PathPandem 1.0

Simulate Pandemic Pathogen Outbreak

## 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.