{"componentChunkName":"component---node-modules-narative-gatsby-theme-novela-src-templates-article-template-tsx","path":"/creating-best-practices-for-visualizing-covid-data","result":{"data":{"allSite":{"edges":[{"node":{"siteMetadata":{"name":"MIT Civic Data Design Lab"}}}]}},"pageContext":{"article":{"id":"b1a85141-23a0-5241-9a33-da921998b79a","slug":"/creating-best-practices-for-visualizing-covid-data","secret":false,"title":"Creating Best Practices for Visualizing Covid Data","author":"Griffin Kantz","date":"May 21st, 2020","dateForSEO":"2020-05-21T00:00:00.000Z","timeToRead":5,"excerpt":"The global importance of the SARS-CoV-2/COVID-19 pandemic, and the salience of eye-catching data visualizations in these times, necessitate a profoundly judicious use of data variables and normalizations. Here is a survey of professional guidance on this topic.","canonical_url":null,"subscription":true,"body":"function _extends() { _extends = Object.assign || function (target) { for (var i = 1; i < arguments.length; i++) { var source = arguments[i]; for (var key in source) { if (Object.prototype.hasOwnProperty.call(source, key)) { target[key] = source[key]; } } } return target; }; return _extends.apply(this, arguments); }\n\nfunction _objectWithoutProperties(source, excluded) { if (source == null) return {}; var target = _objectWithoutPropertiesLoose(source, excluded); var key, i; if (Object.getOwnPropertySymbols) { var sourceSymbolKeys = Object.getOwnPropertySymbols(source); for (i = 0; i < sourceSymbolKeys.length; i++) { key = sourceSymbolKeys[i]; if (excluded.indexOf(key) >= 0) continue; if (!Object.prototype.propertyIsEnumerable.call(source, key)) continue; target[key] = source[key]; } } return target; }\n\nfunction _objectWithoutPropertiesLoose(source, excluded) { if (source == null) return {}; var target = {}; var sourceKeys = Object.keys(source); var key, i; for (i = 0; i < sourceKeys.length; i++) { key = sourceKeys[i]; if (excluded.indexOf(key) >= 0) continue; target[key] = source[key]; } return target; }\n\n/* @jsx mdx */\nvar _frontmatter = {\n \"title\": \"Creating Best Practices for Visualizing Covid Data\",\n \"author\": \"Griffin Kantz\",\n \"date\": \"2020-05-21T00:00:00.000Z\",\n \"excerpt\": \"The global importance of the SARS-CoV-2/COVID-19 pandemic, and the salience of eye-catching data visualizations in these times, necessitate a profoundly judicious use of data variables and normalizations. Here is a survey of professional guidance on this topic.\",\n \"tags\": [\"Covid\", \"Data\", \"Visualization\"],\n \"hero\": \"images/blog_griffin-k_responsible-covid-data-visualization_header.png\"\n};\n\nvar makeShortcode = function makeShortcode(name) {\n return function MDXDefaultShortcode(props) {\n console.warn(\"Component \" + name + \" was not imported, exported, or provided by MDXProvider as global scope\");\n return mdx(\"div\", props);\n };\n};\n\nvar layoutProps = {\n _frontmatter: _frontmatter\n};\nvar MDXLayout = \"wrapper\";\nreturn function MDXContent(_ref) {\n var components = _ref.components,\n props = _objectWithoutProperties(_ref, [\"components\"]);\n\n return mdx(MDXLayout, _extends({}, layoutProps, props, {\n components: components,\n mdxType: \"MDXLayout\"\n }), mdx(\"p\", null, \"The global importance of the SARS-CoV-2/COVID-19 pandemic, and the salience of eye-catching data visualizations in these times, necessitate a profoundly judicious use of data variables and normalizations. Inappropriate choices in these respects can contribute to misconceptions about the magnitudes of various aspects of the crisis, either in comparison to each other or in comparison to other global issues. Unchecked misconceptions about quantitative data in public health have the potential to enable harm, a hazard which such tags as \", mdx(\"a\", _extends({\n parentName: \"p\"\n }, {\n \"href\": \"https://twitter.com/hashtag/datasaveslives?lang=en\",\n \"target\": \"_blank\",\n \"rel\": \"noreferrer\"\n }), \"#\", \"datasaveslives\"), \" may have the effect of blanketing over.\"), mdx(\"p\", null, \"Epidemiology in particular is wrought with terminology and dynamic relationships which are not immediately intuitive to understand. 