aepstein/longevity-simulator

Longevity Simulator

A Python toolkit for modeling mortality rates and simulating the effects of longevity interventions.

Overview

This project provides tools to explore "what-if" scenarios for human longevity by modeling:

• —Disease interventions: What if we cured cancer? Or cardiovascular disease?
• —Aging interventions: What if we could stop or slow biological aging?
• —Survival analysis: Calculate survival curves and median lifespans under different scenarios

Uses real-world data from:

• —CDC/NCHS United States Life Tables (2010-2019)
• —CDC Multiple Cause of Death (MMCD) database (2022)

Features

• —Load and analyze mortality data from CDC life tables
• —Model aging interventions:
• —Stop aging at a specific age
• —Slow the rate of biological aging
• —Model disease cures by removing specific causes of death
• —Calculate survival curves and median lifespans
• —Categorize causes of death using ICD-10 codes

Installation

pip install -r requirements.txt

Quick Start

from src import load_mortality_rates, stop_aging, calculate_survival_curve, calculate_median_lifespan

# Load preprocessed US mortality data
mortality_rates = load_mortality_rates('data/mortality_rates_total.csv')

# Model stopping aging at 25
adjusted_rates = stop_aging(mortality_rates, final_age=25, pad_to_age=200)

# Calculate survival curve
survival = calculate_survival_curve(adjusted_rates)

# Get median lifespan
median = calculate_median_lifespan(survival)
print(f"Median lifespan if aging stopped at 25: {median} years")

Project Structure

longevity-simulator/
├── src/                      # Python modules
│   ├── mortality.py          # Load mortality data
│   ├── interventions.py      # Aging interventions
│   ├── causes.py             # Cause of death analysis
│   └── survival.py           # Survival curve calculations
├── data/                     # Processed data (committed to git)
│   ├── mortality_rates_total.csv      # ~1 KB
│   ├── mortality_rates_male.csv
│   ├── mortality_rates_female.csv
│   ├── cause_fractions_total.csv      # ~23 KB
│   ├── cause_fractions_male.csv
│   └── cause_fractions_female.csv
├── raw_data/                 # Raw data files (not in git, ~300 MB)
│   ├── MMCD_2022.parquet
│   └── life_table_*.txt
├── preprocessing/            # Scripts to process raw data
│   ├── process_mmcd.py
│   ├── process_life_table.py
│   └── README.md
├── notebooks/                # Jupyter notebooks
│   └── demo.ipynb
├── results/                  # Output plots and figures
├── requirements.txt          # Python dependencies
└── README.md                 # This file

Data Sources

• —Life Tables: CDC/NCHS United States Life Tables
• —Mortality Data: CDC Multiple Cause of Death Database

Data Preprocessing

The project uses preprocessed data stored in data/ (small CSV files, ~25 KB total). This allows the repository to be easily shared on GitHub without large files.

To regenerate the processed data from raw sources:

1. 1.Place raw data files in raw_data/
2. 2.Run preprocessing scripts:

   cd preprocessing
   python process_life_table.py
   python process_mmcd.py

See preprocessing/README.md for details.

Use Cases

1. 1.Research: Model the impact of medical breakthroughs on population lifespan
2. 2.Education: Visualize how aging and disease affect mortality
3. 3.Policy: Estimate the demographic impact of public health interventions
4. 4.Curiosity: Explore "what-if" scenarios for human longevity

Future Development

• —Interactive dashboard using Plotly Dash
• —Additional intervention models (combination therapies, age-specific treatments)
• —Comparative analysis across countries and time periods
• —Economic impact modeling

License

MIT License - see LICENSE file for details

Author

Alexander Epstein, Cao Lab, Rockefeller University