The 21st century has seen major epidemics and pandemics caused by infectious diseases like coronaviruses, influenza, and most recently, monkey pox. Infectious disease spread within the human population can be conceptualized as a complex system composed of individuals that interact and transmit viruses via spatiotemporal processes that manifest across and between scales. The complexity of this system ultimately means that infectious disease spread is difficult to understand, predict, and effectively respond to. As spatial data becomes increasingly available at high spatial and temporal resolutions and computing resources can more efficiently handle such data, there have been opportunities for new data science and simulation-based solutions towards improved public health.
Proceeding Downloads
Optimal Risk-aware POI Recommendations during Epidemics
The movement of people can influence the spread of diseases, especially in populated areas. While measures like quarantine can curb disease spread by restricting the movement of those infected, they come with socioeconomic consequences. Furthermore, not ...
EPIPOL: An Epidemiological Patterns of Life Simulation (Demonstration Paper)
This paper introduces the EPIPOL disease simulation model, constructed upon the Patterns-of-life simulation, designed to produce human trajectory data. Over recent years, a surge in disease simulation models has been observed, each distinctive in its ...
Index Terms
- Proceedings of the 4th ACM SIGSPATIAL International Workshop on Spatial Computing for Epidemiology