A Big Data Predictive Decision System for COVID-19 Epidemic in Hong Kong
- Qingpeng ZHANG (Principal Investigator / Project Coordinator)School of Data Science
- Dirk Udo PFEIFFER (Co-Principal Investigator)Department of Infectious Diseases and Public Health
- S Joe QIN (Co-Principal Investigator)School of Data Science
- Hsiang-Yu Sean YUAN (Co-Principal Investigator)Department of Biomedical Sciences
DescriptionThis project aims to develop a prototype of an advanced big data-based predictive decision system for the monitoring and control of COVID-19 epidemic in Hong Kong. The team aims to develop extensive functional modules, including the data collection module, machine learning-based prediction models for Hong Kong and other regions, and the construction of a detailed simulation model of the human movement and public transportation in Hong Kong. The proposed system consists of three major components:1. An interactive and comprehensive visualization-based disease monitoring platform;2. A hybrid machine learning-based prediction module that predicts the counts of COVID-19 infected cases, severe cases, and death cases;3. An interactive decision-making platform for the simulation of various epidemic and control scenarios, and the optimization of control measures based on the reinforcement learning output. Specifically, the machine-learning-based prediction models have provided important insights into the transmissibility of COVID-19 in the re-opening stage.
|Effective start/end date||1/07/21 → …|