
AI for Public Health & Epidemiology: Disease Surveillance, Outbreak Forecasting & Population Health Analytics
Integrating Artificial Intelligence, Epidemiological Modeling & Population Health Analytics for Disease Surveillance and Outbreak Forecasting.
Skills you will gain:
About Program:
This workshop introduces participants to the transformative role of Artificial Intelligence in Public Health and Epidemiology. It focuses on how AI-driven systems are revolutionizing disease surveillance, outbreak prediction, and population health monitoring. Through a structured learning approach, participants will explore how large-scale health data is used to generate actionable insights for improving global healthcare outcomes and public health decision-making.
Aim:
This workshop aims to train participants in the application of artificial intelligence in public health and epidemiology, focusing on disease surveillance, outbreak prediction, health data analytics, and population-level decision-making systems.
Participants will gain conceptual and practical understanding of how AI is used to monitor disease spread, analyze epidemiological trends, support public health policy, and enable early outbreak detection using real-world datasets and accessible analytical tools.
Program Objectives:
Introduce fundamentals of artificial intelligence in public health
Explain epidemiological data analysis and disease surveillance systems
Demonstrate AI-based outbreak prediction and trend analysis concepts
Train participants in population health data interpretation
Explore applications in infectious and chronic disease monitoring
Develop understanding of AI-driven public health decision systems
What you will learn?
📅 Day 0: Software Introduction, Setup & 1:1 Mentor Support (30 mins)
- Introduction to Google Colab, Python notebooks and the workshop’s public health analysis workflow.
- Guidance on Google account setup, notebook access, file uploads and downloading workshop materials.
- Introduction to essential Python libraries: pandas, NumPy, matplotlib, scikit-learn and ipywidgets.
- Accessing selected public datasets from WHO, Our World in Data and CDC.
- Understanding dataset documentation, reporting periods, geographic coverage and population denominators.
- Running setup cells, checking library availability and testing sample visualizations.
- Optional local installation guidance for Python and Jupyter Notebook when browser-based access is unavailable.
🛠️ Hands-on
- Open the prepared notebook and import a small public health CSV dataset.
- Run sample analysis cells, generate a chart and save the notebook.
- Verify dataset access and resolve setup issues before Day 1.
- 1:1 Mentor Support: Individual assistance with account access, software setup, prerequisites and participant queries through allocated support slots.
Hands-on Tools: Google Colab, Python, pandas, NumPy, matplotlib, scikit-learn, ipywidgets; optional local Jupyter Notebook.
📅 Day 1: Public Health Data, Digital Surveillance & Early-Warning Signals
- Foundations of AI in epidemiology, disease surveillance and population health monitoring.
- Indicator-based, syndromic and event-based surveillance: purposes and differences.
- Exploring public health datasets: cases, deaths, hospitalizations, vaccination and population indicators.
- Identifying missing values, duplicate records, reporting delays and changes in testing or reporting.
- Calculating incidence rates, moving averages and changes in disease trends.
- Introduction to multi-source surveillance using clinical reports, wastewater signals and digital indicators.
- Distinguishing an unusual data signal from a verified outbreak.
🛠️ Hands-on
- Import and check a prepared infectious-disease dataset in Google Colab.
- Calculate weekly incidence and moving averages, then visualize trends across selected locations.
- Apply a simple historical-baseline threshold to flag unusual increases and discuss possible reporting artefacts.
Hands-on Tools: Google Colab, Python, pandas, matplotlib; curated WHO, Our World in Data or CDC CSV datasets.
Practical Output: A checked dataset, disease-trend chart and preliminary surveillance-alert table.
📅 Day 2: Machine-Learning Forecasting & Explainable Disease-Trend Analysis
- Understanding forecasting horizons, seasonality and lagged disease indicators.
- Distinguishing disease-count forecasting from outbreak detection and causal explanation.
- Creating simple lag and rolling-average features from historical surveillance data.
- Comparing a persistence baseline with a prepared Random Forest regression model.
- Using chronological training and testing to prevent future-data leakage.
- Evaluating forecasts using mean absolute error and interpreting prediction limitations.
- Introducing explainable AI through feature importance and discussing changing disease patterns.
🛠️ Hands-on
- Use the Day 1 dataset to create lagged features in a prepared notebook.
- Train a small Random Forest model to forecast the next reporting period and compare it with a persistence baseline.
- Plot observed versus predicted counts, calculate forecast error and interpret selected feature contributions.
Hands-on Tools: Google Colab, Python, pandas, scikit-learn, matplotlib.
Practical Output: A baseline-versus-model comparison, forecast plot, error summary and feature-importance chart.
Chronological evaluation is essential: scikit-learn’s forecasting examples demonstrate how inappropriate random splitting can produce overly optimistic performance estimates. scikit-learn
📅 Day 3: Precision Public Health, Interactive Dashboards & Responsible AI
- Connecting surveillance indicators and forecasts with public health investigation and planning.
- Comparing population-standardized disease burden across locations.
- Designing transparent alert thresholds and understanding false alarms versus missed signals.
- Introduction to vaccination planning and healthcare resource prioritization using descriptive indicators.
- Emerging applications: wastewater-informed surveillance, One Health intelligence and AI-assisted analysis of public health reports.
- Generative AI and NLP for report summarization: source verification, hallucinations and human review.
- Responsible AI: privacy, representativeness, reporting bias and communicating uncertainty.
🛠️ Hands-on
- Build a simple interactive surveillance dashboard using the prepared Day 1–2 outputs.
- Select a location and adjust an alert threshold to observe changes in flagged reporting periods.
- Complete a short public health interpretation template covering the signal, supporting evidence, data limitations and recommended verification steps.
Hands-on Tools: Google Colab, Python, pandas, matplotlib, ipywidgets; publicly accessible WHO and CDC reports for interpretation.
Practical Output: An interactive notebook dashboard and a concise, evidence-based surveillance brief.
Mentor Profile
Fee Plan
Get an e-Certificate of Participation!

Intended For :
This program is designed for a diverse global audience, including undergraduate and postgraduate students in life sciences, public health, and biomedical sciences, as well as Ph.D. scholars, research fellows, faculty members, and academicians. It is equally relevant for epidemiologists, public health experts, clinical researchers, and healthcare professionals, along with data scientists working in healthcare and life sciences. Additionally, professionals from the biotechnology and pharmaceutical industries, as well as policy researchers and experts in health informatics, will benefit from the workshop.
Career Supporting Skills
Program Outcomes
Understand AI applications in public health systems
Analyze epidemiological data and disease trends
Interpret outbreak prediction models conceptually
Apply AI thinking to population health challenges
Evaluate public health datasets
Design basic surveillance and monitoring frameworks
