Syndromic Surveillance and Modeling for Infectious Disease

Synopsis

The outbreaks of SARS and swine flu have exposed the need for early outbreak detection and effective disease-spread simulation analysis for health resource management under pandemic outbreaks. Current surveillance systems lack the ability to interrogate disparate data and diverse datasets and sources, and are inaccurate in predicting infectious disease outbreaks and spread trends. This research will develop a radically new "syndromic surveillance" approach to enable reliable data-oriented infectious disease forecasting, simulation, and risk analysis. We shall:

  • Develop advanced data-mining methods to understand and extract disease transmission dynamics and mechanisms based on multiple infectious disease data sources.
  • Develop syndromic surveillance methods for analyzing public health related data for early detection of infectious disease outbreaks.
  • Develop stochastic influenza simulation and health economics models for mimicking disease-spread and risk assessment.
  • Validate the proposed research models through simulated outbreaks, clinical experiments and field experiments, and medical data from previous pandemic periods.

PC

TSUI, Kwok Leung (City University of Hong Kong)

Co-Is

  • CHAN, Ngai-Hang (City University of Hong Kong)
  • TSE, Wai Tat Peter (City University of Hong Kong)
  • LO, S. M. (City University of Hong Kong)
  • YUEN,  Kwok-kit Richard (City University of Hong Kong)
  • HO, P.L. (The University of Hong Kong)
  • WU, Tsz-kei Joseph (The University of Hong Kong)
  • CHAN, Antoni (City University of Hong Kong)
  • WONG, Shui-Yee Zoie (City University of Hong Kong)
  • LAI,  S. T. Thomas (HA Infectious Disease Centre, Princess Margaret Hospital)
  • CHOW, Chun-Bong (HA Infectious Disease Centre, Princess Margaret Hospital)

Collaborators

  • WU, Jeff (Georgia Institute of Technology)
  • GOLDSMAN, David M. (Georgia Institute of Technology)
  • LONGINI, Ira (University of Washington)
  • WOODALL, William H. (Virginia Polytechnic Institute and State University)
  • PECHT, Micheal (CALCE, University of Maryland)

Project Duration

1 Jun 2013 - 31 May 2016

Grant Type

CRF

Funded Amount

Around 5,170,000.00 HKD


Development of Efficient and Adaptable Simulation Models for Pandemic

Synopsis

We propose to develop influenza pandemic simulation models for epidemiologists, public health professions, and policy makers on region-based simulation in consideration of critical population dynamics, running applicable and visual simulation under desktop environment.

PI

TSUI, Kwok Leung (City University of Hong Kong)

Co-Is

  • WONG, Shui Yee (City University of Hong Kong)
  • WU, T. Joseph (The University of Hong Kong)
  • CHOW, Chun Bong (HA Infectious Disease Centre, Princess Margaret Hospital)
  • GOLDSMAN, David M. (Georgia Institute of Technology)
  • AZHAR, Nizam (Emory University)

Project Duration

1 Apr 2012 - 31 Mar 2014

Grant Type

RFCID

Funded Amount

Around 968,335.00 HKD


Safety, Reliability, and Disruption Management of High Speed Rail (HSR) Systems

Synopsis

The mission of the project is to achieve business service innovation of HSR systems that increases public confidence in HSR safety and creates consultancies and business opportunities.

Our goals are to:

  • guarantee safety by implementing self-cognizant fault detection and monitoring systems;
  • enable dependable train operation, performance, and service through advanced rail system design and reliability engineering; and
  • help administrators develop credible and sustainable regional and national public transport strategies and build up the global HSR system image.

PI

TSUI, Kwok Leung (City University of Hong Kong)

Co-Is

  • YAN, Hounin (City University of Hong Kong)
  • XIE, Min (City University of Hong Kong)
  • LO, Siu Ming (City University of Hong Kong)
  • ZHAO. J. Leon (City University of Hong Kong)

Project Duration

1 Jan 2012 - 31 Mar 2014

Grant Type

Seeding Fund

Funded Amount

Around 1,000,000.00 HKD


Modeling and Monitoring of Functional Computer Experiments

Synopsis

The objective of this research is to develop a systematic modeling approach that helps computer model developer and users (designers and scientists) (i) understand the impact and importance of validating the adequacy of functional computer models, (ii) model and monitor functional computer experiments.

  • interpretable and accurate meta-models that combine functional physical experiments and computer model output together.
  • computer model validation metrics and procedures that allow model develps to access the prediction capability and allow designers to optimize engineering designs.
  • a surveillance strategy for monitoring the adequacy and accuracy of functional computer models dynamically.
  • practical solutions for model developers, scientists, and engineering designers.

PI

TSUI, Kwok Leung (City University of Hong Kong)

Co-Is

HUNG, Ying (Rutgers University)

Project Duration

1 Jan 2011 - 31 Dec 2013

Grant Type

RGC General Research Fund (GRF)

Funded Amount

Around 761,994.00 HKD


Self-cognizant Prognostics for Electronics-rich Systems (Top-Up)

Synopsis

Current methods for reliability assessment of electronics-rich systems have fundamental flaws due to their inability to keep pace with new technologies, to account for complex usage profiles, and to address soft and intermittent faults which are common cause of failures.

This is especially problematic given the fact that these systems do commonly fail. Thus, this project is a radically new approach whose research goals include effective and efficient reliability prognostics and health management (PHM) for electronics-rich systems that continuously monitor themselves using algorithms that fuse sensor data, discriminate transient (intermittent) and false alarms from actual failures, correlate faults with relevant system events and mode changes, and predict failures in advance.

This project has: 

  • Develop a unique world-class PHM test laboratory;
  • Develop fault identification and prognostics technologies and software;
  • Demonstrate our methods and algorithms using simulated data (derived from previous experimental data from our team), experimental data from our test lab, and field data;
  • Conduct applied engineering research to address the impact of PHM implementation on business concerns, warranty issues, and return on investment.

PI

TSUI, Kwok Leung (City University of Hong Kong)

Co-Is

  • PECHT, Micheal (CALCE, University of Maryland)
  • CHAN, Ngai-Hang (City University of Hong Kong)
  • CHAN, Y. C. (City University of Hong Kong)
  • LI, Chi-Kwong (The Hong Kong Polytechnic University)
  • YUNG,  K.C. Winco (The Hong Kong Polytechnic University)
  • LEE, E. Y. Joshua (City University of Hong Kong)

Collaborators

  • WU, Jeff (Georgia Institute of Technology)
  • SANDBORN, Peter (University of Maryland)
  • KANG, Rui (Beihang University)

Project Duration

1 Feb 2010 - 31 Dec 2014

Grant Type

CRF

Funded Amount

Around 460,977.00 HKD