Systematic analysis of the effectiveness of adding human mobility data to covid-19 case prediction linear models

Fuente: arXiv
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Main Authors: Abrar, Saad Mohammad, Awasthi, Naman, Smolyak, Daniel, Frias-Martinez, Vanessa
Format: Preprint
Published: 2024
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author Abrar, Saad Mohammad
Awasthi, Naman
Smolyak, Daniel
Frias-Martinez, Vanessa
author_facet Abrar, Saad Mohammad
Awasthi, Naman
Smolyak, Daniel
Frias-Martinez, Vanessa
contents Human mobility data has been extensively used in covid-19 case prediction models. Nevertheless, related work has questioned whether mobility data really helps that much. We present a systematic analysis across mobility datasets and prediction lookaheads and reveal that adding mobility data to predictive models improves model performance only for about two months at the onset of the testing period, and that performance improvements -- measured as predicted vs. actual correlation improvement over non-mobility baselines -- are at most 0.3.
format Preprint
id arxiv_https___arxiv_org_abs_2407_10304
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Systematic analysis of the effectiveness of adding human mobility data to covid-19 case prediction linear models
Abrar, Saad Mohammad
Awasthi, Naman
Smolyak, Daniel
Frias-Martinez, Vanessa
Computers and Society
Social and Information Networks
Human mobility data has been extensively used in covid-19 case prediction models. Nevertheless, related work has questioned whether mobility data really helps that much. We present a systematic analysis across mobility datasets and prediction lookaheads and reveal that adding mobility data to predictive models improves model performance only for about two months at the onset of the testing period, and that performance improvements -- measured as predicted vs. actual correlation improvement over non-mobility baselines -- are at most 0.3.
title Systematic analysis of the effectiveness of adding human mobility data to covid-19 case prediction linear models
topic Computers and Society
Social and Information Networks
url https://arxiv.org/abs/2407.10304