Artificial Intelligence in Epidemiology and Public Health
Artificial Intelligence (AI) and Machine Learning (ML) are transforming public health by enabling outbreak prediction, disease monitoring, and optimization of intervention policies. This 100% online course teaches healthcare professionals, students, and researchers how to develop predictive models, interactive dashboards, and AI applications to support public health decision-making, using real open-source datasets focused on Africa.
Course Content
Module 1 – Introduction and Context
- Major public health challenges in Africa: malaria, HIV, tuberculosis, and cardiovascular diseases
- Why AI is revolutionary in epidemiology and public health
- Success stories of AI in disease surveillance and health policy
- Basic concepts: incidence, prevalence, disease clusters, risk factor
Module 2 – Python and Data Science Fundamentals
- Python for epidemiology: variables, functions, and control structures
- Data manipulation with Pandas and NumPy
- Data visualization: Matplotlib, Seaborn, interactive dashboards
- ML fundamentals: regression, Random Forest, XGBoost, evaluation metrics
Module 3 – Practical Projects with Real Data
Project 1. Malaria-Outbreak Prediction
- Dataset: Malaria Indicator Survey
- Objective: Predict malaria incidence peaks in specific regions
- Techniques: Random Forest, XGBoost, time series regression
- Final product: Development of an AI app for outbreak monitoring in Python
Project 2. HIV – Population Risk Analysis
- Dataset: UNICEF HIV/AIDS Data (public data by country, age, and gender)
- Objective: Identify high-risk groups and associated infection factors
- Techniques: Supervised classification, clustering, heat maps
- Final product: Creation of an interactive dashboard for prevention strategy support
Project 3. Tuberculosis Monitoring and Prediction of hotspost
- Dataset: WHO TB Data (incidence, prevalence, and mortality by country)
- Objective: Identify hotspots and predict high-risk transmission areas
- Techniques: Geospatial modeling, time series analysis
- Final product: Early warning system and intervention planning
Project 4. Air Quality and Environmental Health Monitoring
- Dataset: World Air Quality Index (AQI) + simplified health data (hospital admissions, respiratory disease incidence, or government open health data)
- Objective: Identify pollution-related health hotspots and demonstrate environment-disease correlations for early detection
- Techniques: Geospatial modeling, risk analysis, hotspot mapping, and exploratory data analysis (EDA)
- Final Product: Dashboard or visual report for early warning and public health planning
Module 4 – Tools and Applications for Public Health
- Building ML pipelines for epidemiology
- Creating interactive dashboards and simple apps in python
- Practical applications in disease surveillance and health policy decision support
Module 5 – Application and Communication
- Presenting results to health managers, NGOs, and local authorities
- Visualization of patterns, risk maps, and predictions
- Data-driven intervention strategies
Module 6 – International Market Preparation (Premium)
- Creating competitive CVs and cover letters for international AI health positions
- Professional profile optimization (LinkedIn, GitHub)
- Preparation for technical and behavioral interviews
- Strategies to find opportunities in the UK, Europe, Canada, and Australia
- Strategic support in submitting up to 5 job applications
- Recommendation letter signed by Dr. Alexandre Cobre
Note: Module 6 provides advisory support only. It does not guarantee employment or visa approval.
Instructor
The course is led by Prof. Dr. Alexandre de Fátima Cobre, a Mozambican scientist affiliated with The University of Manchester (UK), specialist in AI applied to drug discovery, collaborator of the WHO, and founder of MozBioMed.AI.
- Ranked among the Top 3 Pharmaceutical Scientists in Brazil (2025)
- Author of over 50 international publications
- Recognized for pioneering AI-based discoveries in COVID-19
Basic Plan – £175 (15,000 MZN)
- Complete course + practical projects + international certificate
- Discounted price (one-time payment): £165 (14,000 MZN)
- Installments: 3x £60 (total £180 / 15,000 MZN)
Premium Plan – £580 (50,000 MZN)
- Everything in the Basic Plan + full Module 6
- Strategic support for the international job market: CV, GitHub, recommendation letter, guidance on job applications and visa
- Discounted price (one-time payment): £550 (47,000 MZN)
- Installments: 5x £120 (total £600 / 50,000 MZN)
Course Highlights
- 100% online with pre-recorded lessons
- Practical projects with real open-source epidemiological data
- Development of AI applications for use in epidemiology and public health
- Completion possible in 3–6 weeks for dedicated students
- International certificate of completion
- Preparation for the global AI health market
