2020/05/01 by Erfaneh Gharavi, Neda Nazemi, Gharavi, Erfaneh +3
Medicine · Social Sciences · #Computers and Society (cs.CY) #Data-Driven Disease Surveillance #FOS: Computer and information sciences #Misinformation and Its Impacts #Public Relations and Crisis Communication #Respiratory viral infections research #Social and Information Networks (cs.SI)
paper · pdf · doi:10.48550/arxiv.2005.00475
openalex publication_date 2020/05/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
During a disease outbreak, timely non-medical interventions are critical in preventing the disease from growing into an epidemic and ultimately a pandemic. However, taking quick measures requires the capability to detect the early warning signs of the outbreak. This work collects Twitter posts surrounding the 2020 COVID-19 pandemic expressing the most common symptoms of COVID-19 including cough and fever, geolocated to the United States. Through examining the variation in Twitter activities at the state level, we observed a temporal lag between the rises in the number of symptom reporting tweets and officially reported positive cases which varies between 5 to 19 days.