2d:null 2e:["$","title",null,{"children":"Charting the COVID-19 Spread in Singapore | dianaow | Observable"}] 33:T4c8,md` In the last week of March, a handful of new cases were discovered at foreign worker dormitory clusters. However, on April 5, it became all too clear that __Singapore is facing a serious threat of rapidly growing transmissions within dormitories and at work sites__ as the number of infected from these locations doubled that of the previous day. Since April 10, a shocking number of over 70 unlinked cases is announced each day, despite the start of 'Circuit Breaker' measures. However, inspection of the raw data from MOH shows that many of the new unlinked cases were Blangladeshi or Indian nationals with work permits. Judging from the situation at hand, these serve as possible indicators that they are residents of dormitories that could potentially be announced as new clusters in subsequent days. Hence, considering the two branches that Singapore's outbreak is progressing, I have chosen to segregate the data into "Community" cases (Singaporeans, Singapore PRs and work permit holders not residing in dormitories) and cases from dormitory residents. This enables easier tracking of unlinked cases amongst the community, a metric closely monitored to assess the effectiveness of 'Circuit Breaker' measures. `34:T47d,make_cumsum_chart = (data, width, domain) => { const lines = vl.markArea({ opacity: 0.8, fill: 'black' }) .data(data) .transform( vl.window(vl.sum('cases').as('Cumulative_Sum')) ) .encode( vl.x() .fieldT('date') .timeUnit('utcyearmonthday') .scale({ domain: domain, paddingInner: 0.1, paddingOuter: 0 }) .axis({ tickCount: 'week', format: '%d %B', grid: false, tickSize: 6 }) .title(null), vl.y() .fieldQ('Cumulative_Sum') .scale({ domain: [0,3000], nice: false }) .axis({ tickCount: 4, domain: false, ticks: false, labelAlign: 'left', labelBaseline: 'bottom' }) .title(null), vl.tooltip([ {"field": "date", "type": "temporal"}, {"field": "cases", "type": "quantitative"} ]) ) .width(width) .height(width / 2) .autosize({ type: 'fit-x', contains: 'padding' }) .config({ style: { cell: { stroke: 'transparent' } }, axis: { labelColor: '#929292', gridColor: '#ececec' }, padding: { top: 5, right: 10, bottom: 20, left: 0 }, }) .render(); return lines }35:Td90,make_stackedbar_chart = (data, width, domain) => { const annotations_data = [ { 'start': '2020-01-23', 'end': '2020-02-04', 'event': 'Cases imported from Wuhan', 'y': width / 12 }, { 'start': '2020-02-04', 'end': '2020-03-08', 'event': 'Local tranismissions through clusters', 'y': width / 12 }, { 'start': '2020-03-08', 'end': '2020-03-25', 'event': 'Surge in imported cases', 'y': width / 12 }, { 'start': '2020-03-25', 'end': '2020-04-07', 'event': 'Resurgence of community spread', 'y': width / 12 + 20 }, { 'start': '2020-04-07', 'end': '2020-04-12', 'event': 'Circuit Breaker kicks in', 'y': width / 12 } ] annotations_data.map(d=>{ let start_date = new Date(d.start).getTime() let end_date = new Date(d.end).getTime() let filtered_data = data.filter(el=> new Date(el['date']).getTime() >= start_date & new Date(el['date']).getTime() <= end_date) let total_days = (end_date - start_date)/(1000*60*60*24) let avg = d3.sum(filtered_data, d=>d.cases) / total_days let avgR = Math.ceil(avg) d.average = `${avgR} average cases per day` }) const bars = vl.markBar({ opacity: 1 }) .data(data) .transform( vl.calculate("datum.type == 'Imported' ? datum.cases : -datum.cases").as('type_cases'), ) .encode( vl.x() .fieldT('date') .timeUnit('utcyearmonthday') .scale({ domain: domain, paddingInner: 0.1, paddingOuter: 0 }) .axis({ tickCount: 'week', format: '%d %B', grid: false, tickSize: 6 }) .title(null), vl.y() .fieldQ('type_cases') .scale({ domain: [-400,80], nice: false }) .axis({ tickCount: 4, domain: false, ticks: false, labelAlign: 'left', labelBaseline: 'bottom' }) .title(null), vl.color() .fieldN("type") .scale({ domain: ['Imported', 'Local', 'Local unlinked', 'Local linked'], "range": ["#0D0887", "#CB4779", "#ED7953", "#F2F224"] }) .legend({orient: 'top'}), vl.tooltip([ {"field": "date", "type": "temporal"}, {"field": "type", "type": "ordinal"}, {"field": "cases", "type": "quantitative"} ]) ) const rects = vl.markRule({ stroke: 'black', fillOpacity: 0, strokeOpacity: 0.5 }) .data(annotations_data) .encode( vl.x() .fieldT('start') .timeUnit('utcyearmonthday'), ) const text = vl.markText({ align: 'left', dx: 2, fontSize: 9, fontWeight: 800 }) .data(annotations_data) .encode( vl.text() .field('event'), vl.x() .fieldT('start') .timeUnit('utcyearmonthday'), vl.y() .field('y') ) const text2 = vl.markText({ align: 'left', dx: 2, dy: 10, fontSize: 9, fontWeight: 300 }) .data(annotations_data) .encode( vl.text('') .field('average'), vl.x() .fieldT('start') .timeUnit('utcyearmonthday'), vl.y() .field('y') ) return vl.layer(bars, rects, text, text2) .width(width) .height(width / 2) .autosize({ type: 'fit-x', contains: 'padding' }) .config({ style: { cell: { stroke: 'transparent' } }, axis: { labelColor: '#929292', gridColor: '#ececec' }, padding: { top: 5, right: 10, bottom: 5, left: 0 }, }) .render(); }36:T561,make_stackedbar_chart_1 = (data, width, domain) => { const bars = vl.markBar({ opacity: 1}) .data(data) .encode( vl.column({ spacing: 12}) .fieldO('date') .timeUnit('monthday') //.scale({ domain: domain, paddingInner: 0.1, paddingOuter: 0 }) //.axis({ tickCount: 'week', format: '%d %B', grid: false, tickSize: 6 }) .title(null), vl.y() .fieldQ('cases') .scale({ domain: [0, 400], nice: false }) .axis({ tickCount: 4, domain: false, ticks: false, labelAlign: 'left', labelBaseline: 'bottom' }) .title(null), vl.color() .fieldN("type") .scale({ domain: ['Imported', 'Dormitory residents', 'Community unlinked', 'Community linked'], "range": ["#0D0887", "black", "#ED7953", "#F2F224"] }) .legend({orient: 'top'}), vl.x() .fieldO("type") .title(null), vl.tooltip([ {"field": "date", "type": "temporal"}, {"field": "type", "type": "ordinal"}, {"field": "cases", "type": "quantitative"} ]) ) return bars .width({step:10}) .height(width / 2) .config({ style: { cell: { stroke: 'transparent' } }, axis: { labelColor: '#929292', gridColor: '#ececec' }, padding: { top: 5, right: 10, bottom: 5, left: 0 }, }) .render(); }37:T96f,make_multiplelinehover_chart = (data, width, domain) => { const hover = vl.selectSingle() .encodings('x') // limit selection to x-axis value .on('mouseover') // select on mouseover events .nearest(true) // select data point nearest the cursor .empty('none'); // empty selection includes no data points const lines = vl.markLine({ opacity: 0.8 }) .data(data) .encode( vl.x() .fieldT('date') .timeUnit('utcyearmonthday') .scale({ domain: domain, paddingInner: 0.1, paddingOuter: 0 }) .axis({ tickCount: 'week', format: '%d %B', grid: false, tickSize: 6 }) .title(null), vl.y() .fieldQ('value') .scale({ domain: [0,300], nice: false }) .axis({ tickCount: 4, domain: false, ticks: false, labelAlign: 'left', labelBaseline: 'bottom' }) .title(null), vl.color() .fieldN("type") .scale({ domain: ['5-day moving average', 'Percentage change'], "range": ["red", "black"] }) .legend({orient: 'top'}), vl.tooltip([ {"field": "date", "type": "temporal"} ]) ) // mark properties for text label layers const label = {align: 'left', dx: 5, dy: -5}; const white = {stroke: 'white', strokeWidth: 2}; // shared base for new layers, filtered to hover selection const base = lines.transform(vl.filter(hover)); const linesHover = vl.layer( lines, // add a rule mark to serve as a guide line vl.markRule({color: '#aaa'}) .transform(vl.filter(hover)) .encode(vl.x().fieldT('date')), // add circle marks for selected time points, hide unselected points lines.markCircle() .select(hover) // use as anchor points for selection .encode(vl.opacity().if(hover, vl.value(1)).value(0)), // add white stroked text to provide a legible background for labels base.markText(label, white).encode(vl.text().fieldQ('value')), // add text labels for stock prices base.markText(label).encode(vl.text().fieldQ('value')) ) .width(width) .height(width / 2) .autosize({ type: 'fit-x', contains: 'padding' }) .config({ style: { cell: { stroke: 'transparent' } }, axis: { labelColor: '#929292', gridColor: '#ececec' }, padding: { top: 5, right: 10, bottom: 5, left: 0 }, }) .render(); return linesHover }38:Tf68,make_cluster_chart = (data, width, domain) => { const areaTrellis = vl.markBar({ opacity: 0.7, fill: 'black', stroke: 'black' }) .data(data) .transform( vl.filter("datum.cluster !== '' & datum.cluster !