31:T909,Plot.plot({ width: 1000, marginLeft: 40, marginRight: 150, y: { grid: true, label: "Deaths Japan", domain: [-20000, 120000], tickFormat: d3.format("~s") }, x: { domain: [new Date("01/01/2020"), latest_date_in_economist_data] }, color: { domain: ["confirmed", "estimate"], range: ["#36a9e1", "#eb5b25"] }, marks: [ Plot.ruleY([0]), Plot.areaY( ihme_japan_cumulative .filter((d) => d.estimate_low !== null) .filter((d) => d.estimate_high !== null) .filter( (d) => d.date.valueOf() < latest_date_in_economist_data.valueOf() ), { x: "date", y1: "estimate_low", y2: "estimate_high", fill: (d) => "estimate", opacity: estimate_opacity } ), Plot.line( ihme_japan_cumulative.filter( (d) => d.date.valueOf() < latest_date_in_economist_data.valueOf() ), { x: "date", y: "estimate", strokeWidth: (d) => "3", filter: (d) => d.estimate !== null, stroke: (d) => "estimate" } ), Plot.text( ihme_japan_cumulative .filter((d) => d.estimate !== null) .filter( (d) => d.date.valueOf() < latest_date_in_economist_data.valueOf() ) .slice(-1), { x: "date", y: "estimate", text: ["Estimate from IHME"], textAnchor: "start", dx: "0.5em" } ), Plot.line(economist_japan_cumulative, { x: "date", y: "confirmed", strokeWidth: (d) => "3", stroke: (d) => "confirmed" }), Plot.areaY(economist_japan_cumulative, { x: "date", y1: "estimate_low", y2: "estimate_high", filter: (d) => d.estimate_low !== null || d.estimate_high !== null, fill: (d) => "estimate", opacity: estimate_opacity }), Plot.line(economist_japan_cumulative, { x: "date", y: "estimate", strokeWidth: (d) => "3", filter: (d) => d.estimate !== null, stroke: (d) => "estimate" }), Plot.text(economist_japan_cumulative.slice(-1), { x: "date", y: "estimate", filter: (d) => d.estimate !== null, text: ["Estimate from The Economist"], textAnchor: "start", dx: "0.5em" }) ] })32:T907,Plot.plot({ width: 1000, marginLeft: 30, marginRight: 140, y: { grid: true, label: "Deaths India", domain: [0, 8_000_000], tickFormat: d3.format("~s") }, x: { domain: [new Date("01/01/2020"), latest_date_in_economist_data] }, color: { domain: ["confirmed", "estimate"], range: ["#36a9e1", "#eb5b25"] }, marks: [ Plot.ruleY([0]), Plot.areaY( ihme_india_cumulative .filter((d) => d.estimate_low !== null) .filter((d) => d.estimate_high !== null) .filter( (d) => d.date.valueOf() < latest_date_in_economist_data.valueOf() ), { x: "date", y1: "estimate_low", y2: "estimate_high", fill: (d) => "estimate", opacity: estimate_opacity } ), Plot.line( ihme_india_cumulative.filter( (d) => d.date.valueOf() < latest_date_in_economist_data.valueOf() ), { x: "date", y: "estimate", strokeWidth: (d) => "3", filter: (d) => d.estimate !== null, stroke: (d) => "estimate" } ), Plot.text( ihme_india_cumulative .filter((d) => d.estimate !== null) .filter( (d) => d.date.valueOf() < latest_date_in_economist_data.valueOf() ) .slice(-1), { x: "date", y: "estimate", text: ["Estimate from IHME"], textAnchor: "start", dx: "0.5em" } ), Plot.line(economist_india_cumulative, { x: "date", y: "confirmed", strokeWidth: (d) => "3", stroke: (d) => "confirmed" }), Plot.areaY(economist_india_cumulative, { x: "date", y1: "estimate_low", y2: "estimate_high", filter: (d) => d.estimate_low !== null || d.estimate_high !== null, fill: (d) => "estimate", opacity: estimate_opacity }), Plot.line(economist_india_cumulative, { x: "date", y: "estimate", strokeWidth: (d) => "3", filter: (d) => d.estimate !