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More recent words of wisdom:\"), mdx(\"p\", null, mdx(\"span\", _extends({\n parentName: \"p\"\n }, {\n \"className\": \"gatsby-resp-image-wrapper\",\n \"style\": {\n \"position\": \"relative\",\n \"display\": \"block\",\n \"marginLeft\": \"auto\",\n \"marginRight\": \"auto\",\n \"maxWidth\": \"1340px\"\n }\n }), \"\\n \", mdx(\"span\", _extends({\n parentName: \"span\"\n }, {\n \"className\": \"gatsby-resp-image-background-image\",\n \"style\": {\n \"paddingBottom\": \"108.65671641791046%\",\n \"position\": \"relative\",\n \"bottom\": \"0\",\n \"left\": \"0\",\n \"backgroundImage\": 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thread disappeared/was probably deleted without explanation or follow-up correction. For transparency and posterity, this is what it looked like. The info in the pictured tweet is unequivocally wrong\\\" [image of tweet by Dr. Eric Feigl Ding: \\\"SUMMARY: So what does this mean for the world??? We are now faced with the most virulent virus epidemic the world has ever seen. An R0=3.8 means that it exceeds SARS's modest 0.49 viral attack rate by 7.75x -- almost 8 fold! A virus that spreads 8 times faster than SARS...\\\"]\",\n \"title\": \"tweet4\",\n \"loading\": \"lazy\",\n \"style\": {\n \"width\": \"100%\",\n \"height\": \"100%\",\n \"margin\": \"0\",\n \"verticalAlign\": \"middle\",\n \"position\": \"absolute\",\n \"top\": \"0\",\n \"left\": \"0\"\n }\n })), \"\\n \"), \"\\n \")), mdx(\"p\", null, \"Even among infectious disease experts, consensus on the best practices for modeling the incoming data would adapt over time (\", mdx(\"a\", _extends({\n parentName: \"p\"\n }, {\n \"href\": \"https://twitter.com/neil_ferguson/status/1243294815200124928\",\n \"target\": \"_blank\",\n \"rel\": \"noreferrer\"\n }), \"thread\"), \"):\"), mdx(\"p\", null, mdx(\"span\", _extends({\n parentName: \"p\"\n }, {\n \"className\": \"gatsby-resp-image-wrapper\",\n \"style\": {\n \"position\": \"relative\",\n \"display\": \"block\",\n \"marginLeft\": \"auto\",\n \"marginRight\": \"auto\",\n \"maxWidth\": \"1324px\"\n }\n }), \"\\n \", mdx(\"span\", _extends({\n parentName: \"span\"\n }, {\n \"className\": \"gatsby-resp-image-background-image\",\n \"style\": {\n \"paddingBottom\": \"52.87009063444109%\",\n \"position\": \"relative\",\n \"bottom\": \"0\",\n \"left\": \"0\",\n \"backgroundImage\": \"url('data:image/png;base64,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')\",\n \"backgroundSize\": \"cover\",\n \"display\": \"block\"\n }\n })), \"\\n \", mdx(\"picture\", {\n parentName: \"span\"\n }, \"\\n \", mdx(\"source\", _extends({\n parentName: \"picture\"\n }, {\n \"srcSet\": [\"/static/3d8969f7f0e3203a38c7436bd7f5772c/33b3d/screen-shot-2020-05-19-at-6.45.02-am.webp 1324w\"],\n \"sizes\": \"(max-width: 1324px) 100vw, 1324px\",\n \"type\": \"image/webp\"\n })), \"\\n \", mdx(\"source\", _extends({\n parentName: \"picture\"\n }, {\n \"srcSet\": [\"/static/3d8969f7f0e3203a38c7436bd7f5772c/b58f7/screen-shot-2020-05-19-at-6.45.02-am.png 1324w\"],\n \"sizes\": \"(max-width: 1324px) 100vw, 1324px\",\n \"type\": \"image/png\"\n })), \"\\n \", mdx(\"img\", _extends({\n parentName: \"picture\"\n }, {\n \"className\": \"gatsby-resp-image-image\",\n \"src\": \"/static/3d8969f7f0e3203a38c7436bd7f5772c/b58f7/screen-shot-2020-05-19-at-6.45.02-am.png\",\n \"alt\": \"Tweet by neil_ferguson: \\\"1/4 - I think it would be helpful if I cleared up some confusion that has emerged in recent days. Some have interpreted my evidence to a UK parliamentary committee as indicating we have substantially revised our assessments of the potential mortality impact of COVID-19.\\\"\",\n \"title\": \"tweet5\",\n \"loading\": \"lazy\",\n \"style\": {\n \"width\": \"100%\",\n \"height\": \"100%\",\n \"margin\": \"0\",\n \"verticalAlign\": \"middle\",\n \"position\": \"absolute\",\n \"top\": \"0\",\n \"left\": \"0\"\n }\n })), \"\\n \"), \"\\n \")), mdx(\"p\", null, \"All these epistemological limitations fit under the umbrella of the \", mdx(\"a\", _extends({\n parentName: \"p\"\n }, {\n \"href\": \"https://en.wikipedia.org/wiki/Dunning%E2%80%93Kruger_effect\",\n \"target\": \"_blank\",\n \"rel\": \"noreferrer\"\n }), \"Dunning-Kruger effect\"), \":\"), mdx(\"p\", null, mdx(\"span\", _extends({\n parentName: \"p\"\n }, {\n \"className\": \"gatsby-resp-image-wrapper\",\n \"style\": {\n \"position\": \"relative\",\n \"display\": \"block\",\n \"marginLeft\": \"auto\",\n \"marginRight\": \"auto\",\n \"maxWidth\": \"800px\"\n }\n }), \"\\n \", mdx(\"span\", _extends({\n parentName: \"span\"\n }, {\n \"className\": \"gatsby-resp-image-background-image\",\n \"style\": {\n \"paddingBottom\": \"71.74999999999999%\",\n \"position\": \"relative\",\n \"bottom\": \"0\",\n \"left\": \"0\",\n \"backgroundImage\": \"url('data:image/jpeg;base64,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')\",\n \"backgroundSize\": \"cover\",\n \"display\": \"block\"\n }\n })), \"\\n \", mdx(\"picture\", {\n parentName: \"span\"\n }, \"\\n \", mdx(\"source\", _extends({\n parentName: \"picture\"\n }, {\n \"srcSet\": [\"/static/658e085757182365014640f9cc50c084/8d2ea/image1.webp 800w\"],\n \"sizes\": \"(max-width: 800px) 100vw, 800px\",\n \"type\": \"image/webp\"\n })), \"\\n \", mdx(\"source\", _extends({\n parentName: \"picture\"\n }, {\n \"srcSet\": [\"/static/658e085757182365014640f9cc50c084/c60e9/image1.jpg 800w\"],\n \"sizes\": \"(max-width: 800px) 100vw, 800px\",\n \"type\": \"image/jpeg\"\n })), \"\\n \", mdx(\"img\", _extends({\n parentName: \"picture\"\n }, {\n \"className\": \"gatsby-resp-image-image\",\n \"src\": \"/static/658e085757182365014640f9cc50c084/c60e9/image1.jpg\",\n \"alt\": \"Dunning-Kruger Effect. Diagram showing relationship between knowledge in field and confidence.\",\n \"title\": \"Dunning-Kruger\",\n \"loading\": \"lazy\",\n \"style\": {\n \"width\": \"100%\",\n \"height\": \"100%\",\n \"margin\": \"0\",\n \"verticalAlign\": \"middle\",\n \"position\": \"absolute\",\n \"top\": \"0\",\n \"left\": \"0\"\n }\n })), \"\\n \"), \"\\n \")), mdx(\"p\", null, mdx(\"em\", {\n parentName: \"p\"\n }, \"Original creator of this diagram unknown.\")), mdx(\"p\", null, \"Following this evolving consensus on best practices, we can often observe improvements over time in some of the COVID data visualizations which have managed to reach a wider audience, and these revisions are instructive.\"), mdx(\"p\", null, \"For example, \", mdx(\"a\", _extends({\n parentName: \"p\"\n }, {\n \"href\": \"https://covidactnow.org/\",\n \"target\": \"_blank\",\n \"rel\": \"noreferrer\"\n }), \"covidactnow.org\"), \" has added models of the infection growth rate (with confidence intervals) and the positive test rate to its forecasts of state-by-state hospital capacity, which were more simplistic \", mdx(\"a\", _extends({\n parentName: \"p\"\n }, {\n \"href\": \"https://web.archive.org/web/20200327060650/http:/www.covidactnow.org/state/NY\",\n \"target\": \"_blank\",\n \"rel\": \"noreferrer\"\n }), \"in the site\\u2019s first few weeks\"), \".\"), mdx(\"p\", null, \"The \", mdx(\"a\", _extends({\n parentName: \"p\"\n }, {\n \"href\": \"http://91-divoc.com/pages/covid-visualization/\",\n \"target\": \"_blank\",\n \"rel\": \"noreferrer\"\n }), \"interactive COVID tool at 91-divoc.com\"), \" was already making the best of multiple approaches upon its debut, showing gross and per-capita case counts by country and region, and allowing users to toggle between linear and logarithmic scales on the y-axis. Like other popular COVID tools, 91-divoc brackets the x-axes of its graphs around early quantitative thresholds such as \", mdx(\"em\", {\n parentName: \"p\"\n }, \"days since 100 cases\"), \", and by late April had shifted its default view from total cases to one-week trailing averages of new cases to better illustrate flattening growth. In mid-April the site added forecast trendlines for countries, but days later opted to truncate those forecasts to seven days forward so as to \\u201Cavoid extreme extrapolation\\u201D. Peruse 91-divoc\\u2019s change log \", mdx(\"a\", _extends({\n parentName: \"p\"\n }, {\n \"href\": \"http://91-divoc.com/pages/covid-visualization/changes.html\",\n \"target\": \"_blank\",\n \"rel\": \"noreferrer\"\n }), \"here\"), \".\"), mdx(\"p\", null, \"The most important prevailing debates on best practices for COVID data visualization concern the proper selection of variables and denominators.\"), mdx(\"p\", null, \"The modeler\\u2019s choice between gross counts and per-capita normalizations depends on the purpose of their model. Gross counts accurately measure the growth of local outbreaks, whereas per-capita rates better depict the burden on a nation/region\\u2019s healthcare system and policymaking apparatus. Some sentiments in favor of per-capita normalizations are \", mdx(\"a\", _extends({\n parentName: \"p\"\n }, {\n \"href\": \"https://twitter.com/NateSilver538/status/1245132431818178561\",\n \"target\": \"_blank\",\n \"rel\": \"noreferrer\"\n }), \"quite inflexble\"), \", but perhaps wrongly so. Observe the linear graph of cases by country captured on March 25 from 91-divoc\\u2019s tool:\"), mdx(\"p\", null, mdx(\"span\", _extends({\n parentName: \"p\"\n }, {\n \"className\": \"gatsby-resp-image-wrapper\",\n \"style\": {\n \"position\": \"relative\",\n \"display\": \"block\",\n \"marginLeft\": \"auto\",\n \"marginRight\": \"auto\",\n \"maxWidth\": \"960px\"\n }\n }), \"\\n \", mdx(\"span\", _extends({\n parentName: \"span\"\n }, {\n \"className\": \"gatsby-resp-image-background-image\",\n \"style\": {\n \"paddingBottom\": \"55.104166666666664%\",\n \"position\": \"relative\",\n \"bottom\": \"0\",\n \"left\": \"0\",\n \"backgroundImage\": 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[\"/static/46ecf1d1e90e59fe79421cb3eb716699/6c7d1/image2.webp 960w\"],\n \"sizes\": \"(max-width: 960px) 100vw, 960px\",\n \"type\": \"image/webp\"\n })), \"\\n \", mdx(\"source\", _extends({\n parentName: \"picture\"\n }, {\n \"srcSet\": [\"/static/46ecf1d1e90e59fe79421cb3eb716699/1fe05/image2.jpg 960w\"],\n \"sizes\": \"(max-width: 960px) 100vw, 960px\",\n \"type\": \"image/jpeg\"\n })), \"\\n \", mdx(\"img\", _extends({\n parentName: \"picture\"\n }, {\n \"className\": \"gatsby-resp-image-image\",\n \"src\": \"/static/46ecf1d1e90e59fe79421cb3eb716699/1fe05/image2.jpg\",\n \"alt\": \"COVID-19 Cases by Country from 91-divoc.com, captured on March 25, 2020.\",\n \"title\": \"COVID-19 Cases by Country from 91-divoc.com, captured on March 25, 2020.\",\n \"loading\": \"lazy\",\n \"style\": {\n \"width\": \"100%\",\n \"height\": \"100%\",\n \"margin\": \"0\",\n \"verticalAlign\": \"middle\",\n \"position\": \"absolute\",\n \"top\": \"0\",\n \"left\": \"0\"\n }\n })), \"\\n \"), \"\\n \")), mdx(\"p\", null, \"Now observe the linear graph of cases per capita, captured on the same day from the same tool:\"), mdx(\"p\", null, mdx(\"span\", _extends({\n parentName: \"p\"\n }, {\n \"className\": \"gatsby-resp-image-wrapper\",\n \"style\": {\n \"position\": \"relative\",\n \"display\": \"block\",\n \"marginLeft\": \"auto\",\n \"marginRight\": \"auto\",\n \"maxWidth\": \"960px\"\n }\n }), \"\\n \", mdx(\"span\", _extends({\n parentName: \"span\"\n }, {\n \"className\": \"gatsby-resp-image-background-image\",\n \"style\": {\n \"paddingBottom\": \"55.208333333333336%\",\n \"position\": \"relative\",\n \"bottom\": \"0\",\n \"left\": \"0\",\n \"backgroundImage\": 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[\"/static/0d552191a3e2c10067d053bce86032a9/6c7d1/image3.webp 960w\"],\n \"sizes\": \"(max-width: 960px) 100vw, 960px\",\n \"type\": \"image/webp\"\n })), \"\\n \", mdx(\"source\", _extends({\n parentName: \"picture\"\n }, {\n \"srcSet\": [\"/static/0d552191a3e2c10067d053bce86032a9/1fe05/image3.jpg 960w\"],\n \"sizes\": \"(max-width: 960px) 100vw, 960px\",\n \"type\": \"image/jpeg\"\n })), \"\\n \", mdx(\"img\", _extends({\n parentName: \"picture\"\n }, {\n \"className\": \"gatsby-resp-image-image\",\n \"src\": \"/static/0d552191a3e2c10067d053bce86032a9/1fe05/image3.jpg\",\n \"alt\": \"COVID-19 Cases per Capita by Country from 91-divoc.com, captured on March 25, 2020.\",\n \"title\": \"COVID-19 Cases per Capita by Country from 91-divoc.com, captured on March 25, 2020.\",\n \"loading\": \"lazy\",\n \"style\": {\n \"width\": \"100%\",\n \"height\": \"100%\",\n \"margin\": \"0\",\n \"verticalAlign\": \"middle\",\n \"position\": \"absolute\",\n \"top\": \"0\",\n \"left\": \"0\"\n }\n })), \"\\n \"), \"\\n \")), mdx(\"p\", null, \"It would seem here that the Vatican City is careening towards anarchy at an unprecedented rate\\u2014an incorrect implication emerging from our choice of scale and normalization.\"), mdx(\"p\", null, \"Here John Burn-Murdoch, whose graphs for the \", mdx(\"em\", {\n parentName: \"p\"\n }, \"Financial Times\"), \" have earned praise, makes his team\\u2019s case against using per-capita rates in graphs (\", mdx(\"a\", _extends({\n parentName: \"p\"\n }, {\n \"href\": \"https://twitter.com/jburnmurdoch/status/1249445458264698880?ref_src=twsrc%5Etfw\",\n \"target\": \"_blank\",\n \"rel\": \"noreferrer\"\n }), \"link\"), \"):\"), mdx(\"p\", null, mdx(\"span\", _extends({\n parentName: \"p\"\n }, {\n \"className\": \"gatsby-resp-image-wrapper\",\n \"style\": {\n \"position\": \"relative\",\n \"display\": \"block\",\n \"marginLeft\": \"auto\",\n \"marginRight\": \"auto\",\n \"maxWidth\": \"506px\"\n }\n }), \"\\n \", mdx(\"span\", _extends({\n 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numbers instead of per capita, and much more: [video link: 'Everything you need to know about that pink graph mapping coronavirus death rates by country by @jburnmurdoch'] And a chart showing why we're using absolute numbers rather than population-adjusted rates: [linked tweet: 'A quick chart for those who keep asking for per-capita adjustment: Here’s population vs total death toll one week after 10th death. No relationship. As I’ve been saying, population