Module 1: Foundations of Outbreak Data and R Environment Setup

• Core concepts of disease surveillance and outbreak investigation data types
• Installation and configuration of R, RStudio, and essential packages (e.g., tidyverse)
• Introduction to the RStudio interface and basic R commands
• Understanding data structures in R: vectors, data frames, and lists
• Principles of reproducible analysis using R scripts and RMarkdown

Practical session: Installing the required software and packages and executing the first basic R script to load a sample outbreak dataset.

Module 2: Data Import, Cleaning, and Tidy Data Principles in R

• Importing epidemiological data from various sources (CSV, Excel, databases)
• Principles of Tidy Data using the tidyr package (wide vs. long format)
• Data manipulation using the dplyr package (filter, select, mutate, summarize)
• Handling missing values, inconsistencies, and errors in case data
• Converting and validating date/time formats for time-series analysis

Practical session: Cleaning and standardizing a raw line list data file, ensuring correct data types and handling missing values using dplyr functions.

Module 3: Descriptive Epidemiology and Measures in R

• Calculating measures of frequency: counts, proportions, and ratios
• Computing key health indicators: Incidence, prevalence, and crude mortality rates
• Stratifying measures by demographic and risk factors (age, sex, location)
• Using the janitor and gtsummary packages for descriptive tables
• Introduction to risk measures: Attack Rates and Relative Risk calculation

Practical session: Calculating age-specific attack rates and generating a demographic summary table of cases using the tidyverse suite.

Module 4: Advanced Visualization for Outbreaks using ggplot2

• Grammar of Graphics: understanding layers, aesthetics, and geometries
• Creating effective bar charts, histograms, and box plots for demographic variables
• Customizing plots with scales, labels, and themes for publication quality
• Visualizing case distribution: scatter plots and time trends
• Generating multi-panel and faceted plots for comparative analysis

Practical session: Creating a custom, publication-ready histogram of case ages faceted by outcome (e.g., recovered, deceased) using ggplot2.

Module 5: Introduction to Spatial Epidemiology and Mapping in R

• Understanding basic geographical data structures (e.g., shapefiles, GeoJSON)
• Importing and visualizing spatial data using the sf package
• Creating interactive, web-based maps using the leaflet package
• Mapping case distribution and calculating spatial clustering (basic concepts)
• Overlaying case locations onto administrative boundaries for reporting

Practical session: Generating an interactive choropleth map in R/leaflet showing cumulative case counts by administrative region.

Module 6: Time Series Analysis and Epidemic Curves using R

• Calculating daily or weekly case counts from date fields
• Principles of the Epidemic Curve (epi-curve) and its interpretation
• Using the EpiCurve package or ggplot2 to visualize case onset over time
• Identifying the point source, propagating, and continuous sources from the curve shape
• Smoothing and aggregation techniques to highlight underlying trends

Practical session: Generating and customizing a fully labeled Epidemic Curve using a mock line list, including color coding for different case statuses.

Module 7: Advanced Epidemiological Modeling with R

• Introduction to deterministic compartmental models (SIR Model: Susceptible-Infected-Recovered)
• Estimating key outbreak parameters: Reproductive Number ($R_0$) and doubling time
• Running basic simulations of disease spread using EpiModel or similar packages
• Interpreting model outputs and understanding the limitations of forecasting
• Using models to evaluate potential intervention strategies (e.g., vaccination)

Practical session: Running a basic SIR Model simulation in R and visualizing the time course of the infected population.

Module 8: Introduction to Power BI for Data Connection

• Overview of the Power BI interface, components, and purpose
• Connecting to data sources: flat files, web sources, and databases
• Understanding data lineage and the refresh process
• Introduction to Power BI Desktop vs. Power BI Service
• Importing cleaned R data (CSV/ODBC) for visualization

Practical session: Connecting Power BI to a standardized, clean epidemiological dataset (CSV format) and verifying data fields.

Module 9: Data Transformation and M-Query in Power BI

• Utilizing the Power Query Editor for data transformation
• Mastering essential transformations: unpivoting, merging, and appending queries
• Creating calculated columns and custom functions using M-Query language
• Dealing with inconsistencies and cleaning data within Power BI's environment
• Implementing privacy levels and ensuring data security during transformation

Practical session: Performing three complex data cleaning and shaping steps (e.g., unpivoting demographic columns) within the Power Query Editor.