== null, stroke: (d) => "estimate" }), Plot.text(economist_india_cumulative.slice(-1), { x: "date", y: "estimate", filter: (d) => d.estimate !== null, text: ["Estimate from The Economist"], textAnchor: "start", dx: "0.5em" }) ] })33:T52c,economist_world_cumulative = { const data = await d3.csv( "https://raw.githubusercontent.com/TheEconomist/covid-19-the-economist-global-excess-deaths-model/main/output-data/export_world_cumulative.csv", d3.autoType ); const max_date = d3.max(data, (d) => d.date); // 1. Rename the columns // 2. Filter for just the latest date const data_estimate_renamed = data .map((d) => { const { date } = d; const excess = d["cumulative_daily_excess_deaths"]; const estimate = d["cumulative_estimated_daily_excess_deaths"]; const estimate_low = d["cumulative_estimated_daily_excess_deaths_ci_95_bot"]; const estimate_high = d["cumulative_estimated_daily_excess_deaths_ci_95_top"]; return { name: "The Economist model", date, estimate, estimate_low, estimate_high }; }) .filter((d) => d.date.valueOf() === max_date.valueOf()); const data_confirmed_renamed = data .map((d) => { const { date } = d; const confirmed = d["cumulative_daily_covid_deaths"]; return { name: "Confirmed CODIV-19 deaths", date, confirmed }; }) .filter((d) => d.date.valueOf() === max_date.valueOf()); return [...data_estimate_renamed, ...data_confirmed_renamed]; }34:T446,wb_income_groups_per_100k_cumulative = { const data = await d3.csv( "https://raw.githubusercontent.com/TheEconomist/covid-19-the-economist-global-excess-deaths-model/main/output-data/output-by-world-bank-income-group/wb_income_groups_per_100k_cumulative.csv", d3.autoType ); // 1. Rename the columns const data_renamed = data.map((d) => { const { date } = d; const world_bank_income_group = d["World_Bank_income_group"]; const confirmed = d["cumulative_daily_covid_deaths_per_100k"]; const excess = d["cumulative_daily_excess_deaths_per_100k"]; const estimate = d["cumulative_estimated_daily_excess_deaths_per_100k"]; const estimate_low = d["cumulative_estimated_daily_excess_deaths_ci_95_bot_per_100k"]; const estimate_high = d["cumulative_estimated_daily_excess_deaths_ci_95_top_per_100k"]; return { date, world_bank_income_group, confirmed, estimate, estimate_low, estimate_high }; }); return data_renamed.filter( (d) => d.world_bank_income_group !== "Unknown income group" ); }35:T40c,key = () => { const svg_width = 10; const confirmed_line = svg` `; const estimate_square = svg` `; const estimate_line = svg` `; return html`

${confirmed_line} Confirmed COVID-19 deaths ${estimate_line} Excess-deaths estimate ${estimate_square} 95% confidence interval

`; }36:[{"id":"afc58f9294cf0f49b9f994bd220ff8b9a62bc5675a436dc7e3c4d016df0de8604b54e42c78c694f7d3b707e0338e0f3d516e887fd7d91b47da394dcad12e7e76","href":"https://static.observableusercontent.com/files/afc58f9294cf0f49b9f994bd220ff8b9a62bc5675a436dc7e3c4d016df0de8604b54e42c78c694f7d3b707e0338e0f3d516e887fd7d91b47da394dcad12e7e76","name":"ihme-japan-2021.csv","type":"text/csv","lastModified":1642159769390.42,"size":23300},{"id":"8993eea783a82fee6abbce1019ce3c83dd61ce38fc8e5fc7be5513a7df4d74d1ada8285724d466cba7ca262d8a6278aaabf7582aa039dcc93b05350a95ef2fa5","href":"https://static.observableusercontent.com/files/8993eea783a82fee6abbce1019ce3c83dd61ce38fc8e5fc7be5513a7df