SIAM (Society for Industrial and Applied Mathematics)

ISSN
Electronic: 1095-7200
Print: 0036-1445
Coden: siread

The SIAM Review consists of five sections, all containing articles of broad interest. Survey and Review features papers with a deliberately integrative and up-to-date perspective on a major topic in applied or computational mathematics or scientific computing. Research Spotlights publishes research papers of moderate length in applied and computational mathematics which will appeal to SIAM Review's diverse audience. The section publishes both traditional articles and, for example, papers about software, focused survey articles, or timely communications. SIGEST highlights a recent paper from one of SIAM's eleven specialized research journals, chosen on the basis of exceptional interest to the entire SIAM community and revised and condensed as needed for greater accessibility. Education consists primarily of individual modules that are self-contained presentations of specific topics in applied mathematics, scientific computation, or their applications; each module provides the primary material needed to teach a given topic as well as supplementary material. The Book Reviews section contains a featured review that provides an overview of several books in a subject area. Shorter reviews of individual books are also included.

Forecasting Elections Using Compartmental Models of Infection

Forecasting elections---a challenging, high-stakes problem---is the subject of much uncertainty, subjectivity, and media scrutiny. To shed light on this process, we develop a method for forecasting elections from the perspective of dynamical systems. Our model borrows ideas from epidemiology, and we use polling data from United States elections to determine its parameters. Surprisingly, our model performs as well as popular forecasters for the 2012 and 2016 U.S. presidential, senatorial, and gubernatorial races. Although contagion and voting dynamics differ, our work suggests a valuable approach for elucidating how elections are related across states. It also illustrates the effect of accounting for uncertainty in different ways, provides an example of data-driven forecasting using dynamical systems, and suggests avenues for future research on political elections. We conclude with our forecasts for the senatorial and gubernatorial races on 6 November 2018 (which we posted on 5 November 2018).

SIAM Rev. 62, pp. 837-865