Module 10: Data Modeling and Relationships in Power BI

• Principles of dimensional modeling (Fact and Dimension tables)
• Creating and managing relationships between tables (one-to-many, many-to-many)
• Setting up date and time dimension tables for temporal analysis
• Optimizing the data model for performance and scalability
• Understanding filtering context and cross-filtering behavior

Practical session: Designing a Star Schema data model in Power BI, establishing relationships between a fact table (Cases) and dimension tables (Time, Location).

Module 11: DAX Fundamentals for Public Health Metrics

• Introduction to DAX (Data Analysis Expressions) syntax and concepts
• Creating calculated measures: totals, averages, and ratios
• Understanding Row Context vs. Filter Context and the CALCULATE function
• Calculating rolling averages and running totals for trend analysis
• Implementing complex public health metrics (e.g., Case Fatality Rate, Incidence Density)

Practical session: Writing three key DAX measures: cumulative cases, a 7-day rolling average of new cases, and a stratified Case Fatality Rate.

Module 12: Creating Interactive Outbreak Dashboards in Power BI

• Principles of effective dashboard design and data storytelling
• Utilizing standard Power BI visualizations (Cards, Tables, Slicers)
• Implementing conditional formatting and custom tooltips
• Designing for user interaction: drill-through and bookmarking features
• Ensuring responsiveness and optimal viewing on mobile devices

Practical session: Building the initial layout of the outbreak dashboard, including key performance indicators (KPIs) and interactive slicers.

Module 13: Geospatial Mapping and Visualization in Power BI

• Using Power BI's built-in map visuals (ArcGIS, Bing Maps)
• Geocoding data and resolving location hierarchies
• Customizing map layers, bubble sizes, and color saturation
• Integrating custom geospatial data (e.g., facility locations)
• Utilizing drill-down features for granular location analysis

Practical session: Creating a bubble map in Power BI showing case counts by city/district, with bubble size proportional to the number of cases.

Module 14: Integrating R Visuals into Power BI

• Enabling and configuring the R script engine within Power BI
• Writing R scripts to generate custom visuals (e.g., advanced statistical plots)
• Passing data seamlessly from the Power BI data model to the R script
• Troubleshooting common errors when integrating R code
• Displaying R-generated visualizations (e.g., complex epidemiological plots) in the dashboard

Practical session: Generating a custom R-based box plot comparing outbreak durations across different transmission settings and embedding it into the Power BI report.

Module 15: Forecasting and Predictive Analytics in R

• Introduction to time series forecasting models (ARIMA, Exponential Smoothing)
• Using the R forecast package to predict future case counts
• Integrating forecasting results into the Power BI data model
• Visualizing uncertainty and confidence intervals around predictions
• Communicating forecast accuracy and limitations to non-technical users

Practical session: Running a basic time series forecast in R on the new case data and importing the projected values into Power BI for dashboard display.

Module 16: Ethics, Data Security, and Confidentiality

• Principles of data anonymization and pseudonymization in public health
• Handling personally identifiable information (PII) and sensitive health data
• Best practices for data governance and access control in Power BI Service
• Understanding data residency and regulatory requirements (e.g., GDPR, HIPAA principles)
• Ethical considerations when visualizing sensitive demographic data

Practical session: Developing an anonymization plan for a sample line list and applying data masking techniques in Power Query.

Module 17: Automating Refresh and Dashboard Publication

• Understanding the Power BI Service workspace and licensing models
• Setting up a data gateway for scheduled data refresh from local sources
• Configuring incremental refresh for large epidemiological datasets
• Sharing and distributing dashboards using apps and security roles
• Monitoring dashboard usage and performance metrics

Practical session: Publishing the completed dashboard to the Power BI Service and configuring a mock scheduled refresh for the data source.

Module 18: Final Capstone Project: End-to-End Analytics

• Defining the final project scope and key deliverables
• Participants execute the full data analysis pipeline (R cleaning to Power BI visualization)
• Peer review and feedback session on dashboard design and analytical accuracy
• Presenting the outbreak findings and policy recommendations based on the dashboard
• Strategies for continued learning and accessing community support (R and Power BI)

Practical session: Presenting the final, comprehensive Power BI outbreak dashboard, outlining the R code used for analysis, and defending the key insights to the class.

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