4d74d1ada8285724d466cba7ca262d8a6278aaabf7582aa039dcc93b05350a95ef2fa5","name":"ihme-japan-2020.csv","type":"text/csv","lastModified":1642159769390.42,"size":15579},{"id":"06f24f6b82d781007c1a8f92a15220be2d433a7b76205ce571200491b03f545c8629117e82420fa6935abc7d95033cde8f69a6cf91d97ca0a7058badf22caf99","href":"https://static.observableusercontent.com/files/06f24f6b82d781007c1a8f92a15220be2d433a7b76205ce571200491b03f545c8629117e82420fa6935abc7d95033cde8f69a6cf91d97ca0a7058badf22caf99","name":"ihme-india-2021@2.csv","type":"text/csv","lastModified":1642177093902.008,"size":26360},{"id":"283e54c7144859b6cd4ad81580c63cb61628d0c1f9a069eb6354567a2949c7cbccb65ab4411eca7aa0d836cbb235566231b9c05e223bc942e6b139cc098806e7","href":"https://static.observableusercontent.com/files/283e54c7144859b6cd4ad81580c63cb61628d0c1f9a069eb6354567a2949c7cbccb65ab4411eca7aa0d836cbb235566231b9c05e223bc942e6b139cc098806e7","name":"ihme-global-2021.csv","type":"text/csv","lastModified":1642160764246.535,"size":29642},{"id":"6dc058fd2ce7344fd77c54dde466f1727594fb775e6fa6d6630ec3090d2deec3d9c4ecb313aefad6b1af03042c8be80dc6e4007ba09b461b3afbff4435c601f5","href":"https://static.observableusercontent.com/files/6dc058fd2ce7344fd77c54dde466f1727594fb775e6fa6d6630ec3090d2deec3d9c4ecb313aefad6b1af03042c8be80dc6e4007ba09b461b3afbff4435c601f5","name":"ihme-india-2020@2.csv","type":"text/csv","lastModified":1642177079475.84,"size":17040}] 2f:{"title":"Nature: The pandemic’s true death toll graphics","cells":[{"id":0,"value":"# *Nature*: The pandemic’s true death toll graphics\n\nThis notebook collects the infographics made for the *Nature* news feature [The pandemic’s true death toll: millions more than official counts](https://www.nature.com/articles/d41586-022-00104-8).\n\nThe data was sourced from Our World In Data, *The Economist*, the World Mortality Dataset and the Institute for Health Metrics and Evaluation.\n","pinned":false,"hidden":false,"mode":"md"},{"id":1494,"value":"---\n\n## GLOBAL TOLL\n\nBy January 2022, there had been 5.5 million official COVID-19 deaths worldwide in the pandemic. But models estimate that there have been between two and four times that number of excess deaths — that is, mortality above what was expected — since the start of 2020.","pinned":false,"hidden":false,"mode":"md"},{"id":1541,"value":"{\n const svg_width = 10;\n\n const estimate_square = svg`\n `;\n\n const estimate_line = svg`\n `;\n\n return html`

${estimate_line} Excess deaths estimate \t ${estimate_square} 95% confidence interval

`;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":1337,"value":"Plot.plot({\n width: 600,\n y: {\n tickFormat: d3.format(\"~s\"),\n label: `Global deaths (millions) to ${latest_date_in_economist_data.toDateString()}`,\n domain: [0, 25_000_000]\n },\n x: {\n label: \"\"\n },\n marks: [\n Plot.barY(combined_world_cumulative, {\n x: \"name\",\n y1: 0,\n y2: \"confirmed\",\n fill: \"#36a9e1\"\n }),\n Plot.barY(combined_world_cumulative, {\n x: \"name\",\n y1: 0,\n y2: \"estimate_high\",\n fill: \"#eb5b25\",\n opacity: 0.3\n }),\n Plot.barY(combined_world_cumulative, {\n x: \"name\",\n y1: \"estimate_low\",\n y2: \"estimate_high\",\n fill: \"#eb5b25\",\n opacity: 0.3\n }),\n Plot.ruleY([0]),\n Plot.tickY(combined_world_cumulative, {\n x: \"name\",\n y: \"estimate\",\n stroke: \"#eb5b25\",\n strokeWidth: 2\n })\n ]\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":1570,"value":"---\n\n## EXCESS DEATHS\n\nThe 116 countries and territories in the World Mortality Dataset have reported 4.1 million COVID-19 deaths so far, but their combined excess mortality is around 1.6 times higher, at 6.5 million. Ten example countries are shown. ","pinned":false,"hidden":false,"mode":"md"},{"id":1577,"value":"{\n const svg_width = 10;\n\n const confirmed_square = svg`\n `;\n\n const estimate_square = svg`\n `;\n\n return html`

${confirmed_square} Reported deaths \t ${estimate_square} Excess deaths

`;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":1357,"value":"Plot.plot({\n marginLeft: 10,\n marginRight: 120,\n x: {\n tickFormat: d3.format(\"~s\"),\n label: \"Deaths\",\n domain: [0, 1_100_000],\n grid: true\n },\n y: {\n label: \"\",\n tickFormat: (d) => \"\"\n },\n color: {\n domain: [\"Confirmed\", \"Estimate\"],\n range: [\"#36a9e1\", \"#eb5b25\"]\n },\n facet: {\n data: wmd_data,\n y: \"name\",\n marginRight: 120,\n label: \"\"\n },\n fy: {\n domain: countries_to_show\n },\n marks: [\n Plot.barX(wmd_data, {\n y: \"type\",\n x1: 0,\n x2: \"deaths\",\n fill: \"type\"\n }),\n Plot.ruleX([0])\n ]\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":650,"value":"---\n\n## RICH AND POOR\n\nOfficial figures suggest that wealthy countries had the highest deaths per capita during the pandemic. But a model that estimates excess deaths suggests that \u0003is false: lower middle-income countries might have been hit hardest.","pinned":false,"hidden":false,"mode":"md"},{"id":1641,"value":"key()","pinned":false,"hidden":false,"mode":"ojs"},{"id":761,"value":"Plot.plot({\n width: 1000,\n marginTop: 50,\n facet: {\n data: wb_income_groups_per_100k_cumulative,\n x: \"world_bank_income_group\",\n label: \"\"\n },\n y: {\n grid: true,\n label: \"Deaths per 100,000 population\",\n domain: [0, 400]\n },\n color: {\n domain: [\"confirmed\", \"estimate\"],\n range: [\"#36a9e1\", \"#eb5b25\"]\n },\n marks: [\n Plot.ruleY([0]),\n Plot.line(wb_income_groups_per_100k_cumulative, {\n x: \"date\",\n y: \"confirmed\",\n strokeWidth: (d) => \"3\",\n stroke: (d) => \"confirmed\"\n }),\n Plot.areaY(wb_income_groups_per_100k_cumulative, {\n x: \"date\",\n y1: \"estimate_low\",\n y2: \"estimate_high\",\n filter: (d) => d.estimate_low !== null || d.estimate_high !== null,\n fill: (d) => \"estimate\",\n opacity: estimate_opacity\n }),\n Plot.line(wb_income_groups_per_100k_cumulative, {\n x: \"date\",\n y: \"estimate\",\n filter: (d) => d.estimate !== null,\n strokeWidth: (d) => \"3\",\n stroke: (d) => \"estimate\"\n })\n ]\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":701,"value":"---\n\n## MODEL DISAGREEMENTS\n\nModels of excess deaths sometimes sharply disagree: the Institute for Health Metrics and Evaluation (IHME) estimates 71,000 excess deaths for Japan, whereas The Economist model puts them at below 10,000. (In this chart, periods of negative excess mortality indicate that overall deaths were below average, even as COVID-19 deaths were being recorded.)","pinned":false,"hidden":false,"mode":"md"},{"id":1658,"value":"key()","pinned":false,"hidden":false,"mode":"ojs"},{"id":715,"value":"$31","pinned":false,"hidden":false,"mode":"ojs"},{"id":1592,"value":"---\n\n## MILLIONS OF MISSING DEATHS\n\nIndia has reported