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Public Health Informatics: Surveillance & Population Health Analytics

Public Health Informatics: Surveillance & Population Health Analytics

Learn how to design and operate population-level surveillance systems, use AI & GIS for outbreak prediction, and implement health information systems to manage public-health crises.

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Expiry period Lifetime
Made in English
Last updated at Mon Sep 2025
Level
Beginner
Total lectures 40
Total quizzes 0
Total duration 0 Hours
Total enrolment 125
Number of reviews 0
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Short description Learn how to design and operate population-level surveillance systems, use AI & GIS for outbreak prediction, and implement health information systems to manage public-health crises.
Outcomes
  • Explain the scope and purpose of public health informatics and differentiate it from clinical informatics.
  • Identify common population-level data sources and describe standard terminologies and interoperability requirements.
  • Design a basic disease surveillance system suited to a defined population and health priority.
  • Apply data-cleaning and ETL practices to public-health datasets and evaluate data quality metrics (timeliness, completeness, validity).
  • Use descriptive statistics and time-series techniques to detect aberrations and early signals of outbreaks.
  • Build simple predictive models (classification/time-series) and interpret model outputs for public-health decision making.
  • Create effective dashboards and maps that communicate population health trends and guide interventions.
  • Describe legal, ethical, and equity issues in population data use and propose data governance approaches.
  • Produce a project plan or prototype (including technical and policy elements) for a surveillance/response intervention.
Requirements
  • Prerequisites (recommended): Undergraduate degree or professional experience in public health, health sciences, statistics, computer science, or related field OR demonstrable interest/experience in public-health work. Basic familiarity with statistics (mean/SD, rates, basic hypothesis testing). Comfortable using spreadsheets (Excel/Google Sheets).
  • Technical requirements: Computer (Windows/Mac/Linux) with internet access. Ability to install basic software (or use provided cloud notebooks). Recommended: Anaconda (Python) or R and RStudio; alternative: cloud Jupyter/RStudio environment provided in course. Optional but recommended: basic familiarity with Python (pandas, scikit-learn) or R (tidyverse) for labs. Modern web browser for dashboards and GIS labs.
  • Time commitment: ~4–6 hours per week (self-paced), 8–12 weeks total.

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