AI-Powered Public Health Analytics & Predictive Modelling Training Workshop

Course overview

The AI-Powered Public Health Analytics & Predictive Modelling Training Workshop equips public health professionals, epidemiologists, researchers and healthcare programme managers with practical skills in applying artificial intelligence, machine learning and advanced data analytics to public health challenges. The training covers health data preparation, epidemiological statistics, disease surveillance, predictive modelling, risk stratification, time-series forecasting and AI-assisted reporting. Participants learn to use tools such as Python, pandas, scikit-learn, SQL and Power BI to analyse health trends, predict healthcare service demand, identify population-level risk patterns and develop data-driven insights. The course also addresses model validation, algorithmic bias, data privacy, explainable AI and ethical considerations to support responsible, evidence-based public health planning, disease prevention and resource allocation.

The AI-Powered Public Health Analytics & Predictive Modelling Training Workshop provides practical training in artificial intelligence, machine learning and predictive analytics for public health applications. Participants learn to prepare health datasets, analyse epidemiological trends, develop disease forecasting models, assess population health risks and predict healthcare service demand using Python and modern data science tools. The course covers classification, regression, clustering, time-series forecasting, model evaluation, explainable AI and AI-assisted reporting. Designed for public health professionals, epidemiologists, researchers, health data analysts and healthcare programme managers, the training emphasises responsible AI, data privacy and evidence-based decision-making to strengthen disease surveillance, public health planning and resource allocation.

Course objectives

• Prepare, clean and analyse public health datasets for statistical analysis and machine learning.
• Apply AI-assisted analytics and visualisation to produce evidence-based reports, dashboards and recommendations while protecting sensitive health information.
• Interpret model performance, uncertainty, bias and limitations when communicating predictions to public health decision-makers.
• Use Python and relevant analytics tools to implement classification, regression, clustering and time-series forecasting techniques.
• Develop and evaluate predictive models for disease surveillance, health service utilisation and population risk analysis.
• Apply epidemiological statistics and exploratory data analysis to identify population health trends and disease patterns.

Who should attend

Course content

• Module 1: Introduction to AI and Predictive Analytics in Public Health Applications of AI in public health, differences between descriptive and predictive analytics, machine learning fundamentals, public health use cases for AI, opportunities and limitati
• Module 2: Public Health Data Sources and Analytical Frameworks Health information management systems, demographic and census data, disease surveillance datasets, laboratory and clinical data, health facility utilisation records, demographic and health sur
• Module 3: Data Collection, Cleaning and Preprocessing Importing CSV and Excel datasets,HMS,KOBO, handling missing values, identifying duplicates and inconsistent records, correcting data types, detecting outliers, encoding categorical variables, scaling
• Module 4: Statistical Analysis and Epidemiological Foundations Descriptive statistics, distributions and variability, incidence and prevalence, morbidity and mortality indicators, relative risk and odds ratios, correlation and association, confidence inte
• Module 5: Exploratory Data Analysis and Feature Engineering Exploring disease patterns, examining relationships between health indicators, cohort and demographic segmentation, temporal trend analysis, constructing analytical features, developing populatio
• Module 6: Python for Public Health Machine Learning Python environment setup, Jupyter Notebook workflows, pandas and NumPy for data manipulation, data visualisation with Matplotlib and Seaborn, scikit-learn fundamentals, reusable preprocessing pipelines,
• Module 7: Supervised Learning for Public Health Prediction Introduction to supervised learning, logistic regression, decision trees, random forests, gradient boosting, classification and regression problems, training and test datasets, cross-validation, f
• Module 8: Disease Surveillance and Outbreak Prediction Routine disease surveillance data, case trend analysis, early warning indicators, detecting unusual increases in reported cases, outbreak signal detection, temporal and geographic clustering, predicti
• Module 8: Disease Surveillance and Outbreak Prediction Routine disease surveillance data, case trend analysis, early warning indicators, detecting unusual increases in reported cases, outbreak signal detection, temporal and geographic clustering, predicti
• Module 9: Time-Series Forecasting for Public Health Time-series data structures, trend and seasonal patterns, stationarity, moving averages, exponential smoothing, ARIMA and SARIMA models, forecasting disease incidence, projecting health service utilisati
• Module 10: Population Health Risk Stratification Defining population-level risk, selecting appropriate predictors, risk scoring methods, logistic regression and tree-based models, clustering populations by health indicators, identifying underserved or hig
• Module 11: Advanced Machine Learning and Pattern Recognition Unsupervised learning, k-means clustering, hierarchical clustering, dimensionality reduction, anomaly detection, feature importance, hyperparameter tuning, ensemble methods, comparing alternativ
• Module 12: Model Evaluation, Validation and Explainable AI Confusion matrices, sensitivity and specificity, precision and recall, F1-score, ROC-AUC, calibration, regression error metrics, temporal and external validation, overfitting and underfitting, cla
• Module 13: AI for Health Resource Planning and Service Demand Forecasting Forecasting patient visits and service demand, estimating medicine and commodity requirements, analysing staffing and facility capacity, predicting demand for vaccination and screen
• Module 14: Geospatial Analytics and Environmental Health Prediction Introduction to GIS and spatial health data, mapping disease distribution, spatial joins and aggregation, geographic risk indicators, environmental and climate-related health factors, hot
• Module 15: Generative AI and AI-Assisted Public Health Reporting Applications and limitations of generative AI, summarising analytical findings, drafting public health reports, generating code explanations, assisting with data documentation, developing ev
• Module 16: Public Health Data Governance, Privacy and Responsible AI Health data confidentiality, consent and lawful data use, de-identification and pseudonymisation, access controls, secure data handling, algorithmic fairness, bias in health datasets, re
• Module 17: Predictive Analytics Dashboards and Decision Support Designing public health indicators, presenting observed and predicted trends, developing interactive dashboards with Power BI, connecting analytical outputs to reporting workflows, displaying
• Module 18: Capstone Project and Practical Implementation Defining a public health prediction problem, selecting an appropriate dataset, establishing a baseline model, preparing and exploring the data, building and validating a predictive model, interpreti

General notes

• Public Health Predictive Analytics, Machine Learning for Public Health, AI-Powered Healthcare Analytics, Epidemiological Modelling Training, Disease Forecasting Training, Public Health Data Science, Python for Public Health, AI in Disease Surveillance, Population Health Analytics, Healthcare Predictive Modelling.
• The AI-Powered Public Health Analytics & Predictive Modelling Training Workshop provides practical training in artificial intelligence, machine learning and predictive analytics for public health applications. Participants learn to prepare health datasets, analyse epidemiological trends, develop disease forecasting models, assess population health risks and predict healthcare service demand using Python and modern data science tools. The course covers classification, regression, clustering, time-series forecasting, model evaluation, explainable AI and AI-assisted reporting. Designed for public health professionals, epidemiologists, researchers, health data analysts and healthcare programme managers, the training emphasises responsible AI, data privacy and evidence-based decision-making to strengthen disease surveillance, public health planning and resource allocation.

Frequently asked questions

Schedule & fees