2d:null 2e:["$","title",null,{"children":"COVID19 - recent worldwide evolution | Ernesto Laval | Observable"}] 33:T54f,{ const metric = "new_deaths_smoothed_per_million"; const metricLabel = "Daily deaths per million"; const dataPlot = _.chain(targetData) .map((d) => d.items) .flatten() .value(); const maxValue = _.chain(dataPlot) .map((d) => +d[metric]) .max() .value(); return Plot.plot({ height: 700, width, marginTop: 160, marginLeft: 120, axis: null, x: { axis: "top", round: true }, y: { //type: "log", domain: [0, maxValue / 2] }, color: { legend: true, label: metricLabel, scheme: "purd" }, fy: { domain: targetData.map((d) => d.location) // preserve input order }, facet: { data: dataPlot, y: "location" }, marks: [ Plot.areaY(dataPlot, { x: (d) => new Date(d["date"]), y: (d) => +d[metric], z: "location", fill: (d) => +d[metric], sort: "date" }), Plot.lineY(dataPlot, { x: (d) => new Date(d["date"]), fill: (d) => +d[metric], z: "location", strokeWidth: 1, sort: "date" }), Plot.text( dataPlot, Plot.selectFirst({ x: d3.min(dataPlot, (d) => new Date(d.date)), text: "location", dx: -3, frameAnchor: "right" }) ) ] }); }34:T7a2,{ const data = targetData.filter((d) => d.location == selectedCountry); const chartCases = buildInlineCharts({ data: data, headlineMetric: "new_tests_smoothed_per_thousand", metric: "new_cases_smoothed_per_100K", yLabel: "New cases x 100K", baseValue: 1, threshold: 10, thresholdColor: d3.schemeTableau10[3], weeks: numWeeks, yFormat: ".1f", colors: ["lightgrey"], headlines: [], //referenceData: referenceData, referenceLabel: selectedReference, referenceDates: referenceDates, fullWidth: true, hidePeak: true }); const chartPositiveRate = buildInlineCharts({ data: data, headlineMetric: "new_tests_smoothed_per_thousand", metric: "positive_rate", baseValue: 0.01, threshold: 0.1, weeks: numWeeks, yFormat: ".0%", yLabel: "Positive rate", colors: ["lightgrey"], headlines: [], thresholdColor: d3.schemeTableau10[1], //referenceData: referenceData, referenceLabel: selectedReference, referenceValues: [], referenceDates: referenceDates, fullWidth: true, hidePeak: true }); const chartDeaths = buildInlineCharts({ data: data, //headlineMetric: "new_tests_smoothed_per_thousand", metric: "new_deaths_smoothed_per_million", baseValue: 0.1, threshold: 1, //reference: refChile, weeks: numWeeks, yFormat: ".1f", yLabel: "Deaths per millionn", thresholdColor: d3.schemeTableau10[2], colors: ["lightgrey"], headlines: [], //referenceData: referenceData, referenceLabel: selectedReference, defaultMax: 1, referenceValues: [], referenceDates: referenceDates, fullWidth: true, hidePeak: true }); return html`

