31:T557,usaMap = { const svg = d3.create("svg").attr("viewBox", [0, 0, 975, 610]); const path = d3.geoPath(); svg .append("g") .attr("transform", "translate(550,0)") .append(() => legend({ color: colorScale, title: "% of detected COVID-19 cases sequenced in past 30 days*" }) ); svg .append("g") .selectAll("path") .data(topojson.feature(us, us.objects.states).features) .join("path") .style("fill", (d) => { const { id } = d; let value = 0; const [state] = map_data.filter( (d) => parseFloat(d.id, 10) === parseFloat(id, 10) ); if (typeof state === "object") { value = parseFloat(state.value, 10); } return colorScale(value); }) .attr("d", path) .append("title") .text((d) => { const { id } = d; console.log(id); let value = "Title"; const [state] = map_data.filter( (d) => parseFloat(d.id, 10) === parseFloat(id, 10) ); if (typeof state === "object") { value = state["Name"] + " " + state["value"]; } return value; }); svg .append("path") .datum(topojson.mesh(us, us.objects.states, (a, b) => a !== b)) .attr("fill", "none") .attr("stroke", "white") .attr("stroke-linejoin", "round") .attr("d", path); return svg.node(); }32:Ta0f,data_from_flourish = [ { name: "Alabama", value: "1.6" }, { name: "Alaska", value: "5.6" }, { name: "Arizona", value: "3.8" }, { name: "Arkansas", value: "2.0" }, { name: "California", value: "5.7" }, { name: "Colorado", value: "12.0" }, { name: "Connecticut", value: "4.8" }, { name: "Delaware", value: "4.2" }, { name: "District of Columbia", value: "8.0" }, { name: "Florida", value: "2.8" }, { name: "Georgia", value: "2.1" }, { name: "Hawaii", value: "7.6" }, { name: "Idaho", value: "4.2" }, { name: "Illinois", value: "2.5" }, { name: "Indiana", value: "2.4" }, { name: "Iowa", value: "1.9" }, { name: "Kansas", value: "2.8" }, { name: "Kentucky", value: "1.9" }, { name: "Louisiana", value: "2.4" }, { name: "Maine", value: "8.3" }, { name: "Maryland", value: "6.2" }, { name: "Massachusetts", value: "8.4" }, { name: "Michigan", value: "3.4" }, { name: "Minnesota", value: "8.4" }, { name: "Mississippi", value: "1.6" }, { name: "Missouri", value: "1.8" }, { name: "Montana", value: "4.4" }, { name: "Nebraska", value: "3.9" }, { name: "Nevada", value: "5.3" }, { name: "New Hampshire", value: "3.8" }, { name: "New Jersey", value: "2.8" }, { name: "New Mexico", value: "5.7" }, { name: "New York", value: "3.4" }, { name: "North Carolina", value: "3.0" }, { name: "North Dakota", value: "5.3" }, { name: "Ohio", value: "1.5" }, { name: "Oklahoma", value: "0.5" }, { name: "Oregon", value: "6.6" }, { name: "Pennsylvania", value: "2.0" }, { name: "Rhode Island", value: "5.0" }, { name: "South Carolina", value: "1.7" }, { name: "South Dakota", value: "1.7" }, { name: "Tennessee", value: "1.8" }, { name: "Texas", value: "3.2" }, { name: "Utah", value: "7.7" }, { name: "Vermont", value: "19.5" }, { name: "Virginia", value: "2.4" }, { name: "Washington", value: "8.7" }, { name: "West Virginia", value: "6.0" }, { name: "Wisconsin", value: "3.7" }, { name: "Wyoming", value: "20.9" } ].map((d) => { return { ...d, value: parseFloat(d.value, 10) }; })33:[{"id":"08f6391c7c157cbbec69246e86814997931b3cf1a450ebfe11974b5541259fd89444b3e5ecfac311c17b60da9489e2ead43cd6113dca9de580499f95ccaa0e39","href":"https://static.observableusercontent.com/files/08f6391c7c157cbbec69246e86814997931b3cf1a450ebfe11974b5541259fd89444b3e5ecfac311c17b60da9489e2ead43cd6113dca9de580499f95ccaa0e39","name":"Omicron sequencing timeline viz dataset for Nature updated 12_9_2021 12pm EST.csv","type":"text/csv","lastModified":1645809303421.263,"size":3757},{"id":"2c711afcca5cbf0bd333c13a99e592a3197af7bd0ef67b08b29db3273d55142923341b02604ae2e290ad5d591bc0211801f3910afb897b59acd49c2c8bbb3984","href":"https://static.observableusercontent.com/files/2c711afcca5cbf0bd333c13a99e592a3197af7bd0ef67b08b29db3273d55142923341b02604ae2e290ad5d591bc0211801f3910afb897b59acd49c2c8bbb3984","name":"us-state-ids.csv","type":"text/csv","lastModified":1645811548417.301,"size":716}] 2f:{"title":"Nature: Omicron blindspots - Graphics","cells":[{"id":0,"value":"# *Nature*: Omicron blindspots - Graphics\n\nThis notebook collects the infographics made for the Nature news article [Omicron blindspots: why it’s hard to track coronavirus variants](https://www.nature.com/articles/d41586-021-03698-7).","pinned":false,"hidden":false,"mode":"md"},{"id":92,"value":"---\n\n## Sequence of events\n\nWeeks can pass between when a SARS-CoV-2 coronavirus sample is collected from a person and when the genome sequence of the virus is submitted to a database, such as the one at the GISAID initiative. This makes tracking the spread of the Omicron variant tricky. Here, the earliest cases of Omicron in 43 locations are plotted on the basis of sample collection date, as of 9 December.","pinned":false,"hidden":false,"mode":"md"},{"id":46,"value":"{\n const svg_width = 16;\n\n const sample_circle = svg`\n `;\n\n const sumbitted_circle = svg`\n `;\n\n return html`\n

