Geosocial Media’s Early Warning Capabilities Across US County-Level Political Clusters: Observational Study

JMIR Infodemiology, Год журнала: 2025, Номер 5, С. e58539 - e58539

Опубликована: Янв. 30, 2025

Background The novel coronavirus disease (COVID-19) sparked significant health concerns worldwide, prompting policy makers and care experts to implement nonpharmaceutical public interventions, such as stay-at-home orders mask mandates, slow the spread of virus. While these interventions proved essential in controlling transmission, they also caused substantial economic societal costs should therefore be used strategically, particularly when activity is on rise. In this context, geosocial media posts (posts with an explicit georeference) have been shown provide a promising tool for anticipating moments potential crises. However, previous studies early warning capabilities data largely constrained by coarse spatial resolutions or short temporal scopes, limited understanding how local political beliefs may influence capabilities. Objective This study aimed assess epidemiological COVID-19 vary over time across US counties differing beliefs. Methods We classified into 3 clusters, democrat, republican, swing counties, based voting from last 6 federal election cycles. we analyzed consecutive waves (February 2020-April 2022). specifically examined lag between signals surges cases, measuring both number days which preceded cases (temporal lag) correlation their respective series. Results differed clusters waves. On average, 21 republican compared 14.6 democrat 24.2 counties. general, were preceding 5 out all clusters. observed decrease that Furthermore, decline signal strength impact trending topics presented challenges reliability signals. Conclusions provides valuable insights strengths limitations tool, highlighting can change county-level Thus, findings indicate future systems might benefit accounting addition, declining role need assessed research.

Язык: Английский

Digital phenotyping for mental health based on data analytics: A systematic literature review

Artificial Intelligence in Medicine, Год журнала: 2025, Номер 163, С. 103094 - 103094

Опубликована: Март 1, 2025

Язык: Английский

Understanding Public Agencies' Expectations and Realities of AI-Driven Chatbots for Public Health Monitoring

Опубликована: Апрель 24, 2025

Язык: Английский