Shiqi Yao
Biographic Data
| ID | 3635543 |
|---|---|
| NAME | Shiqi Yao |
| GIVEN NAMES | Shiqi |
| FAMILY NAME | Yao |
| SIGNATURE | YAO S |
| AFFILIATIONS | Chinese University of Hong Kong |
| ORCID | 0000-0003-0844-2973 |
| VERIFIED | Yes |
| TOTAL WORKS | 3 |
| TOTAL CITATIONS | 6 |
| AUTHOR COUNT | 3 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2021 |
| LATEST PUBLICATION YEAR | 2024 |
| H-INDEX | 2 |
Multi-dimensional epidemiology and informatics data on Covid-19 wave at the end of zero Covid policy in China
Background: China exited strict Zero-COVID policy with a surge in Omicron variant infections in December 2022. Given China's pandemic policy and population immunity, employing Baidu Index (BDI) to analyze the evolving disease landscape and estimate the nationwide pneumonia hospitalizations in the post Zero COVID period, validated by hospital data, holds informative potential for future outbreaks. Methods: Retrospective observational analyses were…
Spatiotemporal Interpolation Using Graph Neural Network
Spatiotemporal interpolation is a widely used technique for estimating values at unsampled locations using the spatiotemporal dependencies in observations. Classic interpolation models face challenges, however, in dealing with the inherent nonlinearity and nonstationarity of spatiotemporal processes, particularly in sparse and irregularly sampled regions. To overcome these issues, we propose a novel model for spatiotemporal interpolation based on…
Integrated vaccination and physical distancing interventions to prevent future Covid-19 waves in Chinese cities
Integrated vaccination and physical distancing interventions to prevent future Covid-19 waves in Chinese cities
Spatiotemporal Interpolation Using Graph Neural Network
Spatiotemporal interpolation is a widely used technique for estimating values at unsampled locations using the spatiotemporal dependencies in observations. Classic interpolation models face challenges, however, in dealing with the inherent nonlinearity and nonstationarity of spatiotemporal processes, particularly in sparse and irregularly sampled regions. To overcome these issues, we propose a novel model for spatiotemporal interpolation based on…
Integrated vaccination and physical distancing interventions to prevent future Covid-19 waves in Chinese cities
Spatiotemporal Interpolation Using Graph Neural Network
Spatiotemporal interpolation is a widely used technique for estimating values at unsampled locations using the spatiotemporal dependencies in observations. Classic interpolation models face challenges, however, in dealing with the inherent nonlinearity and nonstationarity of spatiotemporal processes, particularly in sparse and irregularly sampled regions. To overcome these issues, we propose a novel model for spatiotemporal interpolation based on…
Multi-dimensional epidemiology and informatics data on Covid-19 wave at the end of zero Covid policy in China
Background: China exited strict Zero-COVID policy with a surge in Omicron variant infections in December 2022. Given China's pandemic policy and population immunity, employing Baidu Index (BDI) to analyze the evolving disease landscape and estimate the nationwide pneumonia hospitalizations in the post Zero COVID period, validated by hospital data, holds informative potential for future outbreaks. Methods: Retrospective observational analyses were…
2019-20 coronavirus outbreak (2 works) · Computer Science (2 works) · COVID-19 epidemiological studies (2 works) · Medicine (2 works) · Outbreak (2 works) · Virology (2 works) · Adjacency list (1 works) · Air Quality and Health Impacts (1 works) · Algorithm (1 works) · Artificial Intelligence (1 works)