31:T4ba,Plot.plot({ marks: [ Plot.frame(), Plot.dot(dataPlot, { x: d => d.gdp_per_capita / 1e3, y: d => d.total_vaccinations / 1e6, stroke: "gray", fill: "people_fully_vaccinated_per_hundred", r: (resp * width) / 200, title: d => `${d.location} \n${ d.people_fully_vaccinated_per_hundred }% fully vaccinated, population in millions:${d.population / 1e6}, New cases per million:${d.new_cases_per_million} \n` }), Plot.text(dataPlot, { x: d => d.gdp_per_capita / 1e3, y: d => d.total_vaccinations / 1e6, text: "location", dx: (resp * width) / 50, dy: (-resp * width) / 200 }) ], y: { nice: true, type: "log", label: "Total vaccinations administered in millions", labelAnchor: "center" }, x: { label: "GDP Per Capita in thousand international $", labelAnchor: "center", nice: true }, style: { fontSize: (resp * width) / 50, overflow: "visible" }, marginBottom: (resp * width) / 15, marginLeft: (resp * width) / 10, marginRight: (resp * width) / 15, width, grid: true, color: { scheme: "Greens", domain: [0, 50] } })32:T4cb,Plot.plot({ marks: [ Plot.frame(), Plot.dot(dataPlot, { y: d => (d.dosesSecuredInMillions * 1e6) / d.population, x: d => d.gdp_per_capita / 1e3, stroke: "gray", fill: d => d.population / 1e6, r: (resp * width) / 200, title: d => `${d.location} \n${ d.people_fully_vaccinated_per_hundred }% fully vaccinated, population in millions :${d.population / 1e6}, New cases per million:${d.new_cases_per_million} \n` }), Plot.text(dataPlot, { y: d => (d.dosesSecuredInMillions * 1e6) / d.population, x: d => d.gdp_per_capita / 1e3, text: "location", dx: (resp * width) / 50, dy: (-resp * width) / 200 }) ], y: { nice: true, label: "Doses Secured/Person as of 07/07/2021", labelAnchor: "center" }, x: { label: "GDP Per Capita in thousand international $", labelAnchor: "center", nice: true }, style: { fontSize: (resp * width) / 50, overflow: "visible" }, marginBottom: (resp * width) / 15, marginLeft: (resp * width) / 10, marginRight: (resp * width) / 15, width, grid: true, color: { scheme: "Greens", domain: [50, 1000], type: "log" } })33:T497,Plot.plot({ marks: [ Plot.frame(), Plot.dot(dataPlot, { x: d => d.gdp_per_capita / 1e3, y: "people_fully_vaccinated_per_hundred", stroke: "gray", fill: "new_cases_per_million", r: (resp * width) / 200, title: d => `${d.location} \n${ d.people_fully_vaccinated_per_hundred }% fully vaccinated, population in millions :${d.population / 1e6}, New cases per million:${d.new_cases_per_million} \n` }), Plot.text(dataPlot, { x: d => d.gdp_per_capita / 1e3, y: "people_fully_vaccinated_per_hundred", text: "location", dx: (resp * width) / 50, dy: (-resp * width) / 200 }) ], y: { nice: true, label: "Percent Population Fully Vaccinated", labelAnchor: "center" }, x: { label: "GDP Per Capita in thousand international $", labelAnchor: "center", nice: true }, style: { fontSize: (resp * width) / 50, overflow: "visible" }, marginBottom: (resp * width) / 15, marginLeft: (resp * width) / 10, marginRight: (resp * width) / 15, width, grid: true, color: { scheme: "Blues", domain: [0, 400] } })34:T6d6,Plot.plot({ marks: [ Plot.frame(), countriesForTimePlot.map(d => { return Plot.dot(getTimeDataByCountry(dataPlotTime, d, "2021-03-01"), { x: d => new Date(d.date), y: "people_fully_vaccinated_per_hundred", fill: "new_cases_per_million", r: (resp * width) / 300, title: e => `${d} \n${e.people_fully_vaccinated_per_hundred}% fully vaccinated, date:${e.date}, New cases per million:${e.new_cases_per_million} \n` }); }), countriesForTimePlot.map(d => { return Plot.dot(dataPlot.filter(e => e.location == d), { x: d => new Date(d.last_updated_date), y: "people_fully_vaccinated_per_hundred", fill: "new_cases_per_million", r: (resp * width) / 300, title: e => `${d} \n${e.people_fully_vaccinated_per_hundred}% fully vaccinated, date:${e.last_updated_date}, New cases per million:${e.new_cases_per_million} \n` }); }), countriesForTimePlot.map(d => { return Plot.text(dataPlot.filter(e => e.location == d), { x: d => new Date(d.last_updated_date), y: "people_fully_vaccinated_per_hundred", text: "location", dx: (resp * width) / 50, dy: 0 }); }) ], y: { nice: true, label: "Percent population fully vaccinated", labelAnchor: "center", grid: true, ticks: 10 }, x: { label: "Date", labelAnchor: "center", nice: true, ticks: 5 }, style: { fontSize: (resp * width) / 50, overflow: "visible" }, marginBottom: (resp * width) / 15, marginLeft: (resp * width) / 10, marginRight: (resp * width) / 15, width, height: resp * width, color: { scheme: "Blues", domain: [0, 400] } })35:T6da,Plot.plot({ marks: [ Plot.frame(), countriesForTimePlot.map(d => { return Plot.dot(getTimeDataByCountry(dataPlotTime, d, "2021-03-01"), { x: d => new Date(d.date), y: "people_fully_vaccinated_per_hundred", fill: "new_deaths_per_million", r: (resp * width) / 300, title: e => `${d} \n${e.people_fully_vaccinated_per_hundred}% fully vaccinated, date:${e.date}, New deaths per million:${e.new_deaths_per_million} \n` }); }), countriesForTimePlot.map(d => { return Plot.dot(dataPlot.filter(e => e.location == d), { x: d => new Date(d.last_updated_date), y: "people_fully_vaccinated_per_hundred", fill: "new_deaths_per_million", r: (resp * width) / 300, title: e => `${d} \n${e.people_fully_vaccinated_per_hundred}% fully vaccinated, date:${e.last_updated_date}, New deaths per million:${e.new_deaths_per_million} \n` }); }), countriesForTimePlot.map(d => { return Plot.text(dataPlot.filter(e => e.location == d), { x: d => new Date(d.last_updated_date), y: "people_fully_vaccinated_per_hundred", text: "location", dx: (resp * width) / 50, dy: 0 }); }) ], y: { nice: true, label: "Percent population fully vaccinated", labelAnchor: "center", grid: true, ticks: 10 }, x: { label: "Date", labelAnchor: "center", nice: true, ticks: 5 }, style: { fontSize: (resp * width) / 50, overflow: "visible" }, marginBottom: (resp * width) / 15, marginLeft: (resp * width) / 10, marginRight: (resp * width) / 15, width, height: resp * width, color: { scheme: "Reds", domain: [0, 10] } })36:T43b,dosesSecured = [ { location: "United States", dosesSecuredInMillions: 3360 }, { location: "India", dosesSecuredInMillions: 1196 }, { location: "United Kingdom", dosesSecuredInMillions: 629 }, { location: "Canada", dosesSecuredInMillions: 610 }, { location: "Brazil", dosesSecuredInMillions: 608 }, { location: "Indonesia", dosesSecuredInMillions: 431 }, { location: "Japan", dosesSecuredInMillions: 364 }, { location: "Turkey", dosesSecuredInMillions: 270 }, { location: "Mexico", dosesSecuredInMillions: 205 }, { location: "South Korea", dosesSecuredInMillions: 172 }, { location: "Chile", dosesSecuredInMillions: 80 }, { location: "Italy", dosesSecuredInMillions: 70 }, { location: "Morocco", dosesSecuredInMillions: 66 }, { location: "Argentina", dosesSecuredInMillions: 62 }, { location: "Colombia", dosesSecuredInMillions: 50 }, { location: "Germany", dosesSecuredInMillions: 44 } //{ location: "European Commission", dosesSecuredInMillions: 3919 } ] // data from https://www.unicef.org/supply/covid-19-vaccine-market-dashboard as of 