fewer than 500,000 COVID-19 deaths – but both *The Economist* model and household surveys suggest the real pandemic toll is in the millions.","pinned":false,"hidden":false,"mode":"md"},{"id":1613,"value":"key()","pinned":false,"hidden":false,"mode":"ojs"},{"id":1432,"value":"$32","pinned":false,"hidden":false,"mode":"ojs"},{"id":1624,"value":"---\n\n## Data\n","pinned":false,"hidden":false,"mode":"md"},{"id":1512,"value":"---\n\n### Global Toll\n","pinned":false,"hidden":false,"mode":"md"},{"id":1228,"value":"**IHME data** downloaded from [https://www.healthdata.org/covid/data-downloads]() (file 'Reference scenario 2021' 73mb) and processed with [Csvkit](https://csvkit.readthedocs.io/en/latest/tutorial/2_examining_the_data.html) to include only rows relating to 'Global'.\n\n```bash\ncsvcut -c date,location_name,cumulative_deaths,seir_cumulative_mean,seir_cumulative_lower,seir_cumulative_upper data_download_file_reference_2021.csv | \\\ncsvgrep -c location_name -m Global > ihme-global-2021.csv\n```\n\nAs the IHME model contains predictions, the data is cut off at the latest date in *The Economist*'s date, which at present is ${latest_date_in_economist_data.toDateString()}.","pinned":false,"hidden":false,"mode":"md"},{"id":1236,"value":"ihme_world_cumulative = {\n const data = await FileAttachment(\"ihme-global-2021.csv\").csv({\n typed: true\n });\n\n const max_date_from_economist = new Date(\"01/13/2022\");\n\n // 1. Rename the columns\n // 2. Sort by date\n return data\n .map((d) => {\n const { date } = d;\n const estimate = d.seir_cumulative_mean;\n const estimate_low = d.seir_cumulative_lower;\n const estimate_high = d.seir_cumulative_upper;\n\n return {\n name: \"IHME model\",\n date,\n estimate,\n estimate_low,\n estimate_high\n };\n })\n .filter(\n (d) => d.date.valueOf() === latest_date_in_economist_data.valueOf()\n );\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":1694,"value":"** *The Economist* ** data linked to from GitHub - [export_country_cumulative.csv](https://github.com/TheEconomist/covid-19-the-economist-global-excess-deaths-model/blob/main/output-data/export_country_cumulative.csv)","pinned":false,"hidden":false,"mode":"md"},{"id":1208,"value":"$33","pinned":false,"hidden":false,"mode":"ojs"},{"id":1491,"value":"latest_date_in_economist_data = economist_world_cumulative.slice(-1)[0].date","pinned":true,"hidden":false,"mode":"ojs"},{"id":1249,"value":"combined_world_cumulative = [\n ...economist_world_cumulative,\n ...ihme_world_cumulative\n]","pinned":false,"hidden":false,"mode":"ojs"},{"id":1344,"value":"---\n\n### Excess deaths\n","pinned":false,"hidden":false,"mode":"md"},{"id":1701,"value":"**World Mortality Dataset** data linked to from GitHub - [excess-mortality.csv](https://github.com/dkobak/excess-mortality/blob/main/excess-mortality.csv)\n","pinned":false,"hidden":false,"mode":"md"},{"id":1410,"value":"countries_to_show = [\n \"Russia\",\n \"United States\",\n \"Brazil\",\n \"Mexico\",\n \"Iran\",\n \"South Africa\",\n \"Peru\",\n \"Egypt\",\n \"Philippines\",\n \"Italy\"\n]","pinned":false,"hidden":false,"mode":"ojs"},{"id":1347,"value":"wmd_data = {\n const data = await d3.csv(\n \"https://raw.githubusercontent.com/dkobak/excess-mortality/main/excess-mortality.csv\",\n d3.autoType\n );\n\n // // 1. Rename the columns\n // // 2. Filter for just the