Metadata-Version: 2.1
Name: PathPandem
Version: 1.0
Summary: Simulate Pandemic Pathogen Outbreak
Home-page: UNKNOWN
Author: Pradyumna Paranjape
Author-email: pradyparanjpe@rediffmail.com
License: GPLv3
Platform: UNKNOWN
Requires-Dist: numpy
Requires-Dist: gooey
Requires-Dist: matplotlib

## Usage:

### GitHub Repository
git clone https://github.com/pradyparanjpe/PathPandem.git

### Pre-requisites for running from the source-code:
1. Python3.8 or higher
2. Numpy >= 1.18
3. Matplotlib >= 3.2.1
4. Gooey >= [1.0.3](https://github.com/chriskiehl/Gooey)

*1 may be installed from official source; further, 2, 3, 4 may be installed by command `pip install <module>`*.

## pip
pip install PathPandem

## Legend:
### Background Colour:
**Movements**
- Green: No restrictions on movement.
- Red: Lockdown Imposed.

**Scientific Progress**
- Blue: Drug discovered.
- Cyan: Vaccine discovered.

**Combinations**
- Grey: Red + Cyan.
- Magenta: Red + Blue.
- (Any other standard RGB combinations).

## Caution:
1. Population more than 10000 may stall the system.
2. Tested only on Linux running from source-code.
3. *True* numbers are plotted. However in reality, infection manifests symptoms after an initial lag of 1-3 days and test results appear further later by 1-2 days. Hence, graph trends need be imagined as having shifted suitably.
4. Although Infection may appear to exhaust in small sized, limited population; in reality, due to birth of new individuals, and in a very large population, the pathogen persists around at extemely low density.

## Composition of scenario:
- The GUI only edits the blanket population behaviour.
- A heterogenous population can be composed using basic Python scripting in the `spread_simul.py` to construct heterogenously behaving population.

## TODO:
- Replace unimodal movement of people around their home to bimodal movement between home and workplace.
- Parallelize numpy matrix `ufuncs` if possible.
- Include asymptomatic patients/carriers. Limit movement of serious cases [although this won't have a visible effect for diseases with majority of cases being mild].
- Animation, saved as mp4 for review

## Epidemiological explanation:
- Herd immunity starts reducing viral presence in community after viral steady state. i.e. plot of *Active* patients flattens. This happens when [1 - (1/R_{0})] fraction of the community becomes resistant. (Through vaccination or exposure)
- Medicine development is fairly a rare event given the rightful stringency involved in testing.
- With small population size, random fluctuations become impactful. Multiple runs with same parameters are recommended.
- Visualization is recommended only with very small population size.


