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COVID-19 Vaccination predication in Switzerland – Widget Raw
// Variables used by Scriptable.
// These must be at the very top of the file. Do not edit.
// icon-color: red; icon-glyph: plus-square;
/**
* @akosma wrote this nice little script (in python) to try to figure out when the
* Swiss population would be fully vaccinated (with both doses of
* the COVID-19 vaccination). The script would print out the result to the
* console. This is the JS version, which displays the result in a widget.
*
*
* You'll find the original code source here:
* https://gitlab.com/akosma/covid-19-vaccination-in-switzerland/
* Thank you @akosma
*/
async function getPredictedDate() {
// Download list of all latest files available for download
const filesUrl = 'https://www.covid19.admin.ch/api/data/context'
const filesResp = new Request(filesUrl)
const filesJson = await filesResp.loadJSON()
// Download the JSON stats for fully vaccinated people
const fullyVaccUrl = filesJson['sources']['individual']['json']['fullyVaccPersons']
const fullyVaccResp = new Request(fullyVaccUrl)
const fullyVaccJson = await fullyVaccResp.loadJSON()
// Filter JSON data for the whole country
// Schema docs:
// https://www.covid19.admin.ch/api/data/documentation/models/sources-definitions-vaccinationincomingdata.md
chEntries = fullyVaccJson.filter((entry) => entry.geoRegion === 'CH')
population = chEntries[0]['pop']
// Aggregate for regression
aggregates = chEntries.map((entry) => [new Date(entry.date).getTime() / 1000, entry.sumTotal])
// Linear regression
const result = linear(aggregates, { order: 2, precision: 20, period: null })
const gradient = result.equation[0]
const yIntercept = result.equation[1]
// Predict the date where everyone will be vaccinated at the current rate
const prediction = (population - yIntercept) / gradient
const predictedDate = new Date(prediction * 1000)
return predictedDate
}
async function createWidget() {
const predictedDate = await getPredictedDate()
// Widget
const w = new ListWidget()
w.backgroundColor = new Color('#edf2f4')
// Logo 💉
const logo = w.addText('💉')
logo.font = Font.title1()
w.addSpacer(5)
// Description
const txt = w.addText('All of 🇨🇭 vaccinated on:')
txt.font = Font.subheadline()
txt.textColor = new Color('#8d99ae')
// Prediction
const prediction = w.addText(`${predictedDate.toLocaleDateString('en-CH')}`)
prediction.font = Font.headline()
prediction.textColor = new Color('#2b2d42')
w.addSpacer()
return w
}
const widget = await createWidget()
if (config.runsInWidget) {
Script.setWidget(widget)
Script.complete()
} else {
widget.presentSmall()
}
// =============================================================================
// part of regression-js
// https://github.com/Tom-Alexander/regression-js
/**
The MIT License (MIT)
Copyright (c) Tom Alexander <me@tomalexander.co.nz>
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in
all copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
THE SOFTWARE.
*/
/**
* Round a number to a precision, specificed in number of decimal places
*
* @param {number} number - The number to round
* @param {number} precision - The number of decimal places to round to:
* > 0 means decimals, < 0 means powers of 10
*
*
* @return {numbr} - The number, rounded
*/
function round(number, precision) {
const factor = 10 ** precision
return Math.round(number * factor) / factor
}
function linear(data, options) {
const sum = [0, 0, 0, 0, 0]
let len = 0
for (let n = 0; n < data.length; n++) {
if (data[n][1] !== null) {
len++
sum[0] += data[n][0]
sum[1] += data[n][1]
sum[2] += data[n][0] * data[n][0]
sum[3] += data[n][0] * data[n][1]
sum[4] += data[n][1] * data[n][1]
}
}
const run = len * sum[2] - sum[0] * sum[0]
const rise = len * sum[3] - sum[0] * sum[1]
const gradient = run === 0 ? 0 : round(rise / run, options.precision)
const intercept = round(sum[1] / len - (gradient * sum[0]) / len, options.precision)
const predict = (x) => [round(x, options.precision), round(gradient * x + intercept, options.precision)]
const points = data.map((point) => predict(point[0]))
return {
points,
predict,
equation: [gradient, intercept],
// r2: round(determinationCoefficient(data, points), options.precision),
string: intercept === 0 ? `y = ${gradient}x` : `y = ${gradient}x + ${intercept}`,
}
}
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