Python for Biostatistics: Analyzing Infectious Diseases Data

📚 Python for Biostatistics: Analyzing Infectious Diseases Data

🚀 Course Headline: Forecast infectious disease rates, build epidemiological modelling, and map the spread of infectious diseases with heatmaps!

Course Description

Key Learning Areas:

1️⃣ Data Analysis

• Explore infectious disease datasets from various angles.
• Understand the nuances of biostatistics data and the challenges it presents.

2️⃫ Time Series Forecasting

• Learn the intricacies of the Seasonal Trend Decomposition (STL) model.
• Predict the spread of infectious diseases into the future.

3️⃣ Public Health Policy

• Craft data-driven public health policies informed by epidemiological modeling.

Introduction to Biostatistics Fundamentals:

In our introductory sessions, you'll get up to speed on:

• The core concepts of biostatistics, including common challenges and statistical models.
• The Kermack-McKendrick equation for calculating infectious disease transmission.
• Factors that can influence the spread of infectious diseases – population density, healthcare accessibility, and antigenic variation.

Hands-On Project:

As you've grasped the theoretical foundations, we'll move on to a hands-on project where you'll:

1. Set Up Your Workspace: Learn how to configure the Google Colab IDE for your analysis.
2. Data Acquisition: Discover how to source and download relevant datasets from platforms like Kaggle.
3. Project Execution: Engage in three core components of the project:
   • Exploratory Data Analysis (EDA): Dive into your dataset, identify patterns, and draw insights.
   • Forecasting Model Building: Develop a model to predict future disease spread using time series techniques.
   • Epidemiological Modeling & Public Health Policy Making: Apply the SIR model to understand disease dynamics and inform policy decisions.

Why This Course? 📈

• Career Advancement: If you're looking to break into or advance within the public health/healthcare industry, this course is invaluable.
• Versatile Skills: Acquire skills applicable beyond biostatistics, such as time series decomposition for various markets.
• Public Health Policy Expertise: Become adept at making informed decisions and considering external factors in public health policy creation.

What You'll Learn:

• Biostatistics Fundamentals: Gain a solid understanding of biostatistics and its application in infectious disease analysis.
• Infectious Disease Transmission Rate: Learn to calculate this critical metric using the SIR model.
• External Factors Analysis: Investigate how population density, herd immunity, and antigenic variation affect disease spread.
• Data Acquisition & Preparation: Find, download, and prepare datasets for analysis from platforms like Kaggle.
• Data Cleaning Techniques: Remove missing data, duplicates, and identify outliers using Z score.
• Demographic Analysis: Analyze patient demographics to inform interventions.
• Spatial Visualization: Map disease prevalence by county with heatmaps.
• Trend Analysis: Identify yearly trends in infectious diseases.
• Confidence Interval Analysis: Apply statistical methods to provide a range of possible values for your findings.
• Forecasting: Use time series decomposition models to forecast future disease rates.
• Epidemiological Modeling: Apply the SIR model to understand disease dynamics and inform interventions.
• Policy Evaluation: Assess the effectiveness of public health policies through data-driven analysis.

Embark on a journey to become proficient in biostatistical data analysis and make a tangible impact in public health with Python. Enroll in the Python for Biostatistics: Analyzing Infectious Diseases Data course today! 🎓