countries we want to show\n const data_estimate = data\n .map((d) => {\n const name = d[\"Country\"];\n const type = \"Estimate\";\n const deaths = d[\"Excess deaths\"];\n\n return {\n name,\n type,\n deaths\n };\n })\n .filter((d) => countries_to_show.includes(d.name));\n\n const data_confirmed = data\n .map((d) => {\n const name = d[\"Country\"];\n const type = \"Confirmed\";\n const deaths = d[\"COVID-19 deaths\"];\n\n return {\n name,\n type,\n deaths\n };\n })\n .filter((d) => countries_to_show.includes(d.name));\n\n return [...data_confirmed, ...data_estimate];\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":1635,"value":"---\n\n### Rich and poor","pinned":false,"hidden":false,"mode":"md"},{"id":1705,"value":"** *The Economist* ** data linked to from GitHub - [wb_income_groups_per_100k_cumulative.csv](https://github.com/TheEconomist/covid-19-the-economist-global-excess-deaths-model/blob/main/output-data/output-by-world-bank-income-group/wb_income_groups_per_100k_cumulative.csv)","pinned":false,"hidden":false,"mode":"md"},{"id":621,"value":"$34","pinned":false,"hidden":false,"mode":"ojs"},{"id":1148,"value":"---\n\n### Model disagreements\n\n**IHME data** downloaded from [https://www.healthdata.org/covid/data-downloads]() and processed with [Csvkit](https://csvkit.readthedocs.io/en/latest/tutorial/2_examining_the_data.html) to include only rows relating to Japan.\n\nFor 2020:\n\n```bash\ncsvcut -c date,location_name,cumulative_deaths,seir_cumulative_mean,seir_cumulative_lower,seir_cumulative_upper data_download_file_reference_2020.csv | \\\ncsvgrep -c location_name -m Japan > ihme-japan-2020.csv\n```\n\nFor 2021:\n\n```bash\ncsvcut -c date,location_name,cumulative_deaths,seir_cumulative_mean,seir_cumulative_lower,seir_cumulative_upper data_download_file_reference_2021.csv | \\\ncsvgrep -c location_name -m Japan > ihme-japan-2021.csv\n```\n","pinned":false,"hidden":false,"mode":"md"},{"id":707,"value":"ihme_japan_cumulative = {\n const data_2020 = await FileAttachment(\"ihme-japan-2020.csv\").csv({\n typed: true\n });\n const data_2021 = await FileAttachment(\"ihme-japan-2021.csv\").csv({\n typed: true\n });\n\n // 1. Rename the columns\n // 2. Sort by date\n return [...data_2020, ...data_2021].map((d) => {\n const { date } = d;\n const confirmed = d.cumulative_deaths;\n const estimate = d.seir_cumulative_mean;\n const estimate_low = d.seir_cumulative_lower;\n const estimate_high = d.seir_cumulative_upper;\n\n return { date, confirmed, estimate, estimate_low, estimate_high };\n });\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":1710,"value":"***The Economist*** data linked to from GitHub - [export_country_cumulative.csv](https://github.com/TheEconomist/covid-19-the-economist-global-excess-deaths-model/blob/main/output-data/export_country_cumulative.csv)","pinned":false,"hidden":false,"mode":"md"},{"id":732,"value":"economist_japan_cumulative = {\n const data = await d3.csv(\n \"https://raw.githubusercontent.com/TheEconomist/covid-19-the-economist-global-excess-deaths-model/main/output-data/export_country_cumulative.csv\",\n d3.autoType\n );\n\n // 1. Rename the columns\n return data\n .filter((d) => d[\"iso3c\"] === \"JPN\")\n .map((d) => {\n const { date, continent } = d;\n const confirmed = d[\"cumulative_daily_covid_deaths\"];\n const excess = d[\"cumulative_daily_excess_deaths\"];\n