Cases

${chartCases}

Positive rate

${chartPositiveRate}

Deaths

${chartDeaths}

`; }35:T4db,{ const metric = "new_tests_smoothed_per_thousand"; const plotData = summaryData .filter((d) => d.continent.match(/Europe/) && +d.population > 1000000) .map((d) => { d[metric] = _.chain(d.items) .filter((d) => +d[metric]) .last() .value() && +_.chain(d.items) .filter((d) => +d[metric]) .last() .value()[metric]; d["test_date"] = _.chain(d.items) .filter((d) => +d[metric]) .last() .value() && _.chain(d.items) .filter((d) => +d[metric]) .last() .value()["date"]; return d; }); //return plotData; return Plot.plot({ style: { fontSize: 14 }, width: width, height: width * 0.4, marginLeft: 50, marginTop: 30, marginBottom: 200, color: { legend: false }, x: { type: "band", tickRotate: 90, label: "País" }, y: { label: "Tests diarios por mil habitantes" //domain: [0, 2000] }, marks: [ Plot.barY(plotData, { x: "location", y: metric, sort: { x: "y", reverse: true }, fill: (d) => (d["location"].match(/Portugal/) ? "Portugal" : "Otro") }) ] }); }36:T519,{ const metric = "people_fully_vaccinated_per_hundred"; const plotData = summaryData .filter((d) => d.continent.match(/Europe/) && +d.population > 1000000) .map((d) => { d[metric] = _.chain(d.items) .filter((d) => +d[metric]) .last() .value() && +_.chain(d.items) .filter((d) => +d[metric]) .last() .value()[metric]; d["dateVacuna"] = _.chain(d.items) .filter((d) => +d[metric]) .last() .value() && _.chain(d.items) .filter((d) => +d[metric]) .last() .value()["date"]; return d; }); //return plotData; return Plot.plot({ style: { fontSize: 14 }, width: width, height: width * 0.4, marginLeft: 150, marginTop: 30, marginBottom: 200, color: { legend: false }, x: { type: "band", tickRotate: 90, label: "País" }, y: { label: "Cobertura de vacuna con dosis primaria completa (%)", tickFormat: ".1%", domain: [0, 1] }, marks: [ Plot.barY(plotData, { x: "location", y: (d) => d[metric] / 100, sort: { x: "y", reverse: true }, fill: (d) => (d["location"].match(/Portugal/) ? "Portugal" : "Otro") }) ] }); }37:T5a0,{ const dataPlot = _.chain(dataRecentGrowth) .filter((d) => d.location == selectCountry) .map((d) => d.items) .flatten() .filter((d) => d.date >= fromDate) .sortBy((d) => d.date) .value(); const max = _.chain(dataPlot) .map((d) => +d.new_cases_smoothed_per_million / 10) .max() .value(); //return dataPlot; return Plot.plot({ width: width * 0.7, style: { fontSize: 14 }, marginTop: 50, marginRight: 100, y: { domain: [0, max], type: "linear", label: "Casos diarios x 100 mil habitantes (media móvil 7 días)" }, marks: [ Plot.lineY(dataPlot, { y1: (d) => +d.record_1weekAgo.new_cass_smoothed_per_million, y: (d) => +d.new_cases_smoothed_per_million / 10, x1: (d) => new Date(d.record_2weeksAgo.date), x: (d) => new Date(d.date), stroke: "location", strokeWidth: 3, bend: false }), Plot.lineY(dataPlot, { y: (d) => +d.new_deaths_smoothed_per_million * 50, x: (d) => new Date(d.date), stroke: "location", strokeWidth: 3 }), Plot.text( dataPlot, Plot.selectLast({ y: (d) => +d.new_cases_smoothed_per_million / 10, x: (d) => new Date(d.date), text: "location", textAnchor: "start", dx: 5, z: "location", bend: true }) ) ] }); }38:T520,dataRecentGrowth = { const plotData = _.chain(summaryData) .map((d) => { d.growth = d.record_1weekAgo && +d.record_current.new_cases_smoothed_per_million / +d.record_1weekAgo.new_cases_smoothed_per_million; return d; }) .filter( (d) => +d.record_current.new_cases_smoothed_per_million > 500 && d.population > 1000000 && d.growth > 1.3 ) .sortBy((d) => -d.growth) .value(); //d.record_current.new_cases_smoothed_per_million return plotData; return Plot.plot({ marginLeft: 100, marginRight: 100, marks: [ Plot.lineY( plotData, Plot.map( { y: rollsum }, { x: (d) => new Date(d["date"]), y: "new_deaths", interval: d3.utcDay, stroke: "lightgrey" } ) ), Plot.text( plotData, Plot.selectLast( Plot.map( { y: rollsum, text: rollsum }, { x: (d) => new Date(d["date"]), y: "new_deaths", text: (d) => `${d["new_deaths"]}`, textAnchor: "start", frameAnchor: "bottom", dy: -3 } ) ) ), Plot.ruleY([0]) ] }); }39:T488,{ const data = _.chain(targetData) .filter((d) => d.location == selectedCountry || d.location == "Italy") .map((d) => d.items) .flatten() .filter((d) => d.date >= "2020-01-01") .value(); return Plot.plot({ marginTop: 30, marginLeft: 80, marginRight: 200, width: width, height: 500, caption: "Data source: Our World in Data", style: { fontSize: 14 }, y: { label: "Daily cases per 100K people (log scale)", type: "log", grid: true, tickFormat: ".2f" //domain: [0.1, 300] }, marks: [ Plot.line(data, { x: (d) => new Date(d.date), y: (d) => +d.new_cases_smoothed_per_100K, stroke: "location", strokeWidth: 3 }), Plot.text( data, Plot.selectLast({ x: (d) => new Date(d.date), dx: 5, y: (d) => +d.new_cases_smoothed_per_100K, text: (d) => `${d.location} (${d3.format(".0f")( d.new_cases_smoothed_per_100K )})`, textAnchor: "start", fontSize: 14, fill: "location" }) ) ] }); }3a:T911,{ //const scaleType = scaleType_deathsChart; /*const dataCOVID = _.chain(dataChile) .filter((d) => d.localidad == "Chile" && d.fecha >= "2022-01") .map((d) => ({ fecha: d.fecha, valor: d.nuevos_casos_semanales / 7 })) .value();*/ const dataCOVID = _.chain(targetData) .filter((d) => d.location == selectedCountry) .map((d) => d.items) .flatten() .filter((d) => d.date >= "2021-12-21") .map((d) => ({ fecha: d.date, valor: +d.new_cases_smoothed })) .sortBy((d) => d.fecha) .value(); //return dataCOVID; const lastValue = _.last(dataCOVID); const firstValue = _.first(dataCOVID); const maxValue = _.chain(dataCOVID) .map((d) => d.valor) .max() .value(); const weeklyReferences = _.range(0, dataCOVID.length / 7).map((i) => moment(lastValue.fecha).subtract(i, "week").format("YYYY-MM-DD") ); const k = 7; const anchor = "end"; //return lastValue; return Plot.plot({ width: width * 0.95, height: width / 2, style: { fontSize: 18 }, y: { domain: [500, maxValue * 1.5], type: "log", label: `Promedio de casos diarios en últimos 7 días (escala ${ "log" == "log" ? "logarítmica" : "lineal" })` }, x: { tickFormat: (d, i) => weeklyReferences.includes(d) ? moment(d).month() == 0 ? moment(d).format("D MMM") : moment(d).format("D MMM") : "", tickSize: 0 }, marginTop: 50, marginRight: 100, marginLeft: 80, marks: [ Plot.ruleY([+lastValue["valor"]], { strokeWidth: 0.5, stroke: (d) => "lightgrey" }), Plot.line(dataCOVID, { x: (d) => d["fecha"], y: (d) => +d["valor"], stroke: "grey", strokeWidth: 2 }), Plot.dot( dataCOVID.filter((d) => weeklyReferences.includes(d.fecha)), { x: (d) => d["fecha"], y: (d) => +d["valor"], r: 6, fill: "red" } ), Plot.text( dataCOVID.filter((d) => weeklyReferences.includes(d.fecha)), { x: (d) => d["fecha"], dy: -20, y: (d) => +d["valor"], text: (d) => d3.format(".2s")(+d["valor"]) } ) ] }); }3b:T70d,{ const data = _.chain(targetData) .filter((d) => d.location == selectedCountry) .map((d) => d.items) .flatten() .filter( (d) => d.date >= "2020-01-01" && +d.people_fully_vaccinated_per_hundred > 0 ) .value(); return Plot.plot({ marginTop: 30, marginLeft: 80, marginRight: 200, width: width, height: 500, caption: "Data source: Our World in Data", style: { fontSize: 14 }, y: { label: "Vaccination (%)", //type: "log", grid: true, tickFormat: ".1%" //domain: [0.1, 300] }, marks: [ Plot.line(data, { x: (d) => new Date(d.date), y: (d) => +d.people_fully_vaccinated_per_hundred / 100, stroke: (d) => "primario", strokeWidth: 3 }), Plot.line(data, { x: (d) => new Date(d.date), y: (d) => +d.total_boosters_per_hundred / 100, stroke: (d) => "refuerzo", strokeWidth: 3 }), Plot.text( data, Plot.selectLast({ x: (d) => new Date(d.date), dx: 5, y: (d) => +d.people_fully_vaccinated_per_hundred / 100, text: (d) => `Initial protocol (${d3.format(".1%")( d.people_fully_vaccinated_per_hundred / 100 )})`, textAnchor: "start", fontSize: 14, fill: (d) => "primario" }) ), Plot.text( data, Plot.selectLast({ x: (d) => new Date(d.date), dx: 5, y: (d) => +d.total_boosters_per_hundred / 100, text: (d) => `Booster (${d3.format(".1%")(d.total_boosters_per_hundred / 100)})`, textAnchor: "start", fontSize: 14, fill: (d) => "refuerzo" }) ) ] }); }3c:T50c,{ const data = _.chain(targetData) .filter((d) => selectedCountries.includes(d.location)) .map((d) => d.items) .flatten() .filter((d) => d.date >= "2020-05-01") .value(); return Plot.plot({ marginTop: 30, marginLeft: 60, marginRight: 200, width: width, height: 600, caption: "Data source: Our World in Data", style: { fontSize: 14 }, y: { label: "People fully vaccinated (%)", type: "linear", tickFormat: ".0%", grid: true }, marks: [ Plot.line( data.filter((d) => +d.people_fully_vaccinated_per_hundred), { x: (d) => new Date(d.date), y: (d) => +d.people_fully_vaccinated_per_hundred / 100, stroke: "location", strokeWidth: 3 } ), Plot.text( data.filter((d) => +d.people_fully_vaccinated_per_hundred), Plot.selectLast({ x: (d) => new Date(d.date), dx: 5, y: (d) => +d.people_fully_vaccinated_per_hundred / 100, text: (d) => `${d.location} (${d3.format(".0%")( d.people_fully_vaccinated_per_hundred / 100 )})`, textAnchor: "start", fontSize: 14, fill: "location" }) ) ] }); }3d:T40f,{ const data = _.chain(targetData) .filter((d) => selectedCountries.includes(d.location)) .map((d) => d.items) .flatten() .filter((d) => d.date >= "2020-05-01") .value(); return Plot.plot({ marginTop: 30, marginRight: 200, width: width, height: 600, caption: "Data source: Our World in Data", style: { fontSize: 14 }, y: { label: "Total deaths per million people (log scale)", type: "log", grid: true }, marks: [ Plot.line(data, { x: (d) => new Date(d.date), y: (d) => +d.total_deaths_per_million, stroke: "location", strokeWidth: 3 }), Plot.text( data, Plot.selectLast({ x: (d) => new Date(d.date), dx: 5, y: (d) => +d.total_deaths_per_million, text: (d) => `${d.location} (${d3.format(".0f")(d.total_deaths_per_million)})`, textAnchor: "start", fontSize: 14, fill: "location" }) ) ] }); }3e:T756,{ const data = _.chain(targetData) .filter((d) => selectedCountries.includes(d.location)) .map((d) => d.items) .flatten() .filter((d) => d.date >= "2022-08-22") .value(); const maxDate = _.chain(data) .map((d) => d.date) .max() .value(); const weekDates = _.chain(_.range(0, 8)) .map((i) => moment(maxDate).subtract(i, "weeks").format("YYYY-MM-DD")) .value(); const dataPlot = _.chain(data) .filter((d) => weekDates.includes(d.date)) .value(); return Plot.plot({ marginTop: 30, marginRight: 200, width: width, height: 600, caption: "Data source: Our World in Data", style: { fontSize: 18 }, x: { grid: true, domain: [new Date("2022-08-22"), new Date("2022-10-12")] }, y: { label: "Weekly hospital admissions per million (log scale)", type: "log", grid: true, domain: [30, 200] }, marks: [ Plot.line(data, { x: (d) => new Date(d.date), y: (d) => +d.weekly_hosp_admissions_per_million, stroke: "location", strokeWidth: 3 }), Plot.text( data, Plot.selectLast({ x: (d) => new Date(d.date), dx: 40, y: (d) => +d.weekly_hosp_admissions_per_million, text: (d) => `${d.location} `, textAnchor: "start", fontSize: 18, fill: "location" }) ), Plot.dot(dataPlot, { x: (d) => new Date(d.date), y: (d) => +d.weekly_hosp_admissions_per_million, fill: "location", r: 30 }), Plot.text(dataPlot, { x: (d) => new Date(d.date), y: (d) => +d.weekly_hosp_admissions_per_million, text: (d) => d3.format("d")(+d.weekly_hosp_admissions_per_million), fontWeight: "bold", fill: "white" }) ] }); }3f:T16cf,{ updateMomentLocale(); d3.formatDefaultLocale(es_ES); const deathsAttribute = "new_deaths_smoothed_per_million"; const casesAttribute = "new_cases_smoothed_per_million"; const colorCases = d3.schemeTableau10[0]; const colorDeaths = d3.schemeTableau10[2]; const minValue = 0.1; const data = _.chain(summaryData) .find((d) => targetCountry == d.location) .value() ["items"].filter((d) => d.date >= "2020-03-20"); const lastRecord = _.last(data); const maxDeaths2022 = _.chain(data) .filter((d) => d.date >= "2022") .maxBy((d) => +d[deathsAttribute]) .value(); const maxCases2022 = _.chain(data) .filter((d) => d.date >= "2022") .maxBy((d) => +d[casesAttribute]) .value(); const firstRecord = _.chain(data) .filter((d) => d.date >= "2022") .first() .value(); //return maxDeaths2022; return Plot.plot({ caption: "Fuente de datos: Our World in Data", width: width * 0.8, marginTop: 50, marginRight: 200, marginLeft: 100, style: { fontSize: 18 }, x: { tickFormat: (d) => moment(d).add(1, "day").month() == 0 ? moment(d).add(1, "day").format("YYYY") : moment(d).add(1, "day").format("MMM") }, y: { label: "Personas por millón (escala logarítmica)", type: "log", domain: [minValue, _.max(data.map((d) => +d[casesAttribute]))], tickFormat: ",f" //tickFormat: (d) => (d < 0 ? d3.format(".1f")(d) : d) //tickFormat: (d) => (d > 1 ? d3.format(".2s")(d) : d) }, marks: [ Plot.areaY(data, { x: (d) => new Date(d["date"]), y1: (d) => minValue, y2: (d) => +d[casesAttribute], fill: colorCases }), Plot.areaY( data.filter((d) => +d[deathsAttribute]), { x: (d) => new Date(d["date"]), y1: (d) => minValue, y2: (d) => +d[deathsAttribute] > minValue ? +d[deathsAttribute] : minValue, fill: colorDeaths } ), !showMax ? [] : [ Plot.ruleX([maxCases2022 && new Date(maxCases2022.date)], { stroke: "white", strokeDasharray: "1 0 1" }), Plot.ruleY([maxDeaths2022 && maxDeaths2022[deathsAttribute]], { stroke: colorDeaths, strokeDasharray: "1 0 1" }), Plot.ruleY([maxCases2022 && maxCases2022[casesAttribute]], { stroke: colorCases, strokeDasharray: "1 0 1" }), Plot.text( data, Plot.selectLast({ x: (d) => new Date(d["date"]), y: (d) => maxDeaths2022 && maxDeaths2022[deathsAttribute], dx: 5, dy: -10, text: (d) => `${moment(maxDeaths2022["date"]).format( "D MMM" )} | Máximo 2022`, textAnchor: "start", fill: colorDeaths }) ), Plot.text( data, Plot.selectLast({ x: (d) => new Date(d["date"]), y: (d) => maxDeaths2022 && maxDeaths2022[deathsAttribute], dx: 5, dy: +10, text: (d) => `${ maxDeaths2022 && d3.format(",.1f")(maxDeaths2022[deathsAttribute]) } muertes por millón`, textAnchor: "start", fill: colorDeaths }) ), Plot.text( data, Plot.selectLast({ x: (d) => new Date(d["date"]), y: (d) => maxCases2022 && maxCases2022[casesAttribute], dx: 5, dy: -20, text: (d) => maxCases2022 && `${moment(maxCases2022["date"]).format( "D MMM" )} | Máximo 2022`, textAnchor: "start", fill: colorCases }) ), Plot.text( data, Plot.selectLast({ x: (d) => new Date(d["date"]), y: (d) => maxCases2022 && maxCases2022[casesAttribute], dx: 5, dy: 0, text: (d) => maxCases2022 && `${d3.format(",.0f")( maxCases2022[casesAttribute] )} casos por millón`, textAnchor: "start", fill: colorCases }) ), Plot.text( data, Plot.selectLast({ x: (d) => new Date(d["date"]), y: (d) => maxCases2022 && maxCases2022[casesAttribute], dx: 5, dy: 20, text: (d) => maxCases2022 && `(${d3.format(".1f")( maxCases2022["new_tests_smoothed_per_thousand"] )} tests por 1,000)`, textAnchor: "start", fill: colorCases }) ) ], Plot.text([lastRecord], { x: (d) => new Date(d["date"]), y: (d) => (+maxDeaths2022[deathsAttribute] + +maxCases2022[casesAttribute]) / 4, dx: 5, dy: 20, text: (d) => `CASOS`, textAnchor: "start", fontWeight: "bold", fill: colorCases }), Plot.text([lastRecord], { x: (d) => new Date(d["date"]), y: (d) => +d[deathsAttribute] / 2, dx: 5, dy: 20, text: (d) => `MUERTES`, textAnchor: "start", fontWeight: "bold", fill: colorDeaths }) ] }); }40:T42d,{ const casesAttribute = "new_cases_smoothed_per_million"; const data = _.chain(summaryData) .find((d) => targetCountry == d.location) .value() ["items"].filter((d) => d.date >= "2020-03-20"); const maxCases2020 = _.chain(data) .filter((d) => d.date < "2021") .maxBy((d) => +d[casesAttribute]) .value(); const maxCases2022 = _.chain(data) .filter((d) => d.date >= "2022") .maxBy((d) => +d[casesAttribute]) .value(); return html`

