Social media is an easy-to-access platform providing timely updates about
societal trends and events. Discussions regarding epidemic-related events such
as infections, symptoms, and social interactions can be crucial for informing
policymaking during epidemic outbreaks. In our work, we pioneer exploiting
Event Detection (ED) for better preparedness and early warnings of any upcoming
epidemic by developing a framework to extract and analyze epidemic-related
events from social media posts. To this end, we curate an epidemic event
ontology comprising seven disease-agnostic event types and construct a Twitter
dataset SPEED with human-annotated events focused on the COVID-19 pandemic.
Experimentation reveals how ED models trained on COVID-based SPEED can
effectively detect epidemic events for three unseen epidemics of Monkeypox,
Zika, and Dengue; while models trained on existing ED datasets fail miserably.
Furthermore, we show that reporting sharp increases in the extracted events by
our framework can provide warnings 4-9 weeks earlier than the WHO epidemic
declaration for Monkeypox. This utility of our framework lays the foundations
for better preparedness against emerging epidemics.

社交媒体为时事趋势和事件提供了便捷获取信息的平台，我们通过开发一个提取和分析社交媒体帖子中流行病相关事件的框架，利用事件检测技术实现更好的灾害预防和预警，为新兴流行病的应对提供了基础。

社交媒体事件检测与疫情预测

Event Detection from Social Media for Epidemic Prediction

Early warnings for dynamical transitions in complex systems or
high-dimensional observation data are essential in many real world
applications, such as gene mutation, brain diseases, natural disasters,
financial crises, and engineering reliability. To effectively extract early
warning signals, we develop a novel approach: the directed anisotropic
diffusion map that captures the latent evolutionary dynamics in low-dimensional
manifold. Applying the methodology to authentic electroencephalogram (EEG)
data, we successfully find the appropriate effective coordinates, and derive
early warning signals capable of detecting the tipping point during the state
transition. Our method bridges the latent dynamics with the original dataset.
The framework is validated to be accurate and effective through numerical
experiments, in terms of density and transition probability. It is shown that
the second coordinate holds meaningful information for critical transition in
various evaluation metrics.

通过发展新方法：有向各向异性扩散图（directed anisotropic diffusion map），我们能够有效地提取出低维流形中的潜在演化动力学，并应用于真实的脑电图（EEG）数据，找到了适当的有效坐标，推导出能够检测状态转变过程中的临界点的早期预警信号。