Characterizing Disease Spreading via Visibility Graph Embedding.

Gaining timely insights on real-world emergency events, such as infectious disease outbreaks, is critical for developing appropriate response strategies. In this work, we propose a data-driven approach to study the spreading dynamics of the global Covid-19 pandemic. Specifically, we aim to identify a set of most "similar" geographic regions as proxies for making predictions on a targeted location. Example predictions include the number of new cases, number of hospitalizations, and number of deaths. Such predictions can be made at different levels of regional granularities, including city, county, and state levels. Our approach starts by transforming regional time series into graph representations using the natural visibility graph (NVG) model in order to capture their intrinsic trends and properties. These graphs are then projected onto a common embedding space using graph-level network embedding techniques. Essentially, each time series is converted as a data point in a feature embedding space, where spatial proximity indicates similarity among time series. Given a targeted region, our approach can identify the most "relevant" geographic regions by finding its k-nearest neighbors in the embedding space. Subsequently, appropriate response strategies and policies (e.g., school shutdown, indoor dining restriction) can be adapted based on the success or failure experiences from relevant regions. Our approach will potentially provide valuable insights in mitigating the spreading of infectious disease.