	2020	2022
Max cases	${d3.format(",d")(maxCases2020[casesAttribute])}	${d3.format(",d")(maxCases2022[casesAttribute])}
Tests per 100	${d3.format(".1f")(maxCases2020["new_tests_smoothed_per_thousand"])}	${d3.format(".1f")(maxCases2022["new_tests_smoothed_per_thousand"])}
Positive rate	${d3.format(".1%")(maxCases2020["positive_rate"])}	${d3.format(".1%")(maxCases2022["positive_rate"])}

`; }41:T61a,{ const data = _.chain(summaryData) .find((d) => targetCountry == d.location) .value() ["items"].filter((d) => d.date >= "2020-03-20"); const maxDeaths2022 = _.chain(data) .filter((d) => d.date >= "2022") .maxBy((d) => +d["new_deaths_smoothed"]) .value(); //return maxDeaths2022; return Plot.plot({ marginRight: 120, y: { label: "People (daily avg)", type: "log", domain: [0.5, _.max(data.map((d) => +d["new_cases_smoothed"]))] //tickFormat: (d) => d3.format(".2s")(d) }, marks: [ Plot.ruleY([maxDeaths2022 && maxDeaths2022["new_deaths_smoothed"]], { stroke: "grey", strokeDasharray: "1 0 1" }), Plot.line(data, { x: (d) => new Date(d["date"]), y: (d) => +d["new_cases_smoothed"], stroke: "blue" }), Plot.text( data, Plot.selectLast({ x: (d) => new Date(d["date"]), y: (d) => +d["new_cases_smoothed"], dx: 5, text: (d) => "Daily cases (7 day avg)", textAnchor: "start", fill: "blue" }) ), Plot.line(data, { x: (d) => new Date(d["date"]), y: (d) => +d["new_deaths_smoothed"], stroke: "red" }), Plot.text( data, Plot.selectLast({ x: (d) => new Date(d["date"]), y: (d) => +d["new_deaths_smoothed"], dx: 5, text: (d) => "Daily deaths (7 day avg)", textAnchor: "start", fill: "red" }) ) ] }); }42:T1e67,function buildInlineCharts(options) { const data = options && options.data; const reference = options && options.reference; const referenceData = (options && options.referenceData) || []; const metric = (options && options.metric) || null; const headlineMetric = (options && options.headlineMetric) || null; const headlines = (options && options.headlines) || metric; const threshold = options && options.threshold; const weeks = (options && options.weeks) || 4; const colors = (options && options.colors) || ["#FFDC00"]; const referenceValues = (options && options.referenceValues) || []; const referenceDates = (options && options.referenceDates) || []; const referenceLabel = (options && options.referenceLabel) || "Chile"; const yFormat = (options && options.yFormat) || ".3s"; const yLabel = (options && options.yLabel) || "Positividad"; const thresholdColor = (options && options.thresholdColor) || "#ff7b25"; const referenceColor = (options && options.referenceColor) || d3.schemeTableau10[0]; const baseValue = (options && options.baseValue) || 1; const defaultMax = (options && options.defaultMax) || null; const fullWidth = (options && options.fullWidth) || false; const hidePeak = (options && options.hidePeak) || false; debugger; const plotData = data.filter((d) => { return _.chain(d.items) .filter((d) => +d[metric]) .value().length; }); const maxReferenceDate = _.chain(referenceData) .filter((d) => +d[metric]) .map((d) => d["date"]) .max() .value(); const recentReferenceData = _.chain(referenceData) .filter((d) => +d[metric]) .filter( (d) => d.date >= moment(maxReferenceDate).subtract(weeks, "weeks").format("YYYY-MM-DD") ) .value(); const maxReferenceData = _.chain(recentReferenceData) .map((d) => +d[metric]) .max() .value(); function xValue(d) { return d.date; //return new Date(d.date); } const plots = plotData.map((d) => { debugger; const maxDate = _.chain(d.items) .filter((d) => +d[metric]) .last() .value()["date"]; const recentRecords = _.chain(d.items) .filter((d) => +d[metric]) .filter( (d) => d.date >= moment(maxDate).subtract(weeks, "weeks").format("YYYY-MM-DD") ) .filter( (d) => d.date >= moment(maxReferenceDate).subtract(weeks, "weeks").format("YYYY-MM-DD") ) .value(); return { country: d.location, headlineMetric: headlineMetric && _.chain(d.items) .filter((d) => +d[headlineMetric]) .last() .value() && _.chain(d.items) .filter((d) => +d[headlineMetric]) .last() .value()[headlineMetric], headlineMetricDate: headlineMetric && _.chain(d.items) .filter((d) => d[headlineMetric]) .last() .value() && _.chain(d.items) .filter((d) => d[headlineMetric]) .last() .value()["date"], maxValueDate: _.chain(recentRecords) .maxBy((d) => +d[metric]) .value() && _.chain(recentRecords) .maxBy((d) => +d[metric]) .value()["date"], maxDate: maxDate, plots: [null].map((dummy, i) => { const maxValue = _.chain(recentRecords) .map((d) => +d[metric]) .max() .value(); const maxValueDate = _.chain(recentRecords) .maxBy((d) => +d[metric]) .value() && _.chain(recentRecords) .maxBy((d) => +d[metric]) .value()["date"]; const dates = _.chain(recentRecords) .map((d) => d.date) .concat(referenceDates) .value(); const dateExtent = d3.extent(dates); const numDays = moment(dateExtent[1]).diff( moment(dateExtent[0]), "days" ); const dateDomain = _.range(0, numDays + 1).map((i) => moment(dateExtent[0]).add(i, "days").format("YYYY-MM-DD") ); return Plot.plot({ width: 450, height: 350, marginRight: 38, y: { label: yLabel, tickFormat: yFormat, type: "log", zero: false, domain: [ baseValue, _.max([maxValue, maxReferenceData, defaultMax]) * 1.1 ] }, x: { type: "point", domain: dateDomain, tickFormat: (d) => referenceDates.includes(d) ? moment(d).format("D MMM") : "", tickSize: 0, //tickPadding: 0, tickRotate: 0 }, marks: [ Plot.areaY(recentRecords, { x: (d) => xValue(d), y1: (d) => baseValue, y2: (d) => (+d[metric] > baseValue ? +d[metric] : baseValue), fill: (d) => colors[i] }), Plot.areaY(recentRecords, { x: (d) => xValue(d), y1: (d) => (threshold < +d[metric] ? threshold : +d[metric]), y2: (d) => +d[metric], fill: (d) => thresholdColor }), Plot.lineY(recentReferenceData, { x: (d) => xValue(d), y: (d) => +d[metric], stroke: (d) => referenceColor, strokeWidth: 2, strokeDasharray: [1, 0, 1] }), Plot.dot( recentRecords, Plot.selectMaxY({ x: (d) => xValue(d), y: (d) => +d[metric] }) ), Plot.text( recentRecords, Plot.selectMaxY({ x: (d) => xValue(d), y: (d) => +d[metric], dy: -10, text: (d) => d3.format(yFormat)(+d[metric]), fontSize: 14 }) ), Plot.text( maxDate !== maxValueDate ? recentRecords : [], Plot.selectLast({ x: (d) => xValue(d), y: (d) => +d[metric], dx: 20, text: (d) => d3.format(yFormat)(+d[metric]), fontSize: 14 }) ), Plot.text( recentReferenceData, Plot.selectLast({ x: (d) => xValue(d), y: (d) => +d[metric], dx: 20, text: (d) => referenceLabel, fill: referenceColor, fontSize: 14 }) ), Plot.ruleX(referenceDates, { stroke: "grey", strokeOpacity: 0.5 }), Plot.ruleY(referenceValues, { stroke: referenceColor }) ] }); }) }; }); //return plots; return html`

