The advent of social media suggests that it may be possible to get vaccine hesitancy signals at an aggregate level, such as at the level of zip codes. Theoretically, machine learning models can be learned using socioeconomic (and other) features from publicly available sources, such as Twitter. Melotte and Kejriwal (2022)
Image Credit: Alachua County, Flickr
Research Articles
Generalizability challenges of mortality risk prediction models: A retrospective analysis on a multi-center database
PLOS Digital Health: published April 5, 2022 | https://doi.org/10.1371/journal.pdig.0000023
Predicting zip code-level vaccine hesitancy in US Metropolitan Areas using machine learning models on public tweets
PLOS Digital Health: published April 7, 2022 | https://doi.org/10.1371/journal.pdig.0000021
Internet of things–Enabled technologies as an intervention for childhood obesity: A systematic review
PLOS Digital Health: published April 7, 2022 | https://doi.org/10.1371/journal.pdig.0000024
A systematic review of the prediction of hospital length of stay: Towards a unified framework
PLOS Digital Health: published April 14, 2022 | https://doi.org/10.1371/journal.pdig.0000017