arXiv (Cornell University) · 2020 · 15 citations · 0 references
Public HealthInfectious DiseasesPublic Health InformaticsCrisis ManagementDisease SurveillanceOutbreak DetectionOutbreak CommunicationOutbreak ControlOutbreak ManagementOutbreak ReportingOutbreak ResponseSocial Media SurveillanceBehavioral Warning SignsDisease Outbreak SurveillanceEarly Warning Signs AssessmentOutbreak Detection From Reported CasesOutbreak Early Warning SystemsOutbreak Signal DetectionPublic Health AlertingTemporal Lag
During a disease outbreak, timely non-medical interventions are critical in\npreventing the disease from growing into an epidemic and ultimately a pandemic.\nHowever, taking quick measures requires the capability to detect the early\nwarning signs of the outbreak. This work collects Twitter posts surrounding the\n2020 COVID-19 pandemic expressing the most common symptoms of COVID-19\nincluding cough and fever, geolocated to the United States. Through examining\nthe variation in Twitter activities at the state level, we observed a temporal\nlag between the rises in the number of symptom reporting tweets and officially\nreported positive cases which varies between 5 to 19 days.\n