${plots.map( (p) => html`

${p.country}

${p.plots.map((plot) => html`${plot}`)} ${ p.headlineMetric ? `

${d3.format(".1f")( p.headlineMetric )} tests x 1.000 people (${moment(p.headlineMetricDate).format( "D MMM" )})

` : `` }

${ (!hidePeak && (p.maxDate > p.maxValueDate ? `Peak: ${moment(p.maxValueDate).format("D MMM YYYY")} (${moment().diff( moment(p.maxValueDate), "days" )} days ago )` : `No peak observed yet`)) || "" }

` )}

{ const items = _.chain(dateObjects) .values() .filter((d) => +d.new_cases_smoothed_per_million) .each( (d) => (d.new_cases_smoothed_per_100K = +d.new_cases_smoothed_per_million / 10) ) .value(); const record_current = _.chain(items) //.filter((d) => +d.new_cases_smoothed_per_million) .last() .value(); if (!record_current) { debugger; } const date_current = record_current && record_current.date; const date_1weekAgo = moment(date_current) .subtract(1, "week") .format("YYYY-MM-DD"); const date_2weeksAgo = moment(date_current) .subtract(2, "weeks") .format("YYYY-MM-DD"); const date_4weeksAgo = moment(date_current) .subtract(4, "weeks") .format("YYYY-MM-DD"); const record_1weekAgo = dictLocationDate[location][date_1weekAgo]; const record_2weeksAgo = dictLocationDate[location][date_2weeksAgo]; const cases_max4weeks = items.length && +_.chain(items) .filter((d) => d.date >= date_4weeksAgo) .maxBy((d) => +d.new_cases_smoothed_per_million) .value()["new_cases_smoothed_per_million"]; return ( record_current && { location: location, population: +record_current.population, continent: record_current.continent, currentDate: date_current, date_1weekAgo: date_1weekAgo, record_current: record_current, record_1weekAgo: record_1weekAgo, record_2weeksAgo: record_2weeksAgo, cases_1weekAgo: (record_1weekAgo && +record_1weekAgo.new_cases_smoothed_per_million) || 0, cases_current: +record_current.new_cases_smoothed_per_million, change_cases_1week: record_1weekAgo && +record_current.new_cases_smoothed_per_million / +record_1weekAgo.new_cases_smoothed_per_million - 1, cases_max4weeks: cases_max4weeks, items: items } ); }) .filter((d) => d) .value(); }44:T486,countryAlias2 = ({ "Bolivia (Plurinational State of)": "Bolivia", "Moldova, Republic of": "Moldova", "United Kingdom of Great Britain and Northern Ireland": "United Kingdom", "United States of America": "United States", "Korea, Republic of": "South Korea", "Russian Federation": "Russia", "Côte d'Ivoire": "Cote d'Ivoire", "Congo, Democratic Republic of the": "Congo", "Iran (Islamic Republic of)": "Iran", "Palestine, State of": "Palestine", "Lao People's Democratic Republic": "Laos", "Syrian Arab Republic": "Syria", "Taiwan, Province of China": "Taiwan", "Venezuela (Bolivarian Republic of)": "Venezuela", "Viet Nam": "Vietnam", "Tanzania, United Republic of": "Tanzania", "Timor-Leste": "Timor", "Brunei Darussalam": "Brunei", "Cabo Verde": "Cape Verde", "Virgin Islands (British)": "British Virgin Islands", "Bonaire, Sint Eustatius and Saba": "Bonaire Sint Eustatius and Saba", "Falkland Islands (Malvinas)": "Falkland Islands", "Micronesia (Federated States of)": "Micronesia (country)", "Saint Helena, Ascension and Tristan da Cunha": "Saint Helena", Curaçao: "Curacao", "Holy See": "Vatican" })45:[] 31:{"id":"02b3c8dbcab55955","slug":"covid19-recent-worldwide-evolution","title":"COVID19 - recent worldwide evolution","thumbnail":"b21b51f8907614e4f2f1ea7561a960f563093acba3150def696c0fd716c65c6d","access_level":"public","create_time":"$D2022-01-24T23:31:09.676Z","update_time":"$D2022-10-21T20:32:25.959Z","view_time":null,"first_public_time":"$D2022-02-12T02:33:49.471Z","has_unpublished_edits":null,"fork":{"id":"87254cada05d9c88","slug":"paises-que-redujeron-cifras-muy-altas-de-casos-en-2021","title":"Países que redujeron cifras muy altas de casos en 2021","owner":{"id":"0843f5dc42ca68a0","login":"elaval","name":"Ernesto Laval"}},"stars":1,"starred":false,"owner":{"type":"team","id":"0843f5dc42ca68a0","login":"elaval","name":"Ernesto Laval","avatar_url":"https://avatars-next.observableusercontent.com/089f0414fc9025dadb05cc06d7a406bc9be10093187c46729cb4ba3a4098dc30","tier":"starter_2024"},"version":5169,"publish_version":null,"role":null,"authored":false,"authors":[{"type":"individual","id":"e04cd7b5f4351ae9","login":"elaval","name":"Ernesto Laval","avatar_url":"https://avatars-next.observableusercontent.com/089f0414fc9025dadb05cc06d7a406bc9be10093187c46729cb4ba3a4098dc30","tier":null}],"members":[{"type":"individual","id":"e04cd7b5f4351ae9","login":"elaval","name":"Ernesto Laval","avatar_url":"https://avatars-next.observableusercontent.com/089f0414fc9025dadb05cc06d7a406bc9be10093187c46729cb4ba3a4098dc30","tier":null,"role":"editor"}],"comments":[],"custom_thumbnail":true,"trashed":false,"lapsed":null,"subscribed":null,"forks":2,"collection":null,"type":"notebook","body":{"title":"COVID19 - recent worldwide evolution","cells":[{"id":0,"value":"md`# COVID19 - recent worldwide evolution`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":336,"value":"*Data source: Our World in Data*","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":677,"value":"mutable msg = md`## Loading data (this might take some minutes) ...`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2450,"value":"viewof selectedContinent = Inputs.select(\n _.chain(summaryData)\n .map((d) => d.continent)\n .uniq()\n .filter()\n .concat([\"Americas\"])\n\n .sortBy()\n .concat([\"All\"])\n\n .value(),\n { value: \"Americas\", label: \"Select Continent\" }\n)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":5105,"value":"summaryData","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":3741,"value":"viewof selectedReference = {\n function excludedCountry(d) {\n return (\n !showAllCountries_1 &&\n (unReliableReports[d.location] || d.population < 1000000)\n );\n }\n\n const data = _.chain(summaryData)\n .filter((d) => !excludedCountry(d))\n .filter((d) => {\n const lastValidRecord = _.chain(d.items)\n .filter((d) => d[\"positive_rate\"])\n .last()\n .value();\n return (\n lastValidRecord &&\n lastValidRecord.date >=\n moment(new Date()).subtract(8, \"weeks\").format(\"YYYY-MM-DD\")\n );\n })\n .value();\n\n return Inputs.select(\n _.chain(data)\n .map((d) => d.location)\n .uniq()\n .filter()\n .sortBy()\n .value(),\n { value: \"Chile\", label: \"Select reference country\" }\n );\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2660,"value":"