does not affect pace of spread. All per-capita figures do is make smaller countries look worse.'] [scatter plot chart with trendline] \\\"\",\n \"title\": \"tweet05\",\n \"loading\": \"lazy\",\n \"style\": {\n \"width\": \"100%\",\n \"height\": \"100%\",\n \"margin\": \"0\",\n \"verticalAlign\": \"middle\",\n \"position\": \"absolute\",\n \"top\": \"0\",\n \"left\": \"0\"\n }\n })), \"\\n \"), \"\\n \")), mdx(\"p\", null, \"In the \", mdx(\"a\", _extends({\n parentName: \"p\"\n }, {\n \"href\": \"https://twitter.com/CT_Bergstrom/status/1249930293928030209\",\n \"target\": \"_blank\",\n \"rel\": \"noreferrer\"\n }), \"thread below\"), \", Carl T. Bergstrom of the University of Washington explains how per-capita rates \", mdx(\"em\", {\n parentName: \"p\"\n }, \"can\"), \" be responsibly compared between countries if the curves are left-aligned to starting positions of a given fractional infection rate. However, he also advises that per-capita comparisons between regions are preferable to those between countries.\"), mdx(\"p\", null, mdx(\"span\", _extends({\n parentName: \"p\"\n }, {\n \"className\": \"gatsby-resp-image-wrapper\",\n \"style\": {\n \"position\": \"relative\",\n \"display\": \"block\",\n \"marginLeft\": \"auto\",\n \"marginRight\": \"auto\",\n \"maxWidth\": \"1346px\"\n }\n }), \"\\n \", mdx(\"span\", _extends({\n parentName: \"span\"\n }, {\n \"className\": \"gatsby-resp-image-background-image\",\n \"style\": {\n \"paddingBottom\": \"62.704309063893014%\",\n \"position\": \"relative\",\n \"bottom\": \"0\",\n \"left\": \"0\",\n \"backgroundImage\": \"url('data:image/png;base64,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')\",\n \"backgroundSize\": \"cover\",\n \"display\": \"block\"\n }\n })), \"\\n \", mdx(\"picture\", {\n parentName: \"span\"\n }, \"\\n \", mdx(\"source\", _extends({\n parentName: \"picture\"\n }, {\n \"srcSet\": [\"/static/3f8f0179165a2e9a980f52c1b3c9d7af/8170e/screen-shot-2020-05-19-at-6.50.42-am.webp 1346w\"],\n \"sizes\": \"(max-width: 1346px) 100vw, 1346px\",\n \"type\": \"image/webp\"\n })), \"\\n \", mdx(\"source\", _extends({\n parentName: \"picture\"\n }, {\n \"srcSet\": [\"/static/3f8f0179165a2e9a980f52c1b3c9d7af/4eb26/screen-shot-2020-05-19-at-6.50.42-am.png 1346w\"],\n \"sizes\": \"(max-width: 1346px) 100vw, 1346px\",\n \"type\": \"image/png\"\n })), \"\\n \", mdx(\"img\", _extends({\n parentName: \"picture\"\n }, {\n \"className\": \"gatsby-resp-image-image\",\n \"src\": \"/static/3f8f0179165a2e9a980f52c1b3c9d7af/4eb26/screen-shot-2020-05-19-at-6.50.42-am.png\",\n \"alt\": \"Tweet by Carl T. Bergstrom: \\\"1. When plotting epidemic curves or death totals, should we divide by population size? Here on twitter this question has generated a lot more heat than light. The answer is a bit subtle and so while I’ve tweeted about this before I want to address it in more detail.\\\"\",\n \"title\": \"tweet06\",\n \"loading\": \"lazy\",\n \"style\": {\n \"width\": \"100%\",\n \"height\": \"100%\",\n \"margin\": \"0\",\n \"verticalAlign\": \"middle\",\n \"position\": \"absolute\",\n \"top\": \"0\",\n \"left\": \"0\"\n }\n })), \"\\n \"), \"\\n \")), mdx(\"p\", null, \"There is also volatile disagreement on the proper selection of variables. Many sources, \", mdx(\"a\", _extends({\n parentName: \"p\"\n }, {\n \"href\": \"https://fivethirtyeight.com/features/coronavirus-case-counts-are-meaningless/\",\n \"target\": \"_blank\",\n \"rel\": \"noreferrer\"\n }), mdx(\"em\", {\n parentName: \"a\"\n }, \"FiveThirtyEight\"), \" among them\"), \", point out the futility of using reported case counts from governments (of inconsistent trustworthiness) using inconsistent testing methods. Burn-Murdoch\\u2019s detractors critique his visuals\\u2019 acceptance of China\\u2019s published counts at face value. \", mdx(\"a\", _extends({\n parentName: \"p\"\n }, {\n \"href\": \"https://blog.datawrapper.de/coronaviruscharts/\",\n \"target\": \"_blank\",\n \"rel\": \"noreferrer\"\n }), \"Datawrapper\"), \" recommends avoiding this problem by instead using confirmed death counts, which are harder to misreport but still imperfect since COVID can be an indirect cause of death.\"), mdx(\"p\", null, \"What are some general best practices and precautions for analyzing and visualizing COVID data? Here are some excellent sources addressing this question.