31:T401,md`

Data

CDC

CDC's Data is central to this study, such as CDC's Provisional COVID-19 Death Counts by Sex, Age, and State. New datasets for COVID Death Counts are emerging all the time ... CDC's data resources come with the excelent feature of being delivered in the very comprehensive and scalable SOCRATA platform.

Census

The American Community Survey provides additional demographic data. They require an API key ... tsk tsk. I'm thinking of setting a service to proxy it from our domains, please comment Praful and Erica.

`32:T792,ranges = { let div = DOM.element('div'); vars.forEach(k => { let trace = { x: [], y: [], type: 'bar' }; let li = DOM.element('li'); li.innerHTML = `

${k}

`; let labels = [...new Set(noon.map(x => x[k]))].sort((a, b) => { if (a.match(/^\d+/g)) { a = parseInt(a.match(/^\d+/g)[0]); b = parseInt(b.match(/^\d+/g)[0]); } return a > b ? 1 : -1; }); console.log(labels); labels.forEach(v => { li.innerHTML += '
' + v; trace.x.push(v); let count = noon .filter(x => x[k] == v) .map(x => parseInt(x['allcause'])) .reduce((a, b) => a + b); //console.log(k, v, count); trace.y.push(count); }); let layout = { title: `${k}, All Cause
${Date()}` }; let plotDiv = DOM.element('div'); div.appendChild(plotDiv); Plotly.newPlot(plotDiv, [trace], layout); div.appendChild(li); // multi cause plot let causePlot = DOM.element('div'); div.appendChild(causePlot); let traces = []; newCauses.forEach(c => { let trace = { x: [], y: [], type: 'scatter', name: c }; labels.forEach(v => { //li.innerHTML += '
' + v; trace.x.push(v); let count = noon .filter(x => x[k] == v) .map(x => parseInt(x[c])) .reduce((a, b) => a + b); //console.log(k, v, count); trace.y.push(count); }); traces.push(trace); }); Plotly.newPlot(causePlot, traces, { title: `${k}, 2020 weeks 0-${ usa2020.length }
${Date()}`, //autosize: false, width: 950, height: 500, yaxis: { title: 'weekly mortality', type: 'log' //range: [2, Math.log10(2100000)] } }); }); return div; }33:T7e0,{ let div = DOM.element('div'); let h = `

2020 Data
as of ${Date()}

`; h += ``; let cc = causes; //.sort(); let traceNew = { x: [], y: [], type: 'bar', name: 'new (mwk9-wnfr)' }; let traceRef = { x: [], y: [], type: 'bar', name: 'ref (muzy-jte6)' }; causes.forEach((c, i) => { switch (c) { case 'allcause': c = 'all_cause'; break; case 'naturalcause': c = 'natural_cause'; break; case 'influenza_and_pneumonia_j09': c = 'influenza_and_pneumonia_j09_j18'; break; case 'covid_19_u071_multiple_cause': c = 'covid_19_u071_multiple_cause_of_death'; break; case 'covid_19_u071_underlying': c = 'covid_19_u071_underlying_cause_of_death'; break; } traceNew.x.push(c); traceRef.x.push(c); let li = DOM.element('li'); li.innerHTML = cc[i]; //div.appendChild(li); let countRef = usa2020.map(x => parseFloat(x[c])).reduce((a, b) => a + b); traceRef.y.push(countRef); let countNew = noon.map(x => parseFloat(x[cc[i]])).reduce((a, b) => a + b); traceNew.y.push(countNew); h += ``; //li.innerHTML += `: ${count}`; }); h += '

Ref variable name	Ref data (muzy-jte6, week 1-${usa2020.length})	New data (mwk9-wnfr, week 1-${noon[0].mmwrweek})	New variable name
${c}	${countRef}	${countNew}	${cc[i]}

'; div.innerHTML = h; let plotDiv = DOM.element('div'); div.appendChild(plotDiv); let layout = { title: `2020, weeks 0-${ usa2020.length }
${Date()}`, yaxis: { title: 'weekly mortality', type: 'log', range: [2, Math.log10(2100000)] }, xaxis: { tickangle: 30 } }; Plotly.newPlot(plotDiv, [traceNew, traceRef], layout); return div; }34:[{"id":"480130bdc8047871ec4afe02d48131734d6a6918769f1291878e53b9483f80c0c2e471cddb2df36bd49c91e0ad4dcf7dc827f96c7398200d5a5e1b47dfa9b749","href":"https://static.observableusercontent.com/files/480130bdc8047871ec4afe02d48131734d6a6918769f1291878e53b9483f80c0c2e471cddb2df36bd49c91e0ad4dcf7dc827f96c7398200d5a5e1b47dfa9b749","name":"All States dataLayers.json","type":"application/json","lastModified":1595606450476.939,"size":22142}] 2f:{"title":"COVID19Demographics","cells":[{"id":0,"value":"md`\n

COVID19^Demographics

Support libraries at github.com/episphere/covid-demographics. Live stand alone application is served at episphere.github.io/covid-demographics.

