Soman, Satejl Caitlin Loftus; Steven Buschbach, Manasi Phadnis and Luis M. A. Bettencourt
As pandemics (including the COVID-19 crisis) pose threats to societies, public health oﬀicials, epidemiologists, and policymakers need improved tools to assess the impact of disease, as well as a framework for understanding the effects and tradeoffs of health policy decisions. The epimargin package provides functionality to answer these questions in a way that incorporates and quantifies irreducible uncertainty in both the input data and complex dynamics of disease propagation. The epimargin software package primarily consists of: 1. a set of Bayesian estimation procedures for epidemiological metrics such as the reproductive rate (Rt), which is the average number of secondary infections caused by an active infection 2. a flexible, stochastic epidemiological model informed by estimated metrics and reflecting real-world epidemic and geographic structure, and 3. a set of tools to evaluate different public health policy choices simulated by the model. The software is implemented in the Python 3 programming language and is built using commonly-used elements of the Python data science ecosystem, including NumPy (Harris et al., 2020), Scipy (Virtanen et al., 2020), and Pandas (McKinney & others, 2011).