\"), mdx(\"ul\", null, mdx(\"li\", {\n parentName: \"ul\"\n }, mdx(\"strong\", {\n parentName: \"li\"\n }, mdx(\"a\", _extends({\n parentName: \"strong\"\n }, {\n \"href\": \"https://www.tableau.com/about/blog/2020/3/ten-considerations-you-create-another-chart-about-covid-19\",\n \"target\": \"_blank\",\n \"rel\": \"noreferrer\"\n }), \"10 considerations before you create another chart about COVID-19\")), \" (03/13/20) by Amanda Makulec, Operations Director for the Data Visualization Societ\", mdx(\"a\", _extends({\n parentName: \"li\"\n }, {\n \"href\": \"http://news.mit.edu/2020/catherine-dignazio-visualizing-covid-19-data-0414\",\n \"target\": \"_blank\",\n \"rel\": \"noreferrer\"\n })), \"y\"), mdx(\"li\", {\n parentName: \"ul\"\n }, mdx(\"strong\", {\n parentName: \"li\"\n }, mdx(\"a\", _extends({\n parentName: \"strong\"\n }, {\n \"href\": \"http://news.mit.edu/2020/catherine-dignazio-visualizing-covid-19-data-0414\",\n \"target\": \"_blank\",\n \"rel\": \"noreferrer\"\n }), \"3 Questions: Catherine D\\u2019Ignazio on visualizing COVID-19 data\")), \" (04/13/20) profiling MIT assistant professor D\\u2019Ignazio;\", mdx(\"a\", _extends({\n parentName: \"li\"\n }, {\n \"href\": \"https://www.esri.com/arcgis-blog/products/product/mapping/mapping-coronavirus-responsibly/\",\n \"target\": \"_blank\",\n \"rel\": \"noreferrer\"\n }))), mdx(\"li\", {\n parentName: \"ul\"\n }, mdx(\"strong\", {\n parentName: \"li\"\n }, mdx(\"a\", _extends({\n parentName: \"strong\"\n }, {\n \"href\": \"https://www.esri.com/arcgis-blog/products/product/mapping/mapping-coronavirus-responsibly/\",\n \"target\": \"_blank\",\n \"rel\": \"noreferrer\"\n }), \"Mapping coronavirus, responsibly\")), \" (02/25/20) by Kenneth Field for ESRI;\"), mdx(\"li\", {\n parentName: \"ul\"\n }, mdx(\"strong\", {\n parentName: \"li\"\n }, mdx(\"a\", _extends({\n parentName: \"strong\"\n }, {\n \"href\": \"https://fivethirtyeight.com/features/why-its-so-freaking-hard-to-make-a-good-covid-19-model/\",\n \"target\": \"_blank\",\n \"rel\": \"noreferrer\"\n }), \"Why It\\u2019s So Freaking Hard to Make a Good COVID-19 Model\")), \" (03/31/20) by Maggie Koerth, Laura Bronner, and Jasmine Mithani for \", mdx(\"em\", {\n parentName: \"li\"\n }, \"FiveThirtyEight\"), \";\"), mdx(\"li\", {\n parentName: \"ul\"\n }, \"Once again, \", mdx(\"a\", _extends({\n parentName: \"li\"\n }, {\n \"href\": \"https://twitter.com/EvanMPeck/status/1235568532840120321\",\n \"target\": \"_blank\",\n \"rel\": \"noreferrer\"\n }), \"this Twitter thread\"), \" by Evan M. Peck of Bucknell University.\")), mdx(\"p\", null, \"For trustworthy visualizations, explore \", mdx(\"a\", _extends({\n parentName: \"p\"\n }, {\n \"href\": \"https://www.tableau.com/about/blog/2020/4/most-interesting-data-vizzes-covid-19-weve-seen-media-so-far\",\n \"target\": \"_blank\",\n \"rel\": \"noreferrer\"\n }), \"these gems selected by Tableau\"), \" or the\", mdx(\"a\", _extends({\n parentName: \"p\"\n }, {\n \"href\": \"https://blog.datawrapper.de/coronaviruscharts/\",\n \"target\": \"_blank\",\n \"rel\": \"noreferrer\"\n }), \" charts featured by Datawrapper\"), \".\"), mdx(\"p\", null, \"The COVID-19 pandemic has underscored the importance of informed epidemiological data analysis. Often, the niche expertise required for this analysis can be a hazard \", mdx(\"a\", _extends({\n parentName: \"p\"\n }, {\n \"href\": \"https://twitter.com/ferrisjabr/status/1221146622341443584\",\n \"target\": \"_blank\",\n \"rel\": \"noreferrer\"\n }), \"even to credentialed experts\"), \". The democratic, collaborative nature of public forum data science can help us meet the demands of this vexing global problem. But, this does not suggest that every individual or team reporting on COVID has an equal claim to accuracy; rather, it implies that the analysis challenges are larger than any one mind can confidently answer.\"));\n}\n;\nMDXContent.isMDXComponent = 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Our work has always been about understanding data and using it to call attention to the needs and interests of citizens on the margins of policy development. The global pandemic, and its quantitative nature, has made this mission all the more crucial. COVID-19 is exposing existing inequalities in our country and across the globe. We urgently must document these disparities as they emerge to have a chance of addressing them.\"), mdx(\"p\", null, \"We are starting this blog in order to share the work we\\u2019re doing with a larger audience. We join a large community of scientists, designers, and urbanists around the world who are already engaged in open conversations about COVID-19 through the lens of its data\\u2014its nuances, its stories, and its calls to action. 