Evidence map›Paper›PMID 37822674›Full record

ArticleMethodsX2023

An interpretable time series machine learning method for varying forecast and nowcast lengths in wastewater-based epidemiology.

Mallory Lai, Shaun S Wulff, Yongtao Cao, Timothy J Robinson, Rasika Rajapaksha

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in MethodsX, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
1.9field-weighted citation impact, top 11% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed, 11 citations in OpenAlex.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors at 3 institutions in 2 countries.

Mallory LaiDepartment of Mathematics and Statistics, University of Wyoming, 1000 E University Ave, Laramie, WY, USA.
Shaun S WulffDepartment of Mathematics and Statistics, University of Wyoming, 1000 E University Ave, Laramie, WY, USA.
Yongtao CaoDepartment of Mathematical and Computer Sciences, Indiana University of Pennsylvania, 210 South Tenth Street, IN, USA.
Timothy J RobinsonDepartment of Mathematics and Statistics, University of Wyoming, 1000 E University Ave, Laramie, WY, USA.
Rasika RajapakshaDepartment of Computer Systems Engineering, University of Kelaniya, University Drive, Bulugaha Junction, Kelaniya, Colombo, Sri Lanka.
University of Wyoming · USIndiana University of Pennsylvania · USUniversity of Kelaniya · LK

Funding

Wyoming INBRE Phase 4- Equipment Supplement for x-ray diffractometer for Center for Advanced Scientific InstrumentationP20GM103432 · NIGMS · UNIVERSITY OF WYOMING · PI Nicolas A. Blouin · 2012 to 2026
$56.8M
NIGMS NIH HHS P20 GM103432
6 · The paper itself

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.

Indexed as

ARIMA modelFeature engineeringFeature selectionGradient boostingPandemic managementPredictionStationarityTemporal processTime Series Machine Learning (TSML)

Identifiers

PMID37822674
PMCPMC10562867
OpenAlexW4387101779

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.