By default I exclude small countries and countries that might have challenges reporting reliable data. Small countries are excluded if they have a population less than ${d3.format(\".1s\")(smallCountriesPopulationThreshold)} people. I consider a country with challenges reporting reliable data if the amount of excess deaths estimated by The Economist is more than ${reliabilityFactor} times the number of reported COVID19 deaths. If you don't want exclusions, click on the following checkbox

","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":2637,"value":"viewof showAllCountries_1 = Inputs.toggle({\n label: \"Show all countries\",\n value: false\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":4695,"value":"$33","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":3733,"value":"

Confirmed cases

New daily confirmed cases per 100K people (7 day average)

Charts with log scale on Y axis

","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":2385,"value":"{\n return buildInlineCharts({\n data: targetData,\n headlineMetric: \"new_tests_smoothed_per_thousand\",\n metric: \"new_cases_smoothed_per_100K\",\n yLabel: \"New cases x 100K\",\n baseValue: 1,\n threshold: 10,\n thresholdColor: d3.schemeTableau10[3],\n\n weeks: numWeeks,\n yFormat: \".1f\",\n colors: [\"lightgrey\"],\n headlines: [],\n referenceData: referenceData,\n referenceLabel: selectedReference,\n referenceDates: referenceDates\n });\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":3796,"value":"

Positive rate

% of COVID-19 tests that are positive (7 day average)

Charts with log scale on Y axis

","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":3392,"value":"{\n return buildInlineCharts({\n data: targetData,\n headlineMetric: \"new_tests_smoothed_per_thousand\",\n metric: \"positive_rate\",\n baseValue: 0.01,\n threshold: 0.1,\n weeks: numWeeks,\n yFormat: \".0%\",\n yLabel: \"Positive rate\",\n colors: [\"lightgrey\"],\n headlines: [],\n thresholdColor: d3.schemeTableau10[1],\n referenceData: referenceData,\n referenceLabel: selectedReference,\n referenceValues: [],\n referenceDates: referenceDates\n });\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":3855,"value":"

Confirmed deaths

Daily deaths per million people (7 day average)