== 'S11 Dormitory'"), vl.joinaggregate(vl.sum('cases').as('total_cases')).groupby('cluster'), vl.filter('datum.total_cases > 10') ) .encode( vl.x() .fieldT('date') .timeUnit('utcyearmonthday') .scale({ domain: domain, paddingInner: 0, paddingOuter: 0 }) .axis({ tickCount: 'week', format: '%d %B', grid: false, tickSize: 6 }) .title(null), vl.y() .fieldQ('cases') .scale({ domain: [0,12], nice: true }) .axis({ tickCount: 4, domain: false, ticks: false, labelPadding: 6, labelAlign: 'left', labelBaseline: 'middle'}) .title(null), vl.row({ spacing: -2 }) .fieldN('cluster') .sort(vl.field('total_cases').order('descending')) .header({ labelAlign: 'left', labelBaseline: 'middle', labelAngle: 0, labelLimit: 120 }) .title(null), vl.tooltip([ {"field": "date", "type": "temporal"}, {"field": "cluster", "type": "nominal"}, {"field": "cases", "type": "quantitative"} ]) ) .width(width) .height(50) const S11cases = data.filter(d=>d.cluster === 'S11 Dormitory') const maxCase = d3.max(S11cases, d=>d.cases) const maxDate = S11cases.find(d=>d.cases === maxCase).date const toHighlight = [{date: maxDate, cases: maxCase}] const sumCase = d3.sum(S11cases, d=>d.cases) const areaSingle = vl.markBar({ opacity: 0.7, fill: 'black', stroke: 'black' }) .data(data) .transform( vl.filter("datum.cluster === 'S11 Dormitory'") ) .encode( vl.x() .fieldT('date') .timeUnit('utcyearmonthday') .scale({ domain: domain, paddingInner: 0, paddingOuter: 0 }) .axis({ tickCount: 'week', format: '%d %B', grid: false, tickSize: 6 }) .title(null), vl.y() .fieldQ('cases') .scale({ domain: [0,180], nice: true }) .axis({ tickCount: 4, domain: false, ticks: false, labelPadding: 6, labelAlign: 'left', labelBaseline: 'middle' }) .title(null), vl.tooltip([ {"field": "date", "type": "temporal"}, {"field": "cluster", "type": "nominal"}, {"field": "cases", "type": "quantitative"} ]) ) const rule = vl.markRule({ color: 'red' }) .data(toHighlight) .encode( vl.x() .fieldT('date') .timeUnit('utcyearmonthday') .axis({ tickCount: 'week', format: '%d %B', grid: false, tickSize: 6 }) .title(null), vl.y() .scale({ domain: [0,180], nice: true }) .fieldQ('cases'), vl.size({value: 2}) ) const text = vl.markText({ align: 'left', fontSize: 9, fontWeight: 800, fill: 'red', dx: 2, dy: -8 }) .data(toHighlight) .encode( vl.x() .fieldT('date') .timeUnit('utcyearmonthday') .axis({ tickCount: 'week', format: '%d %B', grid: false, tickSize: 6 }) .title(null), vl.y() .scale({ domain: [0,100], nice: true }) .fieldQ('cases'), vl.text() .fieldQ('cases') ) const areaSingleLayered = vl.layer(areaSingle, rule, text) .width(width) .height(150) .title({text: "S11 Dormitory", anchor: "start"}) return vl.vconcat(areaSingleLayered, areaTrellis) .config({ style: { cell: { stroke: 'transparent' } }, axis: { labelColor: '#929292', gridColor: '#ececec' }, padding: { top: 5, right: 10, bottom: 25, left: 0 }, }) .render(); }39:T41d,make_bar_chart = (data, width, domain) => { const bars = vl.markBar({ opacity: 0.8, fill: 'black' }) .data(data) .transform( vl.window(vl.rank().as('Rank')).sort(vl.field('cases').order('descending')), vl.filter('datum.Rank <= 11') ) .encode( vl.x() .fieldQ('cases') .scale({ domain: [0,1000], paddingInner: 0.1, paddingOuter: 0 }) .axis({ tickCount: 10, grid: false, tickSize: 6 }) .title(null), vl.y() .fieldO('citizenship') .sort(vl.field('cases').order('descending')) .title(null), vl.tooltip([ {"field": "citizenship", "type": "ordinal"}, {"field": "cases", "type": "quantitative"} ]) ) .width(width) .height(width / 2) .autosize({ type: 'fit-x', contains: 'padding' }) .config({ style: { cell: { stroke: 'transparent' } }, axis: { labelColor: '#929292', gridColor: '#ececec' }, padding: { top: 5, right: 0, bottom: 20, left: 0 }, }) .render(); return bars }3a:T41e,/** * returns an array with moving average of the input array * @param array - the input array * @param count - the number of elements to include in the moving average calculation * @param qualifier - an optional function that will be called on each * value to determine whether it should be used */ function movingAvg(array, count, qualifier){ // calculate average for subarray var avg = function(array, qualifier){ var sum = 0, count = 0, val; for (var i in array){ val = array[i]; if (!qualifier || qualifier(val)){ sum += val; count++; } } return sum / count; }; var result = [], val; // pad beginning of result with null values for (var i=0; i < count-1; i++) result.push(null); // calculate average for each subarray and add to result for (var i=0, len=array.length - count; i <= len; i++){ val = avg(array.slice(i, i + count), qualifier); if (isNaN(val)) result.push(null); else result.push(val); } return result; }3b:[{"id":"af36294823c11a5ad352e177aba341c519e9a11df07efa5756ef6d7894a3d0dc24aa332c7e77c493812f3eb36dedec24fb8140273c9d9557ef47c568de1bd291","href":"https://static.observableusercontent.com/files/af36294823c11a5ad352e177aba341c519e9a11df07efa5756ef6d7894a3d0dc24aa332c7e77c493812f3eb36dedec24fb8140273c9d9557ef47c568de1bd291","name":"groupDateType.json","type":"application/json","lastModified":1587135568325.743,"size":7246},{"id":"b11ab5e35fa0b015aa7f17a78f1e585b11f295ca524612804fa635143a5ddc956a5ab858fdc7c79af2aed31e908ab407bcd97d903e07a0d3db13a15741cb4c72","href":"https://static.observableusercontent.com/files/b11ab5e35fa0b015aa7f17a78f1e585b11f295ca524612804fa635143a5ddc956a5ab858fdc7c79af2aed31e908ab407bcd97d903e07a0d3db13a15741cb4c72","name":"groupDailyCount.json","type":"application/json","lastModified":1587135568307.482,"size":2507},{"id":"3a00c52e7d87bfc528f900691d7365cdf0a2ac52990aab02c63584c85d8ef03a3022a883053434f76c23f75303871a42837ecd8d068627159511d802aab5dc01","href":"https://static.observableusercontent.com/files/3a00c52e7d87bfc528f900691d7365cdf0a2ac52990aab02c63584c85d8ef03a3022a883053434f76c23f75303871a42837ecd8d068627159511d802aab5dc01","name":"groupNationality.json","type":"application/json","lastModified":1587135568289.209,"size":1702},{"id":"5820313fc1f21d3738cca5897604b572f657e0570a4b423073fadb27be935537022a30db188f6f021b88d9e08440733037494562460b36fa823f17526146ef3b","href":"https://static.observableusercontent.com/files/5820313fc1f21d3738cca5897604b572f657e0570a4b423073fadb27be935537022a30db188f6f021b88d9e08440733037494562460b36fa823f17526146ef3b","name":"groupDateCluster.json","type":"application/json","lastModified":1587135568655.593,"size":20559},{"id":"a52c36275b18fed78ee2ff235ed70774572581d8afe8991d7190e6cef82da8c75a93b591bc0c27ede32ef2a6b3a55de5784c03c7a77d7f7706baa1cc4ff27797","href":"https://static.observableusercontent.com/files/a52c36275b18fed78ee2ff235ed70774572581d8afe8991d7190e6cef82da8c75a93b591bc0c27ede32ef2a6b3a55de5784c03c7a77d7f7706baa1cc4ff27797","name":"groupDateNationality@4.json","type":"application/json","lastModified":1587207942809.346,"size":3564}] 31:{"id":"ea4b3e1378c63577","slug":"covid-19-cases-in-singapore","title":"Charting the COVID-19 Spread in Singapore","thumbnail":"0754ae07c478cc80089dd21ee4dd3fc710026b0ba665cb2ffb491cdf7a48ff85","access_level":"public","create_time":"$D2020-04-11T02:46:24.291Z","update_time":"$D2020-07-13T02:52:46.027Z","view_time":null,"first_public_time":"$D2020-04-11T10:34:15.752Z","fork":false,"stars":0,"starred":false,"owner":{"type":"team","id":"9565060d73fcbf2d","login":"dianaow","name":"","avatar_url":"https://avatars-next.observableusercontent.com/bb37a5423d45df1bdef69047e6b5572c6bd8c31c0112ced9f5c7f80f9aa9fd8e","tier":"starter_2024"},"version":1373,"publish_version":1373,"role":null,"authored":false,"authors":[{"type":"individual","id":"f9d57064cf004ad0","login":"dianaow","name":"","avatar_url":"https://avatars-next.observableusercontent.com/bb37a5423d45df1bdef69047e6b5572c6bd8c31c0112ced9f5c7f80f9aa9fd8e","tier":null}],"members":[{"type":"individual","id":"f9d57064cf004ad0","login":"dianaow","name":"","avatar_url":"https://avatars-next.observableusercontent.com/bb37a5423d45df1bdef69047e6b5572c6bd8c31c0112ced9f5c7f80f9aa9fd8e","tier":null,"role":"editor"}],"comments":[],"custom_thumbnail":false,"trashed":false,"lapsed":null,"subscribed":null,"forks":0,"collection":null,"type":"notebook","body":{"title":"Charting the COVID-19 Spread in Singapore","cells":[{"id":0,"value":"md`# Charting the COVID-19 Spread in Singapore`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":631,"value":"md`\nThe charts below use figures from a national dataset of every confirmed case since January 24 when the country’s first case was reported.\n\nThe data tracks confirmed cases by gender, age, location, source of infection and other information published in case reports and official press releases from Singapore's Ministry of Health.\n`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":804,"value":"html`

