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https://github.com/paul-em/seir
SEIR Model in JavaScript
https://github.com/paul-em/seir
covid-19 epidemiology infectious-diseases javascript predictive-modeling seir
Last synced: about 2 months ago
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SEIR Model in JavaScript
- Host: GitHub
- URL: https://github.com/paul-em/seir
- Owner: paul-em
- License: mit
- Created: 2020-03-28T22:28:15.000Z (almost 5 years ago)
- Default Branch: master
- Last Pushed: 2023-01-05T17:41:50.000Z (about 2 years ago)
- Last Synced: 2023-11-04T17:06:55.984Z (about 1 year ago)
- Topics: covid-19, epidemiology, infectious-diseases, javascript, predictive-modeling, seir
- Language: TypeScript
- Homepage:
- Size: 1 MB
- Stars: 1
- Watchers: 2
- Forks: 0
- Open Issues: 9
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# SEIR Model in JavaScript
This is a JavaScript implementation of the classical infectious disease model SEIR (Susceptible → Exposed → Infected → Removed) used in epidemiology to simplify the modelling of infectious diseases. [Learn more](https://en.wikipedia.org/wiki/Compartmental_models_in_epidemiology#The_SEIR_model)
Most of this code was copied from the wonderfull [Epidemic Calculator](http://gabgoh.github.io/COVID/index.html) and rewritten in TypeScript and published on npm.
> Can be used in node.js and in the browser.
Zero dependencies.
## Getting started
```npm install seir```
## Usage
```js
import SeirModel from 'seir';
const covid19 = new SeirModel({
r0: 2.2,
dDeath: 32 - 2.9,
dIncubation: 5.2,
dInfectious: 2.9,
dRecoveryMild: 14 - 2.9,
dRecoveryServere: 31.5 - 2.9,
dHospitalLag: 5,
cfr: 0.02,
pServere: 0.2,
dt: 2,
});
const timeline = covid19.calculate({
population,
r0ReductionPercent: 10,
r0ReductionDay: 5,
days: 300,
});
/*
[
{ deaths: 0, hospitalized: 0, recovered: 0, totalInfected: 1, infected: 1, exposed: 2 },
{ deaths: 0, hospitalized: 0, recovered: 1, totalInfected: 4, infected: 3, exposed: 5 },
...
]
*/
console.log(timeline);
```
## Parameters
### new SeirModel
| Name | Required | Type | Default | Description |
| --- | --- | --- | --- | --- |
| **r0** | true | Number | - | Maximnum value of R0 if no measures are taken |
| **dDeath** | true | Number | - | Time from start of infection to death |
| **dIncubation** | true | Number | - | Length of incubation period |
| **dInfectious** | true | Number | - | Duration patient is infectious |
| **dRecoveryMild** | true | Number | - | Recovery time for mild cases |
| **dRecoveryServere** | true | Number | - | Recovery time for servere cases |
| **dHospitalLag** | true | Number | - | Time to hospitalization |
| **cfr** | true | Number | - | Case fatality rate |
| **pServere** | true | Number | - | Hospitalization rate |
| **duration** | false | Number | 7 \* 12 \* 1e10 | Duration of simulation |
| **dt** | false | Number | 1 | Time step size (days) |
| **integrator** | false | String | RK4 | Integrator to use. Available are Euler, Midpoint, Heun, K3, SSP33, SSP43, RK4, RK38 |
### model.calculate
| Name | Required | Type | Default | Description |
| --- | --- | --- | --- | --- |
| **population** | true | Number | - | Population of the location to run the simulation on |
| **initiallyInfected** | false | Number | 1 | Number of initially infected people |
| **initiallyExposed** | false | Number | 0 | Number of initially exposed people |
| **r0ReductionPercent** | false | Number | 0 | How much the r0 value should be reduced (for example because of measures) |
| **r0ReductionDay** | false | Number | 0 | On which the the reduction should take place |
| **days** | false | Number | 110 | For how many days the calculation should return a value |
## Credits
[Gabriel Goh](https://github.com/gabgoh) for creating the [Epidemic Calculator](http://gabgoh.github.io/COVID/index.html) and therefore implementing pretty much all of these calculations. He also acknowledged
[Steven De Keninck](https://enkimute.github.io/) for RK4 Integrator. [Chris Olah](https://twitter.com/ch402), [Shan Carter](https://twitter.com/shancarter) and [Ludwig Schubert](https://twitter.com/ludwigschubert) wonderful feedback. [Nikita Jerschov](https://twitter.com/NikitaJer) for improving clarity of text. Charie Huang for context and discussion.
## Licence
MIT