Module 1: Introduction to Epidemiological Data Science

·       Review of core epidemiological concepts (incidence, prevalence, , transmission dynamics)

·       Introduction to the Python Data Science Stack (Pandas, NumPy, Matplotlib)

·       Setting up the computational environment for public health analysis (Anaconda, virtual environments)

·       Overview of common communicable disease data sources (case reports, sentinel surveillance, syndromic data)

·       Practical session: Initializing the Python environment and loading a time series of historical influenza incidence data.

Module 2: Python Fundamentals for Public Health Data

·       Deep dive into Pandas: DataFrames, indexing, and merging multiple datasets

·       Handling date and time data: Parsing, formatting, and time zone management

·       Data aggregation and resampling techniques for standardizing reporting intervals (e.g., weekly, monthly)

·       Implementing conditional logic and list comprehensions for efficient data manipulation

·       Practical session: Cleaning and merging historical case count data with corresponding demographic data using Pandas.

Module 3: Epidemiological Data Acquisition and Cleaning

·       Identifying and addressing common data quality issues in surveillance systems (reporting lags, underreporting)

·       Techniques for handling missing values in health datasets (Imputation strategies like KNN or mean)

·       Outlier detection and smoothing methods for noisy incidence curves (e.g., rolling averages)

·       Converting raw case data into usable format for modeling (e.g., weekly incidence rate calculation)

·       Practical session: Developing a full data cleaning pipeline function to process raw weekly COVID-19 case counts, including outlier removal and imputation of missing weeks.

Module 4: Visualization and Exploratory Data Analysis (EDA)

·       Creating effective time series plots to visualize incidence trends and seasonality

·       Generating distribution plots and heatmaps to explore disease spread across age groups and regions

·       Using interactive visualization libraries (e.g., Plotly) for dynamic dashboard development

·       Analyzing correlations between health outcomes and potential driving factors (e.g., temperature, population density)

·       Practical session: Producing a comprehensive EDA dashboard using Matplotlib and Seaborn, visualizing the seasonality and growth rate of a sample disease.

Module 5: Classic Compartmental Modeling (SIR/SEIR)

·       Theoretical foundations and mathematical formulation of the SIR model

·       Expanding to the SEIR model to incorporate the latent (exposed) period

·       Numerical simulation of models using Python solvers (SciPy)

·       Parameter estimation and fitting the model to observed incidence data

·       Practical session: Implementing and simulating both SIR and SEIR models in Python, adjusting the  value to observe its impact on the epidemic curve.

Module 6: Time Series Forecasting for Incidence Rates

·       Introduction to traditional time series methods: Moving Average, Exponential Smoothing (ETS)

·       Autoregressive Integrated Moving Average (ARIMA) models: Identification, estimation, and diagnostics

·       Implementing external factors (e.g., vaccination rates, mobility) via ARIMAX models

·       Using the Prophet library for automated forecasting and handling multiple seasonality

·       Practical session: Building and evaluating a SARIMA model to forecast the weekly incidence rate of a cyclical respiratory illness.

Module 7: Machine Learning for Outbreak Prediction

·       Feature engineering for epidemiological ML: Lagged values, population mobility, and climate variables

·       Implementing supervised regression models (Random Forest, Gradient Boosting) for next-period incidence forecasting

·       Developing classification models (e.g., Logistic Regression, SVM) for binary outbreak/no-outbreak prediction

·       Techniques for addressing class imbalance in outbreak prediction datasets

·       Practical session: Training a Gradient Boosting Regressor to predict the one-week-ahead case count, utilizing lagged incidence and temperature as predictors.

Module 8: Geospatial Epidemiology and Spatial Analysis

·       Introduction to GeoPandas and handling spatial data formats (shapefiles, GeoJSON)

·       Visualizing disease prevalence and incidence on regional maps using Folium or Plotly

·       Conducting spatial autocorrelation analysis (Moran's I) to identify disease clustering

·       Implementing spatial smoothing techniques to adjust for small area population bias

·       Practical session: Mapping the historical spread of a disease across different administrative regions and identifying statistically significant hot spots.

Module 9: Model Validation, Uncertainty, and Interpretation

·       Standard model evaluation metrics: Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and coverage of prediction intervals

·       Cross-validation techniques suitable for time series data (e.g., rolling and blocked cross-validation)

·       Quantifying and visualizing forecast uncertainty using prediction intervals and Monte Carlo simulation

·       Interpreting feature importance from complex machine learning models (e.g., SHAP values)

·       Practical session: Comparing the RMSE and prediction interval coverage of three different forecasting models (e.g., SEIR, ARIMA, Random Forest).

Module 10: Communicating Forecasts and Policy Integration

·       Principles of data storytelling for public health policy and operational teams

·       Designing effective, non-technical visualizations for decision-makers

·       Creating reproducible analytical reports and interactive web applications (using Streamlit or Dash)

·       Translating forecasts into clear, actionable policy recommendations and intervention triggers

·       Practical session: Building a simple web application in Python to display the latest disease forecast and key policy implications.

Requirements:

• Participants should be reasonably proficient in English.
• Applicants must live up to Armstrong Global Institute admission criteria.

Terms and Conditions

1. Discounts: Organizations sponsoring Four Participants will have the 5th attend Free

2. What is catered for by the Course Fees: Fees cater for all requirements for the training – Learning materials, Lunches, Teas, Snacks and Certification. All participants will additionally cater for their travel and accommodation expenses, visa application, insurance, and other personal expenses.

3. Certificate Awarded: Participants are awarded Certificates of Participation at the end of the training.

4. The program content shown here is for guidance purposes only. Our continuous course improvement process may lead to changes in topics and course structure.

5. Approval of Course: Our Programs are NITA Approved. Participating organizations can therefore claim reimbursement on fees paid in accordance with NITA Rules.

Booking for Training

Simply send an email to the Training Officer on training@armstrongglobalinstitute.com and we will send you a registration form. We advise you to book early to avoid missing a seat to this training.

Or call us on +254720272325 / +254725012095 / +254724452588

Payment Options

We provide 3 payment options, choose one for your convenience, and kindly make payments at least 5 days before the Training start date to reserve your seat:

1. Groups of 5 People and Above – Cheque Payments to: Armstrong Global Training & Development Center Limited should be paid in advance, 5 days to the training.

2. Invoice: We can send a bill directly to you or your company.

3. Deposit directly into Bank Account (Account details provided upon request)

Cancellation Policy

1. Payment for all courses includes a registration fee, which is non-refundable, and equals 15% of the total sum of the course fee.

2. Participants may cancel attendance 14 days or more prior to the training commencement date.

3. No refunds will be made 14 days or less before the training commencement date. However, participants who are unable to attend may opt to attend a similar training course at a later date or send a substitute participant provided the participation criteria have been met.

Tailor Made Courses

This training course can also be customized for your institution upon request for a minimum of 5 participants. You can have it conducted at our Training Centre or at a convenient location. For further inquiries, please contact us on Tel: +254720272325 / +254725012095 / +254724452588 or Email training@armstrongglobalinstitute.com

Accommodation and Airport Transfer

Accommodation and Airport Transfer is arranged upon request and at extra cost. For reservations contact the Training Officer on Email: training@armstrongglobalinstitute.com or on Tel: +254720272325 / +254725012095 / +254724452588