Confirmed cases, cumulative count

\n ${await make_cumsum_chart(groupDailyCount, width, date_extent)}\n`;","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":830,"value":"html`

As of ${format_1(date_extent[1])}, the Ministry of Health has reported 2532 confirmed cases of COVID-19 in Singapore, including 10 deaths.

`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":637,"value":"md` \nBut the number of confirmed cases is only one metric to assess Singapore's progress in the fight against COVID-19. Another important metric is the growth rate.\n\nOne way of tracking growth is by looking at the daily percentage change in total confirmed cases.\n\nThe daily growth factor is calculated by taking today’s new reported cases and dividing it by yesterday’s new cases. Because these numbers are volatile, a five-day moving average of new cases will be used in the calculations.\n\nSingapore’s growth rate has alarmingly shot up from a stable rate of 3% in February and early March to consistently exceeding 40% in late March, based on the five-day moving average.\n`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":676,"value":"html`

Daily percentage change in total confirmed cases

\n ${await make_multiplelinehover_chart(growth_factors, width, date_extent)}\n`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":819,"value":"md`\nIt can be observed that there are distinct phases in Singapore's growth trajectory of COVID-19 cases. The first locally transmitted case was announced on Feb 4, 2020. Since then small clusters of local transmission have emerged, but the outbreak was still kept under control. As COVID-19 spread globally, imported cases in Singapore surged due to a spike in travellers returning to Singapore. However, even as the number of imported cases tapered in early April, locally transmitted cases, particularly unlinked cases, rose sharply.\n\nAs the upward trend continued, a 'Circuit Breaker' consisting of tougher movement restrictions and social distancing rules was announced on April 3 and came into effect on April 7.\n`\n","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":56,"value":"html`