Abstract

Literate programming for real-time statistics of COVID demographics.

\n\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":82,"value":"md`\n

Results

This first set of results is defined by the manuscript under development.

\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":90,"value":"md`\n

Excess deaths

Excess deaths were estimated as the difference between 5 year average in the number of weekly deaths from the week of the first recorded COVID-19 deaths (March 3rd 2020) to the week ending May 26th 2020

\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":92,"value":"/*\nfetch(\n 'http://localhost:8000/mortalitytracker/localStorage/excessTotalMortality.json'\n)\n*/","pinned":true,"hidden":false,"mode":"ojs"},{"id":85,"value":"md`\n

Age

\n`","pinned":true,"hidden":false,"mode":"ojs"},{"id":59,"value":"$31","pinned":false,"hidden":false,"mode":"ojs"},{"id":73,"value":"md`

Catalog

Shortlist of datasets used by teh libraries:\n

CDC COVID-19 Death Counts by Sex, Age, and State.
CDC Deaths involving coronavirus disease 2019 (COVID-19) by race and Hispanic origin group and age, by state.
CENSUS American Community Survey 5-Year Data (2009-2018).
list of data sources collected by the National Institute of Environmental Health Science (NIEHS) PVI Tool - thank you!
...

\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":106,"value":"md`

Noon

First evaluation due by noon

`","pinned":false,"hidden":false,"mode":"ojs"},{"id":109,"value":"noon = (await fetch('https://data.cdc.gov/resource/mwk9-wnfr.json')).json()","pinned":false,"hidden":false,"mode":"ojs"},{"id":367,"value":"newCauses = [\n \"allcause\",\n \"naturalcause\",\n \"septicemia_a40_a41\",\n \"malignant_neoplasms_c00_c97\",\n \"diabetes_mellitus_e10_e14\",\n \"alzheimer_disease_g30\",\n \"influenza_and_pneumonia_j09\",\n \"chronic_lower_respiratory\",\n \"other_diseases_of_respiratory\",\n \"nephritis_nephrotic_syndrome\",\n \"symptoms_signs_and_abnormal\",\n \"diseases_of_heart_i00_i09\",\n \"cerebrovascular_diseases\",\n \"covid_19_u071_multiple_cause\",\n \"covid_19_u071_underlying\"\n]","pinned":true,"hidden":false,"mode":"ojs"},{"id":112,"value":"weeks = [...new Set(noon.map(x => `${x.year}, ${x.mmwrweek}`))].length // only one !","pinned":false,"hidden":false,"mode":"ojs"},{"id":147,"value":"md`

all reccords describe the first 28 weeks of 2020:\n
analysis_period_start_date: 2020-01-01T00:00:00.000,\n
analysis_period_end_date: 2020-07-11T00:00:00.000\n

`","pinned":false,"hidden":false,"mode":"ojs"},{"id":125,"value":"md`

Variables

No state or other geographic locator or time (all reccords are nationwide for the 28th week of 2020). Variables are

`","pinned":false,"hidden":false,"mode":"ojs"},{"id":132,"value":"vars = ['sex', 'race_ethnicity', 'age_group']","pinned":true,"hidden":false,"mode":"ojs"},{"id":134,"value":"$32","pinned":false,"hidden":false,"mode":"ojs"},{"id":163,"value":"md`

QAQC

checking that numbers add up as per Amy's comment.

`","pinned":true,"hidden":false,"mode":"ojs"},{"id":167,"value":"causes = [\n \"allcause\",\n \"naturalcause\",\n \"septicemia_a40_a41\",\n \"malignant_neoplasms_c00_c97\",\n \"diabetes_mellitus_e10_e14\",\n \"alzheimer_disease_g30\",\n \"influenza_and_pneumonia_j09\",\n \"chronic_lower_respiratory\",\n \"other_diseases_of_respiratory\",\n \"nephritis_nephrotic_syndrome\",\n \"symptoms_signs_and_abnormal\",\n \"diseases_of_heart_i00_i09\",\n \"cerebrovascular_diseases\",\n \"covid_19_u071_multiple_cause\",\n \"covid_19_u071_underlying\"\n]","pinned":false,"hidden":false,"mode":"ojs"},{"id":197,"value":"shortCauses = {\n return {\n allcause: \"All Cause ₂₀₂₀\",\n naturalcause: \"NaturalCause\",\n septicemia_a40_a41: \"Septicemia\",\n malignant_neoplasms_c00_c97: \"Cancer\",\n diabetes_mellitus_e10_e14: \"Diabetes\",\n alzheimer_disease_g30: \"Alzheimer\",\n influenza_and_pneumonia_j09: \"Influ&Pneum\",\n chronic_lower_respiratory: \"Chron.resp\",\n other_diseases_of_respiratory: \"Other resp\",\n nephritis_nephrotic_syndrome: \"Kidney\",\n symptoms_signs_and_abnormal: \"Unclassified\",\n diseases_of_heart_i00_i09: \"Heart diseas\",\n cerebrovascular_diseases: \"Cereb Vasc\",\n covid_19_u071_multiple_cause: \"with covid\",\n covid_19_u071_underlying: \"of covid\"\n };\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":184,"value":"md`

