| Abstract |
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. |
| Authors |
M. L. Lai   , Shaun S. Wulff , Yongtao Cao , Timothy J. Robinson , R.R.L.U.I. Rajapaksha
|
| Journal Info |
Elsevier BV | MethodsX , vol: 11
, pages: 102382 - 102382
|
| Publication Date |
12/1/2023 |
| ISSN |
2215-0161 |
Type |
article |
| Open Access |
gold
|
| DOI |
https://doi.org/10.1016/j.mex.2023.102382 |
| Keywords |
Transfer Learning (Score: 0.520224)
|
