Smoking and Heart Disease Quiz

Systems Analysis and Environmental Modeling

• This course, 040918401, covers systems analysis and environmental modeling, specifically focusing on macro-to-micro approaches with multidisciplinary applications.
• The course material includes a book titled "Systems Analysis and Modeling: A Macro-to-Micro Approach with Multidisciplinary Applications" by Donald W. Boyd.

General Circulation Models (GCMs)

• GCMs, also known as Global Climate Models, are numerical models that simulate physical processes in the atmosphere, ocean, cryosphere, and land surface.
• GCMs are advanced tools to simulate the response of the global climate system to increasing greenhouse gas concentrations.
• GCMs are crucial for predicting and understanding climate behavior on various scales (seasonal, annual, decadal, centennial).
• Their potential lies in providing geographically consistent regional climate change estimates essential for impact analysis.
• GCMs help evaluate climate changes due to natural factors, human activities, or a combination of both.
• GCM results and projections inform national, regional, and local decisions, including water resource management, agriculture, transportation, and urban planning.

Components of GCMs

• GCMs incorporate various components to model the Earth's climate system:
  • Atmospheric Models: Simulate atmospheric dynamics, radiation, and water vapor processes.
  • Ocean Models: Represent ocean currents, heat transport, and ocean-atmosphere interactions.
  • Land Surface Models: Account for soil moisture, vegetation, and land use changes.
  • Cryosphere Models: Include glaciers, ice sheets, and sea ice dynamics.

Basic Working Principles of GCMs

• GCMs use a 3D grid system that divides the Earth's surface into horizontal and vertical cells to calculate climate variables.
• Smaller grid sizes improve resolution, offering more detailed results but requiring greater computational power.
• Time steps simulate changes over time, typically in hourly to annual increments.
• GCMs consider both natural forcings (solar radiation and volcanic eruptions) and anthropogenic forcings (greenhouse gas emissions and deforestation).

Types of GCMs

• Atmosphere-Ocean General Circulation Models (AOGCMs): Coupled systems of atmospheric and oceanic models to study interactions between atmosphere and oceans.
• Earth System Models (ESMs): Extend AOGCMs by including carbon cycles, biogeochemical processes, and vegetation dynamics.
• Regional Climate Models (RCMs): High-resolution models for specific regions, embedded within GCMs to improve regional specific projections.

Recent Developments in GCMs

• High-Resolution Models: Increasingly detailed and resolved models to provide more accurate representation of finer-scale features, like hurricanes and urban heat islands.
• Coupling with AI: Using machine learning to improve model parameterization and reduce computation time.
• CMIP6 Models: The latest model generation used in the IPCC's Sixth Assessment Report.

Conceptual Model Diagram: SIR Epidemic Model

• State Variables:
  • Susceptibles (S): Individuals susceptible to a disease.
  • Infectious (I): Infected individuals capable of transmitting the disease.
  • Recovered (R): Individuals recovered who are immune to the disease.
• Parameters: a (recovery), β (transmission)
• This diagram illustrates the SIR model for understanding the spread of infectious diseases.

The SIR Model for Spread of Disease

• Provides a mathematical model for infectious diseases.
• Includes initial susceptible (S(0)), removed (R(0)) populations, total population (N), basic reproduction number (R₀), and time-dependent susceptible (S(t)), and removed (R(t)) populations.

Stages of Modeling

• A four-step process is outlined: Build, Generate Predictions & Analyze, Validate, and Apply.

Statistical/Empirical Models

• These models use simple theoretical analysis or empirical investigations to determine the relationship between variables.
• This is usually visualized through a scatter plot that show trends.
• The scatter plot displays the relationship between two quantitative variables, with one being independent (X) and the other dependent (Y).

Why Use Mathematical/Statistical Models (General)

• After conceptual modeling, mathematical models are developed to quantify system behavior.
• Models consist of:
  • Factors/forces comprising the model.
  • Relationships between the factors/forces.
  • Numbers often represent factors like animal numbers or resource availability.
  • Mathematical signs (like +, -, X) represent relationships between factors.
  • Parentheses and brackets define the order of mathematical operations.
• Complex models build upon simpler ones for validation.
• Models are compared with observational data to identify strengths and weaknesses.

Scatter Plots

• Scatter plots depict the relationship between two variables visually.
• The plot pattern reveals the relationship type:
  • Positive relationship: Variables increase or decrease together.
  • Negative relationship: As one variable increases, the other decreases.
  • No relationship: No clear pattern between the variables.

Specific Example: Smoking and Blood Pressure

• An example shows that smoking and systolic blood pressure have a positive correlation. Increasing smoking levels correlate to an increase in systolic blood pressure.

Specific Example: Heart Disease and Cigarettes

• Data on heart disease and cigarette smoking in 21 developed countries is presented.
• A linear correlation between cigarette consumption and CHD mortality rates is reported.

Climate Scenarios and Predictions

• Representative Concentration Pathways (RCPs):
  • RCP 2.6: Low emissions and strong mitigation.
  • RCP 4.5: Intermediate emission stabilization.
  • RCP 8.5: High emissions ("business as usual").
• Shared Socioeconomic Pathways (SSPs): Integrate socioeconomic factors (population growth and economic trends) to inform predictions.