A machine learning model for nowcasting epidemic incidence.

Autor: Sahai SY; Department of Computer Science and Engineering, The Ohio State University, United States of America. Electronic address: sahai.17@osu.edu., Gurukar S; Department of Computer Science and Engineering, The Ohio State University, United States of America., KhudaBukhsh WR; Division of Biostatistics and Mathematical Biosciences Institute, The Ohio State University, United States of America., Parthasarathy S; Department of Computer Science and Engineering, The Ohio State University, United States of America., Rempała GA; Division of Biostatistics and Mathematical Biosciences Institute, The Ohio State University, United States of America. Electronic address: rempala.3@osu.edu.
Jazyk: angličtina
Zdroj: Mathematical biosciences [Math Biosci] 2022 Jan; Vol. 343, pp. 108677. Date of Electronic Publication: 2021 Nov 27.
DOI: 10.1016/j.mbs.2021.108677
Abstrakt: Due to delay in reporting, the daily national and statewide COVID-19 incidence counts are often unreliable and need to be estimated from recent data. This process is known in economics as nowcasting. We describe in this paper a simple random forest statistical model for nowcasting the COVID-19 daily new infection counts based on historic data along with a set of simple covariates, such as the currently reported infection counts, day of the week, and time since first reporting. We apply the model to adjust the daily infection counts in Ohio, and show that the predictions from this simple data-driven method compare favorably both in quality and computational burden to those obtained from the state-of-the-art hierarchical Bayesian model employing a complex statistical algorithm. The interactive notebook for performing nowcasting is available online at https://tinyurl.com/simpleMLnowcasting.
Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
(Copyright © 2021 Elsevier Inc. All rights reserved.)
Databáze: MEDLINE
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