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On June 4th, the U.S. Department of Health and Human Services (HHS) released new guidance requiring…","canonical_url":null,"subscription":true,"body":"function _extends() { _extends = Object.assign || function (target) { for (var i = 1; i < arguments.length; i++) { var source = arguments[i]; for (var key in source) { if (Object.prototype.hasOwnProperty.call(source, key)) { target[key] = source[key]; } } } return target; }; return _extends.apply(this, arguments); }\n\nfunction _objectWithoutProperties(source, excluded) { if (source == null) return {}; var target = _objectWithoutPropertiesLoose(source, excluded); var key, i; if (Object.getOwnPropertySymbols) { var sourceSymbolKeys = Object.getOwnPropertySymbols(source); for (i = 0; i < sourceSymbolKeys.length; i++) { key = sourceSymbolKeys[i]; if (excluded.indexOf(key) >= 0) continue; if (!Object.prototype.propertyIsEnumerable.call(source, key)) continue; target[key] = source[key]; } } return target; }\n\nfunction _objectWithoutPropertiesLoose(source, excluded) { if (source == null) return {}; var target = {}; var sourceKeys = Object.keys(source); var key, i; for (i = 0; i < sourceKeys.length; i++) { key = sourceKeys[i]; if (excluded.indexOf(key) >= 0) continue; target[key] = source[key]; } return target; }\n\n/* @jsx mdx */\nvar _frontmatter = {\n \"title\": \"WHO CARES about COVID-19?\",\n \"author\": \"Brian Williams\",\n \"date\": \"2020-08-30T00:00:00.000Z\",\n \"tags\": [\"Covid\", \"Data\", \"Health\", \"Race\", \"Visualization\"],\n \"hero\": \"images/pic-for-2nd-article-cares.png\"\n};\n\nvar makeShortcode = function makeShortcode(name) {\n return function MDXDefaultShortcode(props) {\n console.warn(\"Component \" + name + \" was not imported, exported, or provided by MDXProvider as global scope\");\n return mdx(\"div\", props);\n };\n};\n\nvar layoutProps = {\n _frontmatter: _frontmatter\n};\nvar MDXLayout = \"wrapper\";\nreturn function MDXContent(_ref) {\n var components = _ref.components,\n props = _objectWithoutProperties(_ref, [\"components\"]);\n\n return mdx(MDXLayout, _extends({}, layoutProps, props, {\n components: components,\n mdxType: \"MDXLayout\"\n }), mdx(\"p\", null, mdx(\"strong\", {\n parentName: \"p\"\n }, \"Is Section 18115 really doing anything?\")), mdx(\"p\", null, \"On June 4th, the U.S. Department of Health and Human Services (HHS) released new guidance requiring laboratories to include relevant demographic data, such as age and race, on every COVID-19 test. As specified in the Coronavirus Aid, Relief, and Economic Security (CARES) ActSection 18115,these changes went into effect on August 1st. But did anythingreallyhappen?\"), mdx(\"p\", null, \"As a follow up to the\", mdx(\"a\", _extends({\n parentName: \"p\"\n }, {\n \"href\": \"https://blog.civicdatadesignlab.mit.edu/data-from-reported-covid-19-tests-are-telling-an-incomplete-story:-here's-what-you-need-to-know\",\n \"target\": \"_blank\",\n \"rel\": \"noreferrer\"\n }), \"previous article\"), \"on missing reported race data in COVID-19 tests nationwide, we wanted to see the significant effects (if any) of the new federal guidance passed in the CARES Act Section 18115. Previously, we found large gaps in the aggregate data across the United States. From this, we asked if anything could be done at individual laboratories or medical testing centers to compensate for the gap. Now, we seek to investigate the impact of the new federal regulation on reporting race data.\"), mdx(\"p\", null, \"Data Comparisons, Before and After 18115\"), mdx(\"p\", null, \"Section 18115 may actually be impacting the level of reported race data nationwide\\u2026 But is it enough?\"), mdx(\"p\", null, \"From July 26th to August 26th, positive COVID-19 cases increased by over 1.5 million but only about 1 million had associated race data.\"), mdx(\"p\", null, \"In the United States, the percent of cases with associated race data has varied over time:\"), mdx(\"ol\", null, mdx(\"li\", {\n parentName: \"ol\"\n }, \"cumulative as of August 26th: 55.65%\"), mdx(\"li\", {\n parentName: \"ol\"\n }, \"only between July 26th and August 26th: 66.10%\"), mdx(\"li\", {\n parentName: \"ol\"\n }, \"cumulative as of July 26th: 51.75%\")), mdx(\"p\", null, \"[[\", \"insert bar graph]\", \"](\", mdx(\"a\", _extends({\n parentName: \"p\"\n }, {\n \"href\": \"https://chart-studio.plotly.com/~brianwilliams2022/17.embed\",\n \"target\": \"_blank\",\n \"rel\": \"noreferrer\"\n }), \"https://chart-studio.plotly.com/~brianwilliams2022/17.embed\"), \")\"), mdx(\"p\", null, \"This visualization shows the difference of percents of cases where race data is known, per state. The number represented in each bar is the percent difference in cases with associated race between two different periods: fromJuly 26th to August 26thand from the beginning of data collection up until July 26th.