{"componentChunkName":"component---node-modules-narative-gatsby-theme-novela-src-templates-article-template-tsx","path":"/meet-the-urops!","result":{"data":{"allSite":{"edges":[{"node":{"siteMetadata":{"name":"MIT Civic Data Design Lab"}}}]}},"pageContext":{"article":{"id":"43c0244f-6ead-5388-a2dd-1411b949865e","slug":"/meet-the-urops!","secret":false,"title":"Meet the UROPs!","author":"Evan Denmark","date":"June 26th, 2020","dateForSEO":"2020-06-26T00:00:00.000Z","timeToRead":3,"excerpt":"We have four outstanding undergrads tackling missing data this summer. 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Throughout the summer and beyond, we will have lab members as well as external experts contribute to projects that highlight missing data. This summer, we have four current and recent undergraduate students developing their own projects and we\\u2019ve asked them to introduce themselves and their summer goals.\")), mdx(\"p\", null, mdx(\"strong\", {\n    parentName: \"p\"\n  }, \"Joyce Zhao\")), mdx(\"p\", null, \"Hi it\\u2019s Joyce! I just graduated from Wellesley College, and I\\u2019m looking forward to working with the CDDL this summer. I\\u2019ve been examining the intersections of housing insecurity, gentrification, and COVID-19 within the city of San Francisco, California. Despite a moratorium, eviction notices are still being filed, prompting me to wonder who is still at risk of eviction, where these evictions occur, and how the city responds in the midst of a pandemic. From initial observations, it\\u2019s clear that some neighborhoods in San Francisco have been hit harder by COVID-19 than others. In this project, I hope to use data to spark conversations and raise questions about the relationship between COVID-19 and housing injustice, as well as engage with work done by advocacy groups and organizers in San Francisco.\"), mdx(\"p\", null, mdx(\"strong\", {\n    parentName: \"p\"\n  }, \"Brian Williams\")), mdx(\"p\", null, \"Hey! I\\u2019m an aspiring protein engineer, who\\u2019s actively looking for ways to uplift marginalized communities everywhere. I am a rising junior at MIT, majoring in Biological Engineering and minoring in Black Studies, and am looking forward to working in the Civic Data Design Lab this summer! So far, I have been analyzing and visualizing data reports on racial and ethnic data tied to COVID-19 test results and related deaths. In this project, I\\u2019m hoping to explain the confusing (and not so surprising) trends in the data and shed light on the reasons why discrepancies exist between states when reporting race in COVID-19 tests and deaths. Why is there missing data here, and what does this tell us? About testing strategies? About practicality? About policy?\"), mdx(\"p\", null, \"I am passionate about understanding how policy decisions drive economic and social change. As an avid creative, I love to model and write poetry, and I play varsity baseball at MIT.\"), mdx(\"p\", null, mdx(\"strong\", {\n    parentName: \"p\"\n  }, \"Amy Fang\")), mdx(\"p\", null, \"I graduated from MIT in 2020, majoring in Mechanical Engineering and minoring in Anthropology and Design. In my semester, I took my first urban planning class,11.158 Behavior and Policy: Connections in Transportation, which has catalyzed a newfound fascination for all things behaviorally irrational (yet undeniably human) and excessively complicated. During this time, I became engaged to NUMTOT in a whirlwind romance, who helped her expose the inequitable safety misperceptions of shared micromobility.\"), mdx(\"p\", null, \"Today, I fulfill my insatiable hunger for urban planning through mouthwatering memes, palatable PDF readings from classes I will never take, and spicy opportunities like the Missing Data Project. My first project investigates contradictory policies on the county, state, and national levels, specifically in the realm of everyday COVID-19 public safety practices. Just like my \\u201Cmissing\\u201D data sources, she thrives in ambiguity.\"), mdx(\"p\", null, mdx(\"strong\", {\n    parentName: \"p\"\n  }, \"Yu Jing Chen\")), mdx(\"p\", null, \"Hi! I\\u2019m a rising junior at MIT studying 11-6 (Urban Studies and Planning with Computer Science) and considering a minor in Entrepreneurship and Innovation!On campus, I can be found pretty much everywhere\\u2014 I\\u2019m beyond grateful to have found community in various corners of campus, all the while forging space and platforms to empower those that are traditionally unheard across campus.\"), mdx(\"p\", null, \"Regarding my project, if there\\u2019s one thing to know about COVID-19, it\\u2019s that it does not discriminate. Yet when looking at the statistics, the numbers seem to tell a different story. For indigenous communities, COVID-19 has become all too familiar, with Navajo Nation surpassing New York\\u2014once the epicenter of the pandemic\\u2014in positive per-capita cases. Through this project, I hope to dive into the different reasons why this is the case and mapping out the reality in order to read into the disparities that exist. 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impacts worldwide, calling for all research efforts to tackle the uncertainty and…","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\": \"Unpacking COVID-19 publication research themes and urban indications (Part I)\",\n  \"author\": \"Yuan Lai\",\n  \"date\": \"2020-06-19T00:00:00.000Z\",\n  \"tags\": [\"Covid\", \"Data\", \"Visualization\"],\n  \"hero\": \"images/dashboard.