An interpretable time series machine learning method for varying forecast and nowcast lengths in wastewater-based epidemiology.
| Autor: | Lai M; Department of Mathematics and Statistics, University of Wyoming, 1000 E University Ave, Laramie, WY, USA., Wulff SS; Department of Mathematics and Statistics, University of Wyoming, 1000 E University Ave, Laramie, WY, USA., Cao Y; Department of Mathematical and Computer Sciences, Indiana University of Pennsylvania, 210 South Tenth Street, IN, USA., Robinson TJ; Department of Mathematics and Statistics, University of Wyoming, 1000 E University Ave, Laramie, WY, USA., Rajapaksha R; Department of Computer Systems Engineering, University of Kelaniya, University Drive, Bulugaha Junction, Kelaniya, Colombo, Sri Lanka. |
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| Jazyk: | angličtina |
| Zdroj: | MethodsX [MethodsX] 2023 Sep 27; Vol. 11, pp. 102382. Date of Electronic Publication: 2023 Sep 27 (Print Publication: 2023). |
| DOI: | 10.1016/j.mex.2023.102382 |
| Abstrakt: | Wastewater-based epidemiology has emerged as a viable tool for monitoring disease prevalence in a population. This paper details a time series machine learning (TSML) method for predicting COVID-19 cases from wastewater and environmental variables. The TSML method utilizes a number of techniques to create an interpretable, hypothesis-driven framework for machine learning that can handle different nowcast and forecast lengths. Some of the techniques employed include:•Feature engineering to construct interpretable features, like site-specific lead times, hypothesized to be potential predictors of COVID-19 cases.•Feature selection to identify features with the best predictive performance for the tasks of nowcasting and forecasting.•Prequential evaluation to prevent data leakage while evaluating the performance of the machine learning algorithm. Competing Interests: 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. (© 2023 Published by Elsevier B.V.) |
| Databáze: | MEDLINE |
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