Forecasting elections is a high-stakes problem. Politicians and voters alike are often desperate to know the outcome of a close race, but providing them with incomplete or inaccurate predictions can be misleading. And election forecasting is already an innately challenging endeavor the modeling process is rife with uncertainty, incomplete information, and subjective choices, all of which must be deftly handled. Political pundits and researchers have implemented a number of successful approaches for forecasting election outcomes, with varying degrees of transparency and complexity. However, election forecasts can be difficult to interpret and may leave many questions unanswered after close races unfold.Voters in different states may exert on each other, since accurately accounting for interactions between states is crucial for the production of reliable forecasts.

The strength of a state's influence can depend on a number of factors, including the amount of time that candidates spend campaigning there and the state's coverage in the news. To develop their forecasting approach, the team repurposed ideas from the compartmental modeling of biological diseases. Mathematicians often utilize compartmental models which categorize individuals into a few distinct types (i.e., compartments) to examine the spread of infectious diseases like influenza and COVID-19. A widely-studied compartmental model called the susceptible-infected-susceptible (SIS) model divides a population into two groups: those who are susceptible to becoming sick and those who are currently infected. The SIS model then tracks the fractions of susceptible and infected individuals in a community over time, based on the factors of transmission and recovery.