To mask or not to mask: Modeling the potential for face mask use by the general public to curtail the COVID-19 pandemic

Steffen E. Eikenberry; Marina Mancuso; Enahoro Iboi; Tin Phan; Keenan Eikenberry; Yang Kuang; Eric Kostelich; Abba B. Gumel

Mathematical modeling has been influential in providing deeper understanding on the transmission mechanisms and burden of the ongoing COVID-19 pandemic, contributing to the development of public health policy and understanding. Most mathematical models of the COVID-19 pandemic can broadly be divided into either population-based, SIR (Kermack-McKendrick)type models, driven by (potentially stochastic) differential equations [38, 20, 34, 22, 21, 23, 31, 26, 32, 24, 33] , or agent-based models [39, 28, 25, 27, 30] , in which individuals typically interact on a network structure and exchange infection stochastically. One difficulty of the latter approach is that the network structure is time-varying and can be difficult, if not impossible, to construct with accuracy. Population-based models, alternatively, may risk being too coarse to capture certain real-world complexities. Many of these models, of course, incorporate features from both paradigms, and the right combination of dynamical, stochastic, data-driven, and network-based methods will always depend on the question of interest.