${sample_circle}\n

Earliest sample collected

${sumbitted_circle}

Sequence submitted to GISAID

`;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":14,"value":"Plot.plot({\n marginLeft: 150,\n y: {\n label: null,\n domain: d3\n .sort(sequence_of_events_data, (d) => d[\"Collection date\"])\n .map((d) => d[\"Country\"])\n },\n x: {\n grid: true,\n label: \"2021\"\n },\n color: {\n legend: true\n },\n marks: [\n Plot.link(sequence_of_events_data, {\n x1: \"Collection date\",\n x2: \"Submission date\",\n y1: \"Country\",\n y2: \"Country\",\n stroke: \"Region\",\n strokeWidth: 3\n }),\n Plot.dot(sequence_of_events_data, {\n x: \"Collection date\",\n y: \"Country\",\n fill: \"#fff\",\n stroke: \"Region\",\n strokeWidth: 3,\n r: 5\n }),\n Plot.dot(sequence_of_events_data, {\n x: \"Submission date\",\n y: \"Country\",\n fill: \"Region\",\n stroke: \"Region\",\n strokeWidth: 3,\n r: 5\n })\n ]\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":88,"value":"Sources: GISAID and the Pandemic Prevention Institute at the Rockefeller Foundation","pinned":false,"hidden":false,"mode":"md"},{"id":121,"value":"---\n\n## States of surveillance\n\nThe percentage of laboratory-sequenced coronavirus genomes varies widely across the United States. Researchers recommend sampling at least 5% of samples in order to detect Omicron or other emerging variants.","pinned":false,"hidden":false,"mode":"md"},{"id":149,"value":"$31","pinned":false,"hidden":false,"mode":"ojs"},{"id":154,"value":"*Data as of 13 December\n\nSources: GISAID and the Pandemic Prevention Institute at the Rockefeller Foundation","pinned":false,"hidden":false,"mode":"md"},{"id":99,"value":"---\n## Data","pinned":false,"hidden":false,"mode":"md"},{"id":108,"value":"### Sequence of events","pinned":false,"hidden":false,"mode":"md"},{"id":11,"value":"sequence_of_events_data = FileAttachment(\n \"Omicron sequencing timeline viz dataset for Nature updated 12_9_2021 12pm EST.csv\"\n).csv({ typed: true })","pinned":false,"hidden":false,"mode":"ojs"},{"id":116,"value":"### States of surveillance","pinned":false,"hidden":false,"mode":"md"},{"id":128,"value":"Data from https://flo.uri.sh/visualisation/8062091/embed?auto=1\n\nIn order to copy the data from this map to the clipboard, so that it can be pasted into Observable - run this code in the javascript console:\n\n```\nlet data = _Flourish_data.choropleth.map(d => {\n return { name: d.name, value: d.value }\n});\n\ncopy(data);\n```","pinned":false,"hidden":false,"mode":"md"},{"id":132,"value":"$32","pinned":false,"hidden":false,"mode":"ojs"},{"id":135,"value":"We need to combine this data, which contains a state name an a value, with the [numeric ID code](https://en.wikipedia.org/wiki/List_of_U.S._state_and_territory_abbreviations) for each state so that it can be mapped.","pinned":false,"hidden":false,"mode":"md"},{"id":113,"value":"us_state_ids = FileAttachment(\"us-state-ids.csv\").csv()","pinned":false,"hidden":false,"mode":"ojs"},{"id":139,"value":"map_data = data_from_flourish.map((d) => {\n let { name, value } = d;\n let { id } = us_state_ids.filter((d) => d.name === name)[0];\n return { name, value, id };\n})","pinned":true,"hidden":false,"mode":"ojs"},{"id":141,"value":"us = d3.json(\"https://unpkg.com/us-atlas@1/us/10m.json\")","pinned":true,"hidden":false,"mode":"ojs"},{"id":143,"value":"topo_data = topojson.feature(us, us.objects[\"states\"]).features","pinned":true,"hidden":false,"mode":"ojs"},{"id":146,"value":"colorScale = d3.scaleThreshold(\n [0, 2, 5, 10, 20], // Approximate natural breaks\n [\"#fdc28c\", \"#fdc28c\", \"#f67b52\", \"#ce2619\", \"#7f0000\", \"#000000\"].reverse()\n)","pinned":true,"hidden":false,"mode":"ojs"},{"id":151,"value":"import { legend } from \"@d3/color-legend\"","pinned":true,"hidden":false,"mode":"ojs"}],"files":"$W33","stdlib":"1","titling":"auto"}