Charts with log scale on Y axis

","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":3556,"value":"{\n return buildInlineCharts({\n data: targetData,\n //headlineMetric: \"new_tests_smoothed_per_thousand\",\n metric: \"new_deaths_smoothed_per_million\",\n baseValue: 0.1,\n threshold: 1,\n //reference: refChile,\n weeks: numWeeks,\n yFormat: \".1f\",\n yLabel: \"Deaths per millionn\",\n thresholdColor: d3.schemeTableau10[2],\n colors: [\"lightgrey\"],\n headlines: [],\n referenceData: referenceData,\n referenceLabel: selectedReference,\n\n referenceValues: [],\n referenceDates: referenceDates\n });\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":4281,"value":"## Focus on a specific country","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":4276,"value":"viewof selectedCountry = Inputs.select(targetData.map((d) => d.location))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":4259,"value":"$34","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":4755,"value":"{\n const plotData = summaryData\n .filter((d) => d.location == \"Portugal\")[0]\n .items.filter((d) => d.date >= \"2021-06-01\");\n\n //return plotData;\n return Plot.plot({\n marginLeft: 100,\n marginRight: 100,\n y: { type: \"linear\" },\n marks: [\n Plot.lineY(\n plotData,\n Plot.windowY({\n k: 28,\n //reduce: \"avg\",\n x: (d) => new Date(d[\"date\"]),\n y: \"weekly_icu_admissions\"\n })\n ),\n Plot.text(\n plotData,\n Plot.selectLast(\n Plot.window({\n k: 28,\n //reduce: \"avg\",\n x: (d) => new Date(d[\"date\"]),\n y: \"weekly_icu_admissions\",\n text: (d) => \"new_deaths\"\n })\n )\n )\n ]\n });\n}","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":4858,"value":"{\n const plotData = summaryData\n .map((d) => d.record_current)\n .filter((d) => d.continent.match(/Europe/) && +d.population > 1000000);\n\n //return plotData;\n return Plot.plot({\n style: { fontSize: 14 },\n width: width,\n height: width * 0.4,\n marginLeft: 50,\n marginTop: 30,\n marginBottom: 200,\n color: { legend: false },\n x: { type: \"band\", tickRotate: 90, label: \"País\" },\n y: {\n label: \"Promedio diario de personas fallecidas por millón de habitantes\"\n //domain: [0, 2000]\n },\n marks: [\n Plot.barY(plotData, {\n x: \"location\",\n y: \"new_deaths_smoothed_per_million\",\n sort: { x: \"y\", reverse: true },\n fill: (d) => (d[\"location\"].match(/Portugal/) ? \"Portugal\" : \"Otro\")\n })\n ]\n });\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":4937,"value":"summaryData","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":4948,"value":"plotData = summaryData\n .map((d) => d.record_current)\n .filter((d) => d && d.continent.match(/Europe/))","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":4908,"value":"{\n const plotData = summaryData\n .map((d) => d.record_current)\n .filter((d) => d && d.continent.match(/Europe/) && +d.population > 1000000);\n\n //return plotData;\n return Plot.plot({\n style: { fontSize: 14 },\n width: width,\n height: width * 0.4,\n marginLeft: 50,\n marginTop: 30,\n marginBottom: 200,\n color: { legend: false },\n x: { type: \"band\", tickRotate: 90, label: \"País\" },\n y: {\n label: \"Promedio diario de casos por 100 mil habitantes\",\n domain: [0, 160]\n },\n marks: [\n Plot.barY(plotData, {\n x: \"location\",\n y: (d) => d[\"new_cases_smoothed_per_million\"] / 10,\n sort: { x: \"y\", reverse: true },\n fill: (d) => (d[\"location\"].match(/Portugal/) ? \"Portugal\" : \"Otro\")\n })\n ]\n });\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":4896,"value":"$35","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":4964,"value":"$36","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":4929,"value":"dataPT = d3.csv(\n \"https://raw.githubusercontent.com/covid19-eu-zh/covid19-eu-data/master/dataset/covid-19-pt.csv\"\n)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":4817,"value":"rollsum = Plot.window({ k: 14, anchor: \"end\", reduce: \"mean\" })","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":5043,"value":"fromDate = \"2022-06-01\"","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":5059,"value":"viewof selectCountry = Inputs.select(\n _.chain(dataRecentGrowth)\n .map((d) => d.location)\n .value(),\n { label: \"Select one\" }\n)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":5014,"value":"$37","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":4815,"value":"$38","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":4366,"value":"$39","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":4465,"value":"$3a","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":4404,"value":"$3b","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":4458,"value":"## Select multiple countries","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":4307,"value":"viewof selectedCountries = {\n const countries = _.chain(targetData)\n .filter((d) => d.continent)\n .map((d) =>\n _.chain(d.items)\n .filter((e) => +e.total_boosters_per_hundred)\n .last()\n .value()\n )\n .filter()\n .map((d) => d.location)\n .sortBy()\n\n .value();\n\n return Inputs.select(countries, {\n multiple: true,\n value: [\n \"Australia\",\n \"Costa Rica\",\n \"Croatia\",\n \"Denmark\",\n \"Hungary\",\n \"Israel\",\n \"New Zealand\",\n \"Norway\",\n \"Peru\",\n \"South Korea\",\n \"Taiwan\",\n \"United States\",\n \"Uruguay\"\n ]\n });\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":4288,"value":"$3c","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":4401,"value":"$3d","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":5108,"value":"$3e","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":5160,"value":"{\n const data = _.chain(targetData)\n .filter((d) => selectedCountries.includes(d.location))\n .map((d) => d.items)\n .flatten()\n .filter((d) => d.date >= \"2022-08-22\")\n\n .value();\n\n const maxDate = _.chain(data)\n .map((d) => d.date)\n .max()\n .value();\n\n return maxDate;\n}","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":5110,"value":"targetData","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":4161,"value":"

COVID19 primary vaccines & boosters

Vaccines adminstered per 100 people

\n","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":3113,"value":"topBooster = {\n const countries = _.chain(targetData)\n .filter((d) => d.continent)\n .map((d) =>\n _.chain(d.items)\n .filter((e) => +e.total_boosters_per_hundred)\n .last()\n .value()\n )\n .filter()\n .sortBy((e) => -e.total_boosters_per_hundred)\n .value();\n\n return html`\n\n\n\n\n\n\n\n${countries.map(\n (d) =>\n `\n \n\n\n`\n)}\n

Country	Fully vaccinated (%)	Booster (%)
${d.location}	\n ${d3.format(\".1%\")(+d.people_fully_vaccinated_per_hundred / 100)}\n	\n ${d3.format(\".1%\")(+d.total_boosters_per_hundred / 100)}\n

\n `;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":4253,"value":"","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":4177,"value":"

Cases vs deaths

Average number of confirmed daily cases and deaths

\n","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":4195,"value":"

Chart with a log scale. Log scale allows to compare the growth of both curves when we have exponential behaviour. The curve slope represents the % at which figures are growing or declining.

","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":3067,"value":"viewof targetCountry = {\n const countries = _.chain(targetData)\n .filter((d) => d.continent)\n //.map((d) => d.location)\n\n .value();\n\n const groups = _.chain(countries)\n .uniqBy((d) => d.location)\n .groupBy((d) => d.continent)\n .value();\n return groupedSelect({\n title: \"Select a country\",\n description:\n \"Country for which to show new cases & deaths curves below (log scale)\",\n groups: _.chain(groups)\n .map((items, label) => ({\n label: label || \"None\",\n options: items.map((d) => d.location)\n }))\n .value()\n });\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":4640,"value":"viewof showMax = Inputs.toggle({ label: \"Show max\" })","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":4607,"value":"

${targetCountry}

","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":4504,"value":"$3f","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":4617,"value":"$40","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":3029,"value":"$41","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":3081,"value":"{\n const items = summaryData.find((d) => d.location == targetCountry)[\"items\"];\n const record = _.chain(items)\n .filter((d) => d.people_fully_vaccinated_per_hundred)\n .last()\n .value();\n //return record;\n\n return html`\n

Most recent data on vaccination per 100 people in ${targetCountry}

${d3.format(\".1%\")(\n +record.people_fully_vaccinated_per_hundred / 100\n )} vaccinated original vaccination protocol\n

\n${d3.format(\".1%\")(+record.total_boosters_per_hundred / 100)} booster dose
\n\n`;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":701,"value":"## Author: @elaval","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":453,"value":"## Data source\nOur World in Data (https://github.com/owid/covid-19-data)\n\nThe Economist Excess Deaths model (https://github.com/TheEconomist/covid-19-the-economist-global-excess-deaths-model)","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":289,"value":"html`