptible" state, unaware of district manipulation. Upon becoming informed through media or other communication channels, they move into the "Exposed" state. Prolonged exposure without change may lead to the "Infected" state, in which individuals feel disillusioned and conclude their vote has little impact, resulting in disengagement. Some eventually reach the "Recovered" state, regaining civic motivation through education, activism, or community efforts that restore confidence in the process. A central feature of SEIR-GA is its ability to represent the conflict between political mechanisms that maintain power through unfair districting and citizen reform movements. The model captures how these forces interact over time, influencing voter turnout and political participation. SEIR-GA allows for the simulation of various political environments, from heavily gerrymandered regions to those with strong democratic safeguards. By quantifying both the suppressive and mobilizing effects of gerrymandering, this study provides a predictive tool for policymakers and advocates. Ultimately, it aims to strengthen voter representation and support democracy, advancing UN SDG Target 16.7 for inclusive, participatory, and representative decision-making.