Sub-national longitudinal and geospatial analysis of COVID-19 tweets

• Published in: PloS one 2020-10, Vol.15 (10), p.e0241330-e0241330
• Main Authors: Cuomo, Raphael E, Purushothaman, Vidya, Li, Jiawei, Cai, Mingxiang, Mackey, Timothy K
• Format: Article
• Language: English
• Subjects: Anesthesiology; Asymptomatic; Attitude to Health; Betacoronavirus; Community; Community Participation; Coronavirus Infections - epidemiology; Coronavirus Infections - psychology; Coronaviruses; COVID-19; Disease hot spots; Disease transmission; Distribution; Fatalities; Geography, Medical; Health aspects; Health promotion; Health surveillance; Humans; Infections; Keywords; Machine learning; Medical geography; Medicine; Online social networks; Outbreaks; Pandemics; Pneumonia; Pneumonia, Viral - epidemiology; Pneumonia, Viral - psychology; Prospective Studies; Public Health; Rural areas; Rural Population - statistics & numerical data; SARS-CoV-2; Social distancing; Social Media - statistics & numerical data; Social networks; Spacetime; Spatial analysis; Statistical analysis; Time Factors; Trends; United States - epidemiology; Urban Population - statistics & numerical data; Viral diseases
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0241330

According to current reporting, the number of active coronavirus disease 2019 (COVID-19) infections is not evenly distributed, both spatially and temporally. Reported COVID-19 infections may not have properly conveyed the full extent of attention to the pandemic. Furthermore, infection metrics are unlikely to illustrate the full scope of negative consequences of the pandemic and its associated risk to communities. In an effort to better understand the impacts of COVID-19, we concurrently assessed the geospatial and longitudinal distributions of Twitter messages about COVID-19 which were posted between March 3rd and April 13th and compared these results with the number of confirmed cases reported for sub-national levels of the United States. Geospatial hot spot analysis was also conducted to detect geographic areas that might be at elevated risk of spread based on both volume of tweets and number of reported cases. Statistically significant aberrations of high numbers of tweets were detected in approximately one-third of US states, most of which had relatively high proportions of rural inhabitants. Geospatial trends toward becoming hotspots for tweets related to COVID-19 were observed for specific rural states in the United States. Population-adjusted results indicate that rural areas in the U.S. may not have engaged with the COVID-19 topic until later stages of an outbreak. Future studies should explore how this dynamic can inform future outbreak communication and health promotion.