论文标题
使用Twitter数据进行积极危机管理的早期爆发检测：COVID-19美国的案例研究
Early Outbreak Detection for Proactive Crisis Management Using Twitter Data: COVID-19 a Case Study in the US
论文作者
论文摘要
在疾病暴发期间，及时的非医学干预措施对于防止该疾病成长为流行病，最终是大流行至关重要。但是，采取快速措施需要能力检测爆发的预警信号。这项工作收集了围绕2020年Covid-19-19大流行的Twitter帖子，表达了COVID-19的最常见症状，包括咳嗽和发烧，地理位置到美国。通过检查州一级的Twitter活动的差异，我们观察到症状报告推文的数量上升与正式报告的阳性病例之间的时间滞后，这在5至19天之间有所不同。
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.