07/07/202137:[{"id":"cb22f78bfc59b4028a037c86e1a74ed3a9a08c964f468be438bc3214374f9aa17aa6f8f4e50449c8a22cf4dd209845adb2a50ea8e609400f521639b2f796f7bc","href":"https://static.observableusercontent.com/files/cb22f78bfc59b4028a037c86e1a74ed3a9a08c964f468be438bc3214374f9aa17aa6f8f4e50449c8a22cf4dd209845adb2a50ea8e609400f521639b2f796f7bc","name":"data.json","type":"application/json","lastModified":1625840928657.239,"size":372239},{"id":"f9cd0be91f5c0fe193547452ac17d78caaa01416759f5944179f06cbc0e5cd5248c090dc5869c523576afeee639331f505b1bff592ded287381d5e556fdc24de","href":"https://static.observableusercontent.com/files/f9cd0be91f5c0fe193547452ac17d78caaa01416759f5944179f06cbc0e5cd5248c090dc5869c523576afeee639331f505b1bff592ded287381d5e556fdc24de","name":"dataPlotTime.json","type":"application/json","lastModified":1625841374761.308,"size":7714214}] 2f:{"title":"Exploring vaccination data with Plot","cells":[{"id":0,"value":"md`# Exploring vaccination data with Plot`","pinned":false,"hidden":false,"mode":"ojs"},{"id":416,"value":"md` I recently came across [a nice resource](https://github.com/owid/covid-19-data/tree/master/public/data) for open covid-19 data maintained by [Our World In Data](https://ourworldindata.org/coronavirus). (See their recent article in [Nature](https://www.nature.com/articles/s41562-021-01122-8) introducing this dataset). \n\nI filtered it down to **20 nations with the highest total vaccinations** and using the very handy [Plot](https://observablehq.com/collection/@observablehq/plot) library, was able to find some trends/outliers that are interesting to me (and were not already obvious to me from the news I follow).\n\nIf you enjoy sleuthing, I encourage you to play with [the datasets](https://github.com/owid/covid-19-data/tree/master/public/data). There is a lot to correlate and discover!\n\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":773,"value":"md`*Note1: The data from China is, unfortunately, largely incomplete in this dataset and has therefore been excluded from the plots below.*\n\n*Note2: Comments and Plots are based on data downloaded on July 09, 2021. Use the selection below to switch to live data - comments may no longer apply*`","pinned":false,"hidden":false,"mode":"ojs"},{"id":1087,"value":"viewof live = Inputs.select(\n new Map([[\"Data downloaded on July 09 2021\", 0], [\"Latest Data\", 1]]),\n { value: 0 }\n)","pinned":false,"hidden":false,"mode":"ojs"},{"id":1040,"value":"md`###

India has administered more COVID vaccines than the United States?!`","pinned":false,"hidden":false,"mode":"ojs"},{"id":36,"value":"$31","pinned":false,"hidden":false,"mode":"ojs"},{"id":203,"value":"rampLinear(d3.interpolateGreens, [0, 50], \"Percent Population Fully Vaccinated\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":283,"value":"md`I often come across articles discussing the percentage of population vaccinated for different countries, but what if we also look at the total vaccinations administered?\n\nThe United States and India stand out clearly on this plot (notice the log scale), but they are on two extremes when it comes to national wealth(X axis). Even though India has administered more vaccines than the US(Y axis), it has much further to go than the US before most of its population is vaccinated(fill color). A similar relationship is seen between Brazil and the United Kingdom or between Mexico and Canada. \n\nAll nations must race against the spread of COVID to vaccinate their populations, but the economic and logistical burden on less-wealthy nations with large populations must be substantially greater. \n

`","pinned":false,"hidden":false,"mode":"ojs"},{"id":1046,"value":"md`###