New confirmed cases, daily count

\n Breakdown of linked vs unlinked locally transmitted cases only tracked from Mar 24, 2020\n

\n ${await make_stackedbar_chart(groupDateType, width, date_extent)}\n`;","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1346,"value":"$33","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1301,"value":"html`

Parallel Outbreaks

\n ${await make_stackedbar_chart_1(groupDateNationality, width, date_extent)}\n`;","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":645,"value":"md` \nFor weeks since the first reported case of COVID-19, Singapore managed to keep its numbers low and trackable, with only small, easily contained clusters. However by mid-March, new cases were steadily rising with the emergence of bigger clusters such as the SAFRA Jurong cluster and PCF Fengshan cluster. Since the beginning of April, Singapore has been struck by a multitude of rapidly growing clusters occurring in foreign worker dormitories.\n\nAs of April 12, there are a total of 12 clusters originating from foreign worker dormitories or their worksites. The S11 Dormitory is currently the largest cluster, with a record 159 cases announced on April 9.\n\n*Only clusters with 10 or more infections are visualized below.* \n`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":596,"value":"html`

Growth of local clusters

\n ${await make_cluster_chart(groupDateCluster, 800, date_extent)}\n`;","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1009,"value":"html`

As of ${format_1(date_extent[1])}, the number of Bangladeshis infected is ${perc}% of the total number of infected Singaporean citizens and Singapore Permanent Residents combined.

`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":887,"value":"md`\nThe spread of COVID-19 through foreign worker dormitories is alarming and it remains to be seen whether current efforts to mitigate the spread will be effective or when the number of infections originating not only from dormitories, but nationwide, will peak in the months to come.\n\n*Only the top 10 nationalities with people infected in Singapore are visualized below.*\n`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":902,"value":"html`

Cases per nationality

\n ${await make_bar_chart(groupNationality, width, date_extent)}\n`;","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":256,"value":"md`## Appendix`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":775,"value":"$34","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":231,"value":"$35","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1262,"value":"$36","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":735,"value":"$37","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1097,"value":"$38","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":895,"value":"$39","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":655,"value":"$3a","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":45,"value":"date_extent = {\n const extent = d3.extent(groupDailyCount, d => d['date']).map(d => new Date(d));\n extent[0].setDate(extent[0].getDate() - 2);\n extent[1].setDate(extent[1].getDate());\n\n return extent;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":998,"value":"perc = {\n const sgp = groupNationality.find(d=>d.citizenship == 'Singapore Citizen' || d.citizenship == 'Singapore Permanent Resident').cases\n const b = groupNationality.find(d=>d.citizenship == 'Bangladesh').cases\n const num = (b / sgp) * 100\n return Math.round((num + Number.EPSILON) * 100) / 100\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":661,"value":"growth_factors = {\n const casesPerDay = groupDailyCount.map(d=>d.cases)\n const result = movingAvg(casesPerDay, 5, function(val){ return val != 0; })\n let growth_factors = []\n result.map((d,i) =>{\n growth_factors.push({\n date: groupDailyCount[i].date,\n value: d,\n type: '5-day moving average'\n\n })\n })\n casesPerDay.map((d,i) =>{\n growth_factors.push({\n date: groupDailyCount[i].date,\n value: d,\n type: 'Percentage change'\n })\n })\n \n return growth_factors\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1199,"value":"groupNationality = await FileAttachment(\"groupNationality.json\").json()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":17,"value":"groupDailyCount = await FileAttachment(\"groupDailyCount.json\").json()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1191,"value":"groupDateCluster = await FileAttachment(\"groupDateCluster.json\").json()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1195,"value":"groupDateType = await FileAttachment(\"groupDateType.json\").json()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1268,"value":"groupDateNationality = await FileAttachment(\"groupDateNationality@4.json\").json()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":274,"value":"format_1 = d3.timeFormat(\"%b %d, %Y\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":4,"value":"import {vl} from '@vega/vega-lite-api'","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":36,"value":"d3 = require(\"d3@5\", \"d3-array@2\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"}],"files":"$W3b","stdlib":"1","titling":"auto"}} 32:"$31:body"