SBMS7402 Course overview

Schedule, assessment, and policies

Yu Cheng Hsu

Course logistics

  • Weekly Lecture Time: Mondays, 18:30 – 20:30 (Semester 2)
  • Start Date: January 18, 2027
  • Venue: TBC (To be announced via Moodle / Course LMS)
  • Delivery Mode: In-person lectures and tutorials. (Note: Internship students are exempted from in-person attendance and may join via Zoom as approved).

Teaching Team

Role Name
Course Coordinator Dr. Ray Hsu
Guest speaker Dr. David Shih
Guest speaker Dr. Eric Wan
Guest speaker Prof. Joseph Wu
Guest speaker Prof. Qingpeng Zhang

Weekly Schedule

Session Date Topic / Activity Instructor
W01 18/01/2027 Introduction to Population and Global Health Dr. Ray Hsu
W02 25/01/2027 Data and Considerations in Using Public Data Dr. Ray Hsu
W03 01/02/2027 Study Designs in Healthcare Analytics Dr. Ray Hsu
— 08/02/2027 Public Holiday (CNY) —
W04 15/02/2027 Code Management and Programming Style Prof. David Shih
W05 22/02/2027 Healthcare Database Systems & EHR Architecture Dr. Ray Hsu
Session Date Topic / Activity Instructor
W06 01/03/2027 Categorical Data Analysis I: Handling Questionnaire Data Dr. Ray Hsu
W07 08/03/2027 Categorical Data Analysis II: Survival Analysis (Reading Week) Prof. Eric Wan
W08 15/03/2027 Temporal Perspective: Infectious Disease Modeling Prof. Joseph Wu
W09 22/03/2027 Network Analysis: Comorbidity & Health Networks Prof. Qingpeng Zhang
— 29/03/2027 Public Holiday (Easter) —
— 05/04/2027 Public Holiday (Ching Ming Festival) —
Session Date Topic / Activity Instructor
W10 12/04/2027 Spatial Perspective: Spatial Analysis of Disease Dr. Ray Hsu
W11 19/04/2027 Temporal Perspective: Disease Surveillance & Monitoring Dr. Ray Hsu
W12 26/04/2027 Synthesis & Discussion: Privacy, Concerns & Future of EHR Data Dr. Ray Hsu
Rev 03/05/2027 Revision Week: Zoom Tutorial & Consultation (4-hr block) Dr. Ray Hsu
Exam 10/05/2027 Final Written Examination (2-hr exam, 18:30 – 20:30) Course Team

Assessment Scheme

Component Weighting Description & Scope
Continuous Assessment 50% Practical programming/data analysis assignments, modeling exercises, and active class/online participation.
Final Examination 50% 2-hour written exam covering theoretical understanding, model interpretation, and case-based analytical problems.

Course Policies & Requirements

Attendance

  • Students are required to attend a minimum of 80% of all scheduled teaching sessions.
  • Attendance will be recorded weekly.

Academic Honesty & Plagiarism

  • Academic integrity is strictly enforced. All submitted coursework must be your own work.
  • Assignments will be screened using Turnitin.
  • Detailed guidelines on plagiarism can be accessed at: http://www.hku.hk/student/plagiarism/

Learning Resources

  • Lecture slides, tutorial datasets, and coding examples will be available on the course portal prior to each session.
  • Recommended readings and reference packages will be distributed alongside topic modules.

Course learning objectives

  1. To design and implement robust statistical models to analyse complex healthcare datasets, including EHRs, and public health surveillance streams
  2. To apply infectious disease modelling frameworks to simulate outbreaks and evaluate intervention strategies
  3. To critically assess data quality, bias, and ethical implications in large-scale healthcare analytics
  4. To translate analytical findings into actionable policies or clinical workflows to improve healthcare delivery and population outcomes