Influenza

The interactive data visualization presents the global distribution of influenza cases from 2009 to 2024. The visualization is designed to provide insights into the seasonal patterns of influenza globally, aiding in better understanding the disease.

Data Sources

The aim of this project is to develop an interactive data visualization that presents the distribution and intensity of influenza cases across six distinct countries: Germany, Iceland, UAE, Iran, North Korea, and Japan. This visualization will explore the impact of regional temperatures and geographical differences on the spread and severity of influenza.

Goals

• Data Aggregation: Collect and compile comprehensive data on influenza cases from the specified countries.
• Regional Analysis: Examine how geographical and climatic differences alter the spread and severity of influenza.
• Comparative Study: Highlight the contrasts and similarities in influenza impact among countries with diverse climates and locations.
• Interactive Elements: Enable users to interact with the data to explore trends and patterns dynamically.

Data

The dataset used for this project is extensive, encompassing a vast array of influenza-related data collected from multiple reliable sources, including FluNet by the World Health Organization (WHO), the Global Influenza Surveillance and Response System (GISRS), and Our World in Data. This comprehensive dataset includes numerous variables and spans several years (2009–2024), covering various countries and regions globally.

Given the large size and complexity of the dataset, it was essential to focus on the most critical data points to ensure the visualization remains clear and informative. After careful consideration, the following six key data points were selected for their relevance and ability to provide a comprehensive overview of influenza cases and their impact:

• Reported Cases of Acute Respiratory Infections: This data point captures the number of acute respiratory infections reported, providing insight into the prevalence of respiratory illnesses that may be associated with influenza.
• Reported Cases of Severe Acute Respiratory Infections: This metric highlights the severity of respiratory infections, indicating the number of severe cases that require more intensive medical attention.
• Reported Cases of Influenza-like Illnesses: This data point includes cases that exhibit symptoms similar to influenza, helping to understand the broader impact of influenza-like illnesses.
• Reported Deaths Caused by Severe Acute Respiratory Infections: This critical data point tracks the mortality rate associated with severe respiratory infections, offering a measure of the lethality of influenza outbreaks.
• Reported Cases of Influenza-like Illness per Thousand Outpatients: This normalized metric allows for comparison across different regions and populations by showing the rate of influenza-like illnesses per thousand outpatients.
• Reported Cases of Severe Acute Respiratory Illness per Thousand Outpatients: Similar to the previous metric, this data point provides a normalized view of severe respiratory illnesses per thousand outpatients, facilitating comparative analysis.

For readability purposes, these data points were relabeled in the data visualization to ensure clarity and ease of understanding for the users.

Scope

• Countries Covered: Germany, Iceland, UAE, Iran, North Korea, and Japan.
• Data Points: Number of influenza cases, casualties, and impacted individuals.
• Variables: Regional temperatures and climatic conditions.

Technical Implementation

Languages

• JavaScript (.js)

Libraries

• gmynd.js
• jQuery 3.7.1

Dataset Size

• 49,890 rows (adjusted): Cleaned data specifically for the selected countries (Germany, Iceland, UAE, Iran, North Korea, and Japan).
• 96,875 rows (all countries): Data encompassing all countries included in the original dataset.

Lines of Code

• 754 lines: Meticulously written to handle data processing, visualization, and user interaction.

Code Structure and Functionality

• Data Preparation:
  • Functions like prepareData() are used to process and aggregate data, ensuring it is ready for visualization.
• Visualization:
  • Functions such as drawBarChart(maxData) are implemented to create dynamic and interactive visual elements.
• User Interaction:
  • jQuery is utilized to enhance user interaction, allowing users to filter and explore data dynamically.

Additional Code Aspects

• Error Handling:
  • Robust error handling mechanisms are incorporated to manage potential issues during data processing and visualization. This ensures the application remains stable and provides meaningful error messages to the user.
• Performance Optimization:
  • The code includes performance optimization techniques, such as data caching and efficient DOM manipulation, to ensure smooth and responsive user experiences even with large datasets.
• Modular Design:
  • The codebase follows a modular design pattern, breaking down functionality into reusable components. This enhances maintainability and scalability, allowing for easy updates and feature additions in the future.
• Data Inconsistencies:
  • Addressed by standardizing date formats and ensuring all data points are complete.
• Visualization Complexity:
  • Simplified by using clear color codes and interactive elements to enhance user experience.

Visualization Features

• Interactive Elements: Users can hover over data points to see detailed information about each case.
• Color Coding: Different colors represent various data points (ILI, confirmed cases, hospitalizations, deaths). The intensity of the color indicates the number of cases.

Challenges and Solutions

• Data Inconsistencies: Addressed by standardizing date formats and ensuring all data points are complete.
• Visualization Complexity: Simplified by using clear color codes and interactive elements to enhance user experience.