@inproceedings{ea9296a0caeb4aaaa7f3346c44b11e47,
title = "A Data-Informed Approach for Analysis, Validation, and Identification of COVID-19 Models",
abstract = "The COVID-19 pandemic has generated an enormous amount of data, providing a unique opportunity for modeling and analysis. In this paper, we present a data-informed approach for building stochastic compartmental models that is grounded in the Markovian processes underlying these models. Our initial data analyses reveal that the SIRD model - susceptiple (S), infected (I), recovered (R), and death (D) - is not consistent with the data. In particular, the transition times expressed in the dataset do not obey exponential distributions, implying that there exist unmodeled (hidden) states. We make use of the available epidemiological data to inform the location of these hidden states, allowing us to develop an augmented compartmental model which includes states for hospitalization (H) and end of infectious viral shedding (V). Using the proposed model, we characterize delay distributions analytically and match model parameters to empirical quantities in the data to obtain a good model fit. Insights from an epidemiological perspective are presented, as well as their implications for mitigation and control strategies.",
keywords = "Coronavirus, COVID-19, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), Novel coronavirus, 2019-nCoV, Pandemic",
author = "Olmez, {S. Yagiz} and Jameson Mori and Erik Miehling and Tamer Basar and Smith, {Rebecca L.} and Matthew West and Mehta, {Prashant G.}",
note = "Funding Information: Research supported in part by the C3.ai Digital Transformation Institute sponsored by C3.ai Inc. and the Microsoft Corporation, in part by the Jump ARCHES endowment through the Health Care Engineering Systems Center of the University of Illinois at Urbana-Champaign, and in part by the National Science Foundation grant NSF-ECCS 20-32321. Publisher Copyright: {\textcopyright} 2021 American Automatic Control Council.; 2021 American Control Conference, ACC 2021 ; Conference date: 25-05-2021 Through 28-05-2021",
year = "2021",
month = may,
day = "25",
doi = "10.1101/2020.10.03.20206250",
language = "English (US)",
series = "Proceedings of the American Control Conference",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "3138--3144",
booktitle = "2021 American Control Conference, ACC 2021",
address = "United States",
}