\"), mdx(\"p\", null, \"For example, let\\u2019s say that a given state\\u2019s total cases rose from July to August by x, and of those cases, there is a subsection where race data is unknown. Let\\u2019s say this subsection of unknown race data increases by y. I calculated 1 - (y/x), and compared that same calculation instead with the cumulative period from toward the beginning of the pandemic (late March) to July 26th.\"), mdx(\"p\", null, \"Some of these values are negative because the total number of Other and Unknown cases actuallyincreasesfrom July to August at a greater percentage compared to the beginning of the pandemic. This is pretty alarming.\"), mdx(\"p\", null, \"Please note that North Dakota (ND) seems to have a very large value: 87%. This is due to the fact that North Dakota only very recently started reporting race data foranyof their cases.\"), mdx(\"p\", null, \"But why?\"), mdx(\"p\", null, \"This seems unusual but I think it can be explained by retroactive revisions to the data (either by individual laboratories or a state\\u2019s Department of Public Health) which would move cases from these categories to their accurate racial category.\"), mdx(\"p\", null, \"For example, if a scientist was able to confirm a case or a set of cases belonged to certain demographic after the total number of cases had already been reported, you would simply move cases over to their respective categories. Maybe in certain situations, backlogs of case data prevent labs from sorting cases by demographic data before state deadlines but as time goes along, they are able to update their reported case data. This doesn\\u2019t seem to happen in many states and the testing efficiency problem could easily be a bigger and more widespread problem than I am expressing/speculating here.\"), mdx(\"p\", null, \"Here\\u2019s a regional breakdown of the same data.\"), mdx(\"p\", null, \"[[\", \"insert color map]\", \"](\", mdx(\"a\", _extends({\n parentName: \"p\"\n }, {\n \"href\": \"http://plotly.com/~brianwilliams2022/35.embed\",\n \"target\": \"_blank\",\n \"rel\": \"noreferrer\"\n }), \"http://plotly.com/~brianwilliams2022/35.embed\"), \")\"), mdx(\"p\", null, \"Discussion: Almost too little, too late\"), mdx(\"p\", null, \"So far, the national average of cases with race data increased by about 15% directly following the period Section 18115 went into effect, compared to reported race averages during the rest of the pandemic.\"), mdx(\"p\", null, \"Is this difference big enough? And how much of it can be directly attributed to Section 18115? To be quite honest\\u2026 I\\u2019m not sure.\"), mdx(\"p\", null, \"Though somewhat significant, the impact is almost too little too late. Just imagine what we\\u2019re not seeing, and the things we\\u2019ve \", \"*\", \"already\", \"*\", \" missed. Before August, there were more than 2 million Covid-19 tests that were unidentifiable by race. This type of federal guidance should have been in place since February and at the latest, the beginning of March.\"), mdx(\"p\", null, \"It doesn\\u2019t seem like we will have access to complete, accurate, and thorough data sets into the foreseeable future for many reasons starting at the laboratory level stretching all the way to the federal level. I just hope policymakers, publicly obligated to support our communities, are doing their best trying to walk in the dark during this pandemic.\"), mdx(\"p\", null, \"Sources\"), mdx(\"ol\", null, mdx(\"li\", {\n parentName: \"ol\"\n }, mdx(\"a\", _extends({\n parentName: \"li\"\n }, {\n \"href\": \"https://www.hhs.gov/sites/default/files/covid-19-laboratory-data-reporting-guidance.pdf\",\n \"target\": \"_blank\",\n \"rel\": \"noreferrer\"\n }), \"https://www.hhs.gov/sites/default/files/covid-19-laboratory-data-reporting-guidance.pdf\")), mdx(\"li\", {\n parentName: \"ol\"\n }, mdx(\"a\", _extends({\n parentName: \"li\"\n }, {\n \"href\": \"https://www.cdc.gov/coronavirus/2019-ncov/lab/reporting-lab-data.html#what-to-include\",\n \"target\": \"_blank\",\n \"rel\": \"noreferrer\"\n }), \"https://www.cdc.gov/coronavirus/2019-ncov/lab/reporting-lab-data.html#what-to-include\"))));\n}\n;\nMDXContent.isMDXComponent = 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