Title: Forecasting elections using compartmental models of infections,
Abstract: To forecast political elections, popular pollsters gather polls and combine
information from them with fundamental data such as historical trends, the
national economy, and incumbency. This process is complicated, and it includes
many subjective choices (e.g., when identifying likely voters, estimating
turnout, and quantifying other sources of uncertainty), leading to forecasts
that differ between sources even when they use the same underlying polling
data. With the goal of shedding light on election forecasts (using the United
States as an example), we develop a framework for forecasting elections from
the perspective of dynamical systems. Through a simple approach that borrows
ideas from epidemiology, we show how to combine a compartmental model of
disease spreading with public polling data to forecast gubernatorial,
senatorial, and presidential elections at the state level. Our results for the
2012 and 2016 U.S. races are largely in agreement with those of popular
pollsters, and we use our new model to explore how subjective choices about
uncertainty impact results. Our goals are to open up new avenues for improving
how elections are forecast, to increase understanding of the results that are
reported by popular news sources, and to illustrate a fascinating example of
data-driven forecasting using dynamical systems. We conclude by forecasting the
senatorial and gubernatorial races in the 2018 U.S. midterm elections of 6
November.