Canada has secured enough vaccine for more than 16 doses/person?!`","pinned":false,"hidden":false,"mode":"ojs"},{"id":700,"value":"$32","pinned":false,"hidden":false,"mode":"ojs"},{"id":721,"value":"rampLog(d3.interpolateGreens, [50, 1000], \"Population in Millions\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":714,"value":"md`I had seen articles about vaccine inequity, but still wasn't prepared for this number. It seems a few nations have, based on vaccine supply agreements, secured enough doses to vaccinate more than 4 times(!) their population (Y axis).\n\nVaccine supply data shown here is from the [Unicef Vaccine Market Dashboard](https://www.unicef.org/supply/covid-19-vaccine-market-dashboard#) (I wish they made the data more easily available through an API). \n\nNote that although Germany and Italy have secured <2 doses/person, the European Commission(not on this graph) has additionally secured 8.7 doses per person. `","pinned":false,"hidden":false,"mode":"ojs"},{"id":584,"value":"md`###

Vaccination rates and new cases - What's happening in Chile?\n\nI had assumed that nations that have vaccinated a large percentage of their population will automatically have fewer new cases per million. Why then, do Chile or the United Kingdom have more new cases than South Korea?\n\n

`","pinned":false,"hidden":false,"mode":"ojs"},{"id":843,"value":"rampLinear(d3.interpolateBlues, [0, 400], \"New Cases Per Million\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":293,"value":"$33","pinned":false,"hidden":false,"mode":"ojs"},{"id":855,"value":"md`
A closer look at a few of these countries (top and bottom four from the graph above) over time is even more interesting. \n* Even though Chile has been vaccinating its population at a high rate for the last few months and has close to 60% of its population fully vaccinated, it still has a high number of daily cases per million throughout. \n\n* The recent high case numbers in the UK are likely the effect of the delta variant. \n\n* South Korea and Argentina have similar percentages of their population vaccinated, but Argentina has almost 20 times higher daily cases per million.\n\n* Based on this graph, the ban imposed by some countries on travellers from India seems very hard to justify. \n\n* Anything else that catches your eye? Try choosing other countries to plot`","pinned":false,"hidden":false,"mode":"ojs"},{"id":878,"value":"viewof countriesForTimePlot = Inputs.select(countries, {\n multiple: true,\n label: \"Choose countries to plot\",\n value: [\n \"Chile\",\n \"United States\",\n \"United Kingdom\",\n \"Spain\",\n \"India\",\n \"Indonesia\",\n \"Argentina\",\n \"South Korea\",\n \"Colombia\"\n ]\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":889,"value":"rampLinear(d3.interpolateBlues, [0, 400], \"New Cases Per Million\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":587,"value":"$34","pinned":false,"hidden":false,"mode":"ojs"},{"id":1181,"value":"md`

The delta variant may be causing a spike in new cases in the UK, but it doesn't seem to have affected the new deaths per million. The South American countries of Chile, Argentina, and Colombia are much more strongly affected. \n\nIt would be interesting to correlate with factors like dominant COVID variant, and vaccine efficacy by nation to understand these differences better.`","pinned":false,"hidden":false,"mode":"ojs"},{"id":1169,"value":"rampLinear(d3.interpolateReds, [0, 10], \"New Deaths Per Million\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":1164,"value":"$35","pinned":false,"hidden":false,"mode":"ojs"},{"id":918,"value":"md` Other fun artifacts: \n* The stair-stepping on data from Chile - I guess Chile either does not vaccinate or report vaccinations during the weekend.\n* The peak in cases every Monday on data from Spain - People in Spain either prefer to get tested on Monday or all tests done over the weekend are reported on Monday.\n* What else do you see?