const estimate = d[\"cumulative_estimated_daily_excess_deaths\"];\n const estimate_low =\n d[\"cumulative_estimated_daily_excess_deaths_ci_95_bot\"];\n const estimate_high =\n d[\"cumulative_estimated_daily_excess_deaths_ci_95_top\"];\n\n return {\n date,\n confirmed,\n excess,\n estimate,\n estimate_low,\n estimate_high\n };\n });\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":1421,"value":"---\n\n### Millions of missing deaths\n\n**IHME data** downloaded from [https://www.healthdata.org/covid/data-downloads]() and processed with [Csvkit](https://csvkit.readthedocs.io/en/latest/tutorial/2_examining_the_data.html) to include only rows relating to India.\n\nFor 2020:\n\n```bash\ncsvcut -c date,location_name,cumulative_deaths,seir_cumulative_mean,seir_cumulative_lower,seir_cumulative_upper data_download_file_reference_2020.csv | \\\ncsvgrep -c location_name -r \"\\bIndia\\b\" > ihme-india-2020.csv\n```\n\nFor 2021:\n\n```bash\ncsvcut -c date,location_name,cumulative_deaths,seir_cumulative_mean,seir_cumulative_lower,seir_cumulative_upper data_download_file_reference_2021.csv | \\\ncsvgrep -c location_name -r \"\\bIndia\\b\" > ihme-india-2021.csv\n```\n\n","pinned":false,"hidden":false,"mode":"md"},{"id":1426,"value":"ihme_india_cumulative = {\n const data_2020 = await FileAttachment(\"ihme-india-2020@2.csv\").csv({\n typed: true\n });\n const data_2021 = await FileAttachment(\"ihme-india-2021@2.csv\").csv({\n typed: true\n });\n\n // 1. Rename the columns\n // 2. Sort by date\n return [...data_2020, ...data_2021].map((d) => {\n const { date } = d;\n const confirmed = d.cumulative_deaths;\n const estimate = d.seir_cumulative_mean;\n const estimate_low = d.seir_cumulative_lower;\n const estimate_high = d.seir_cumulative_upper;\n\n return { date, confirmed, estimate, estimate_low, estimate_high };\n });\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":1716,"value":"***The Economist*** data linked to from GitHub - [export_country_cumulative.csv](https://github.com/TheEconomist/covid-19-the-economist-global-excess-deaths-model/blob/main/output-data/export_country_cumulative.csv)","pinned":false,"hidden":false,"mode":"md"},{"id":1429,"value":"economist_india_cumulative = {\n const data = await d3.csv(\n \"https://raw.githubusercontent.com/TheEconomist/covid-19-the-economist-global-excess-deaths-model/main/output-data/export_country_cumulative.csv\",\n d3.autoType\n );\n\n // 1. Rename the columns\n return data\n .filter((d) => d[\"iso3c\"] === \"IND\")\n .map((d) => {\n const { date, continent } = d;\n const confirmed = d[\"cumulative_daily_covid_deaths\"];\n const excess = d[\"cumulative_daily_excess_deaths\"];\n const estimate = d[\"cumulative_estimated_daily_excess_deaths\"];\n const estimate_low =\n d[\"cumulative_estimated_daily_excess_deaths_ci_95_bot\"];\n const estimate_high =\n d[\"cumulative_estimated_daily_excess_deaths_ci_95_top\"];\n\n return {\n date,\n confirmed,\n excess,\n estimate,\n estimate_low,\n estimate_high\n };\n });\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":15,"value":"---\n\n## Appendix","pinned":false,"hidden":false,"mode":"md"},{"id":1598,"value":"$35","pinned":false,"hidden":false,"mode":"ojs"},{"id":1605,"value":"estimate_opacity = 0.3","pinned":false,"hidden":false,"mode":"ojs"}],"files":"$W36","stdlib":"1","titling":"auto"}