Introduction

Data analytics plays an essential role in public health by helping policymakers, healthcare providers, and researchers make data-driven decisions to improve health outcomes. This course provides a comprehensive introduction to the use of data analytics in the public health sector, with a focus on analyzing health data, identifying trends, and creating impactful interventions. Participants will learn how to apply statistical techniques and data visualization methods to public health datasets and effectively communicate their findings to support public health decision-making. By the end of the course, participants will be equipped with the skills needed to analyze and interpret health data to address public health challenges.

Objectives

By the end of this course, participants will:

• Understand the key concepts and types of data used in public health.
• Gain proficiency in statistical techniques commonly applied in public health data analysis, including regression analysis, survival analysis, and hypothesis testing.
• Learn to analyze and interpret data from various public health sources such as surveillance systems, epidemiological studies, and surveys.
• Develop skills in using data visualization tools to communicate public health insights effectively.
• Understand the ethical considerations and privacy laws related to handling health data.
• Learn how to use modern data science tools such as R, Python, and Tableau for public health data analysis and visualization.

Who Should Attend?

This course is ideal for:

• Public health professionals involved in data analysis, research, and policy development.
• Epidemiologists, biostatisticians, and healthcare data analysts looking to expand their skills in public health data analytics.
• Government health officials and policymakers who want to use data to inform public health initiatives.
• Students and professionals interested in using data science to address public health issues and improve community health outcomes.

Day 1: Introduction to Public Health Data Analytics

Morning Session: Overview of Public Health and Data Analytics

• Introduction to public health: Key concepts, public health systems, and challenges.
• The role of data analytics in public health: Surveillance, epidemiology, and policy-making.
• Types of health data: Disease surveillance data, population health data, environmental data, and healthcare system data.
• Key public health metrics: Mortality rates, incidence, prevalence, and risk factors.
• Ethical considerations in public health data: Patient privacy, informed consent, and data protection laws (HIPAA, GDPR).

Afternoon Session: Public Health Data Sources and Types

• Understanding public health data sources: Vital statistics, health surveys (e.g., NHANES, BRFSS), disease registries, and electronic health records (EHR).
• Public health surveillance systems: How diseases are tracked and monitored.
• Data quality and limitations: Missing data, biases, and data collection challenges.
• Introduction to data cleaning and preprocessing in public health.
• Hands-on: Exploring a real-world public health dataset and performing initial data exploration.

Day 2: Statistical Methods in Public Health Data Analysis

Morning Session: Descriptive Statistics and Data Exploration

• Descriptive statistics for public health data: Measures of central tendency, dispersion, and distribution.
• Exploring public health data: Data visualization techniques such as histograms, box plots, and scatter plots.
• Identifying patterns and trends in public health data: Age, gender, socioeconomic factors, and disease prevalence.
• Using R/Python for public health data analysis: Basic data manipulation and visualization techniques.
• Hands-on: Analyzing and visualizing a public health dataset to explore trends and patterns.

Afternoon Session: Hypothesis Testing and Statistical Inference

• Understanding hypothesis testing: Null and alternative hypotheses, p-values, and confidence intervals.
• Common tests in public health: t-tests, chi-square tests, and ANOVA for comparing groups.
• Regression analysis in public health: Linear regression for continuous outcomes, logistic regression for binary outcomes.
• Risk factors and health outcomes: Analyzing how lifestyle, genetics, and environmental factors contribute to health outcomes.
• Hands-on: Performing hypothesis tests and regression analysis on public health data.

Day 3: Advanced Statistical Techniques in Public Health

Morning Session: Survival Analysis and Time-to-Event Data

• Introduction to survival analysis: Time-to-event analysis and its applications in public health.
• Key concepts: Kaplan-Meier estimator, Cox proportional hazards model, and log-rank tests.
• Analyzing time-to-event data in public health: Disease progression, patient survival, and health interventions.
• Hands-on: Using survival analysis techniques to analyze clinical trial or epidemiological data.

Afternoon Session: Multivariate Analysis and Epidemiological Models

• Multivariate analysis techniques: Understanding how to analyze multiple variables and their relationships in public health data.
• Epidemiological models: SIR (Susceptible-Infected-Recovered) models, disease transmission models, and modeling outbreaks.
• Predictive modeling for public health: Using machine learning algorithms for predictive analysis (e.g., disease prediction, epidemic forecasting).
• Hands-on: Building a predictive model for disease spread or health outcomes using public health data.

Day 4: Data Visualization for Public Health

Morning Session: Principles of Public Health Data Visualization

• The importance of data visualization in public health: Communicating complex health data to stakeholders, policymakers, and the public.
• Types of public health visualizations: Bar charts, line graphs, heatmaps, choropleth maps, and time-series plots.
• Tools for data visualization: R (ggplot2, plotly), Python (matplotlib, seaborn), and Tableau.
• Best practices for creating clear and effective public health visualizations.
• Hands-on: Creating basic public health visualizations with R/Python.

Afternoon Session: Advanced Visualization Techniques

• Interactive data visualizations for public health: Using dashboards and interactive charts to explore health data.
• Geographic Information Systems (GIS) in public health: Mapping disease outbreaks, environmental health data, and demographic health disparities.
• Visualizing epidemiological data: Heatmaps, disease burden maps, and risk factor analysis.
• Hands-on: Building an interactive public health dashboard using R (Shiny) or Python (Dash).

Day 5: Public Health Data Analytics in Practice

Morning Session: Case Studies in Public Health Data Analysis

• Analyzing real-world public health problems: Case studies in infectious diseases, chronic diseases, and environmental health.
• Understanding the role of data analytics in decision-making: How data supports health interventions, policy changes, and healthcare resource allocation.
• Using public health data for disease prevention and control: Identifying risk factors, targeting interventions, and evaluating effectiveness.
• Hands-on: Working in groups to analyze a public health issue and propose data-driven solutions.

Afternoon Session: Final Project Presentation and Course Wrap-Up

• Final project: Participants work on a public health dataset to analyze trends, apply statistical methods, and create visualizations.
• Group presentations: Presenting findings, insights, and recommendations based on the data analysis.
• Feedback and discussion: Peer review and instructor feedback on the analysis and visualizations.
• Final Q&A session and course wrap-up.
• Certification of completion for participants who successfully complete the course and final project.

Materials and Tools:

• Software and tools: R (ggplot2, dplyr, Shiny), Python (pandas, matplotlib, seaborn, scikit-learn), Tableau.
• Recommended readings: “Public Health Data Science” by A. P. Khanna and “Data Science for Public Health” by B. L. Swaminathan.
• Real-world case studies: Disease surveillance data, national health surveys, epidemiological studies, and health interventions.

Conclusion and Final Assessment

• Recap of key concepts: Types of health data, statistical techniques, and data visualization methods in public health.
• Final assessment: Evaluation of participants’ final projects and presentations.
• Certification of completion for those who successfully complete the course and final project.