Total counts from new dataset (mwk9-wnfr), week 0-${noon[0].mmwrweek}:

`","pinned":false,"hidden":false,"mode":"ojs"},{"id":174,"value":"{\n let div = DOM.element('div');\n causes.forEach(c => {\n let li = DOM.element('li');\n li.innerHTML = c;\n div.appendChild(li);\n let count = noon.map(x => parseFloat(x[c])).reduce((a, b) => a + b);\n li.innerHTML += `: ${count}`;\n });\n return div;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":238,"value":"md`

Total Counts from ref dataset (muzy-jte6), week 1-${usa2020.length}:

`","pinned":false,"hidden":false,"mode":"ojs"},{"id":240,"value":"{\n let div = DOM.element('div');\n causes.forEach(c => {\n switch (c) {\n case 'allcause':\n c = 'all_cause';\n break;\n case 'naturalcause':\n c = 'natural_cause';\n break;\n case 'influenza_and_pneumonia_j09':\n c = 'influenza_and_pneumonia_j09_j18';\n break;\n case 'covid_19_u071_multiple_cause':\n c = 'covid_19_u071_multiple_cause_of_death';\n break;\n case 'covid_19_u071_underlying':\n c = 'covid_19_u071_underlying_cause_of_death';\n break;\n }\n let li = DOM.element('li');\n li.innerHTML = c;\n div.appendChild(li);\n console.log(c);\n let count = usa2020.map(x => parseFloat(x[c])).reduce((a, b) => a + b);\n li.innerHTML += `: ${count}`;\n });\n return div;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":283,"value":"md`

Tabulating the two counts above

`","pinned":false,"hidden":false,"mode":"ojs"},{"id":286,"value":"$33","pinned":false,"hidden":false,"mode":"ojs"},{"id":189,"value":"md`

Mortality Tracker

Counts for the same # weeks from mortality tracker

`","pinned":false,"hidden":false,"mode":"ojs"},{"id":215,"value":"{\n let div = DOM.element('div');\n causes.forEach(c => {\n let li = DOM.element('li');\n li.innerHTML = c;\n div.appendChild(li);\n let ref = refMortality.traces.filter(x => x.name == [shortCauses[c]]);\n let count = NaN;\n if (ref.length > 0) {\n //count = noon.map(x => parseFloat(x[c])).reduce((a, b) => a + b);\n count = ref.map(x => x.y).map(x => x.reduce((a, b) => a + b));\n }\n\n li.innerHTML += `: ${count}`;\n });\n return div;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":194,"value":"refMortality = FileAttachment(\"All States dataLayers.json\").json()","pinned":false,"hidden":false,"mode":"ojs"},{"id":199,"value":"refMortality.traces.map(x => x.name)","pinned":true,"hidden":false,"mode":"ojs"},{"id":33,"value":"md`

Methods

\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":51,"value":"Plotly = require(\"https://cdn.plot.ly/plotly-latest.min.js\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":36,"value":"cdemo = require('https://episphere.github.io/covid-demographics/cdemo.js')\n//cdemo = require('http://localhost:8000/covid-demographics/cdemo.js')","pinned":false,"hidden":false,"mode":"ojs"},{"id":39,"value":"ageGender = cdemo.getAgeGenderData()","pinned":false,"hidden":false,"mode":"ojs"},{"id":242,"value":"usa2020 = (await fetch(\n 'https://data.cdc.gov/resource/muzy-jte6.json?mmwryear=2020&jurisdiction_of_occurrence=United States&$limit=10000'\n)).json()","pinned":false,"hidden":false,"mode":"ojs"},{"id":123,"value":"// variables are: sex, race_ethnicity, age_group","pinned":true,"hidden":false,"mode":"ojs"}],"files":"$W34","stdlib":"1","titling":"auto"}