https://github.com/amanb2000/Statistical_Mechanics_Distancing

Simulations and calculations relating to a statistical mechanical analysis of disease spread in the context of social distancing in the COVID-19 viral pandemic.
https://github.com/amanb2000/Statistical_Mechanics_Distancing

Last synced: 14 days ago
JSON representation

Simulations and calculations relating to a statistical mechanical analysis of disease spread in the context of social distancing in the COVID-19 viral pandemic.

• Host: GitHub
• URL: https://github.com/amanb2000/Statistical_Mechanics_Distancing
• Owner: amanb2000
• License: mit
• Created: 2021-01-10T06:49:46.000Z (almost 4 years ago)
• Default Branch: main
• Last Pushed: 2021-01-10T07:33:44.000Z (almost 4 years ago)
• Last Synced: 2024-08-01T16:51:06.633Z (3 months ago)
• Size: 2.93 KB
• Stars: 1
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

README

        
# Statistical Mechanics Distancing

Simulations and calculations relating to a statistical mechanical analysis of disease spread in the context of social distancing in the COVID-19 viral pandemic.

## Introduction

Here I aim to elucidate the nature of 1-on-1 airborne disease spread utilizing principles of statistical mechanics and particle simulation.

Emphasis is placed on the relationship between `probability of disease spread` and `time`. Current legislation states that people should distance at "2 meters apart at all times". Is that a good approximation for the way disease spreads? Would it make more sense to contextualize it as "6 foot-seconds" apart? Is it better to be 7 feet apart for 4 hours of 5 feet apart for 4 seconds? 

The simulations contained herin aim to elucidate this. 

## Models

### 1: Parameterized Spheres in Maxwell-Boltzmann Distributed Gas

This model is the simplest possible (parameterized) model for disease spread at different distances. Normal particles are continuously converted to viral particles within the 'infected' sphere.

- Gas is an ideal Maxwell-Boltzmann distributed substance with no currents.

- Two individuals are modeled as spheres.

- Parameters:

	- Sphere size.

	- Number of particles.

	- Temperature.

	- Distance between two spheres.

	- Rate of viral particle conversion.

	- Threshold for infection of uninfected sphere (number of particles or concentration).

*Parameters to Research:*

- [ ] Boltzmann distribution formula.

- [ ] "Critical mass" of airborne virus concentration for infection.

- [ ] Concentration of viral particles eminating from an infected individual.

	- [ ] Rate of viral particle emision.

*Problems with model:*

- Does not account for wind, breathing onto other people, sneezing/coughing.

	- Wind is likely a turbulent property -- would not be a simple addition to the mean velocity along an axis. 

- People aren't spheres.

- It's unclear if viral concentration is the key factor.

The model could potentially serve as a lower-bound for transmission...