`","pinned":false,"hidden":false,"mode":"ojs"},{"id":740,"value":"md`------`","pinned":false,"hidden":false,"mode":"ojs"},{"id":1057,"value":"md` Downloaded OWID data are from [here](https://raw.githubusercontent.com/owid/covid-19-data/master/public/data/latest/owid-covid-latest.json) and [here](https://raw.githubusercontent.com/owid/covid-19-data/master/public/data/owid-covid-data.json) as of July 09, 2021\n\nVaccine market data was downloaded from [Unicef](https://www.unicef.org/supply/covid-19-vaccine-market-dashboard) on 07/07/2021`","pinned":false,"hidden":false,"mode":"ojs"},{"id":1156,"value":"Inputs.table(Object.values(data))","pinned":false,"hidden":false,"mode":"ojs"},{"id":7,"value":"data = live == 0 ? FileAttachment(\"data.json\").json() : dataLive","pinned":false,"hidden":false,"mode":"ojs"},{"id":674,"value":"$36","pinned":false,"hidden":false,"mode":"ojs"},{"id":353,"value":"dataPlotTime = live == 0\n ? FileAttachment(\"dataPlotTime.json\").json()\n : dataPlotTimeLive","pinned":false,"hidden":false,"mode":"ojs"},{"id":259,"value":"dataFullLive = fetch(\n 'https://raw.githubusercontent.com/owid/covid-19-data/master/public/data/owid-covid-data.json'\n)\n .then(resp => resp.json())\n .then(d => Object.values(d))","pinned":false,"hidden":false,"mode":"ojs"},{"id":1114,"value":"dataPlotTimeLive = dataPlot.map(d =>\n dataFullLive.find(e => e.location == d.location)\n)","pinned":false,"hidden":false,"mode":"ojs"},{"id":1095,"value":"dataLive = fetch(\n 'https://raw.githubusercontent.com/owid/covid-19-data/master/public/data/latest/owid-covid-latest.json'\n).then(resp => resp.json())","pinned":false,"hidden":false,"mode":"ojs"},{"id":983,"value":"resp = {\n return width > 500 ? 1 : 2;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":682,"value":"dataWithDosesSecured = dataPlot.map(d => {\n var doseData = dosesSecured.find(e => e.location == d.location);\n\n doseData !== undefined\n ? (d.dosesSecuredInMillions = doseData.dosesSecuredInMillions)\n : (d.dosesSecuredInMillions = undefined);\n return d;\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":88,"value":"dataPlot = Object.values(data)\n .sort(function(a, b) {\n return b.total_vaccinations - a.total_vaccinations;\n })\n .filter(d => d.continent !== null && d.location !== \"China\")\n .slice(0, 20)","pinned":false,"hidden":false,"mode":"ojs"},{"id":201,"value":"import { rampLinear } from \"@ananya-roy/labeled-color-bar\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":719,"value":"import { rampLog } from \"@ananya-roy/labeled-color-bar\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":225,"value":"import { addTooltips } from \"@mkfreeman/plot-tooltip\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":641,"value":"countries = dataPlot.map(d => d.location)","pinned":false,"hidden":false,"mode":"ojs"},{"id":330,"value":"function stringToDate(str) {\n const yymmdd = str.split(\"-\");\n return new Date(yymmdd[0], yymmdd[1] - 1, yymmdd[2]);\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":610,"value":"function getTimeDataByCountry(allData, country, startDate) {\n return allData\n .filter(d => d.location == country)[0]\n .data.filter(d => d.date > startDate);\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":34,"value":"Plot = require(\"@observablehq/plot@0.1.0/dist/plot.umd.min.js\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":1198,"value":"width","pinned":true,"hidden":false,"mode":"ojs"}],"files":"$W37","stdlib":"1","titling":"auto"}