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 ongoing COVID-19 pandemic brings both deep and broad impacts worldwide, calling for all research efforts to tackle the uncertainty and urgency involving the novel virus. With unknown pathogens, epidemiological characteristics, and transmission patterns, the new virus (SARS-CoV-2) inevitably brings inconsistency, discrepancies, and debates among the scientific community. Additionally, the rapid transmission speed and large scale of the infected population requires timely responses despite the above uncertainties.\"), mdx(\"h2\", {\n    \"id\": \"processing-covid-19-manuscripts-metadata\"\n  }, \"Processing COVID-19 manuscripts metadata\"), mdx(\"p\", null, \"On March 17th, the White House Office of Science and Technology Policy launched a \", mdx(\"a\", _extends({\n    parentName: \"p\"\n  }, {\n    \"href\": \"https://www.kaggle.com/allen-institute-for-ai/CORD-19-research-challenge\",\n    \"target\": \"_blank\",\n    \"rel\": \"noreferrer\"\n  }), \"COVID-19 AI OPEN Research Dataset Challenge\"), \" in a partnership with Allen Institute for Artificial Intelligence (AI2), the Chan Zuckerberg Initiative, Microsoft Research, Georgetown University\\u2019s Center for Security and Emerging Technology, and National Institutes of Health. Hosted on \", mdx(\"a\", _extends({\n    parentName: \"p\"\n  }, {\n    \"href\": \"https://www.kaggle.com/\",\n    \"target\": \"_blank\",\n    \"rel\": \"noreferrer\"\n  }), \"Kaggle\"), \", an online community of data scientists and machine learning experts owned by Google, the published dataset contains more than 29,000 research articles (over 13,000 with full text) on SARS-CoV-2 and COVID-19 clinical studies, public health response, population characteristics, and epidemiology. Each publication has been parsed into separate JSON files with its metadata, authors\\u2019 information, abstract, and full manuscript.\"), mdx(\"p\", null, \"One core challenge is the utilization of data science and machine learning for better collection, organization, and audition of surging manuscripts. This data exploration aims to unpack the domains and progression of COVID-19 related studies by unpacking currently available research publications. We adopt both quantitative and qualitative methods from computer science and urban planning with the goal of stimulating interdisciplinary discussion and research collaboration and supporting more inclusive approaches to address some immediate and long-term problems. We first summarize a retrospective overview of COVID-19 research challenges and progression in the data science community through this Kaggle challenge. Text analysis and visualization identify several key findings and critical factors that are highly relevant to COVID-19.\"), mdx(\"h2\", {\n    \"id\": \"quantifying-covid-19-research-thematic-structure\"\n  }, \"Quantifying COVID-19 research thematic structure\"), mdx(\"p\", null, \"This study proceeds in the following steps. First, we explore the entire collection of PDF-parsed datasets to understand the COVID-19 research landscape. To do this, we establish a pipeline to process these publications\\u2019 metadata and abstracts from an extensive collection of JSON files (n=47,731) in a Python environment. Each file includes key information such as the title, number of authors, authors\\u2019 origin (country), and a full text of the abstract, extracted from its associated research article. We further clean the abstract text data (e.g., removing the stop words, lemmatization, vectorization) to generate a descriptive summary of popular words (single word, bi-gram, and tri-gram), number of authors, and origin (by the first author\\u2019s country). Using \", mdx(\"a\", _extends({\n    parentName: \"p\"\n  }, {\n    \"href\": \"https://en.wikipedia.org/wiki/Topic_model\",\n    \"target\": \"_blank\",\n    \"rel\": \"noreferrer\"\n  }), \"LDA topic modeling\"), \" techniques, we train a model with the processed texts to discover underlying topic groups and corresponding keywords.\"), mdx(\"p\", null, \"We expect to identify not only major research interests but also a small subset of publications that may relate to urban science through this exploratory analysis. To find the latter subset, we filter abstract texts based on a list of urban science-related vocabulary, such as \\u201Curban planning\\u201D, \\u201Cpublic health\\u201D, \\u201Cenvironment\\u201D, \\u201Csocial distancing\\u201D, \\u201Ctransportation\\u201D, \\u201Cmobility\\u201D, \\u201Chousing\\u201D, \\u201Ccommunity\\u201D, and \\u201Crace\\u201D. This process extracts a subset (n=3914) from all manuscripts to further identify publications that may relate to cities and urban science. Analyzing urban-related articles will provide more detailed insights into current research interests and key findings relevant to planning, policy, and operation in cities. Using topic modeling technique, we quantify each article\\u2019s thematic composition with four components:\"), mdx(\"ol\", null, mdx(\"li\", {\n    parentName: \"ol\"\n  }, mdx(\"strong\", {\n    parentName: \"li\"\n  }, \"Epidemiological\"), \" research on infection prevention and control, including the effectiveness of different response strategies and public health measures, such as quarantine, community contact reduction, travel restriction, social distancing at school and workplaces, personal protective equipment (PPE), and public health digital surveillance. Popular terms representing this theme include\\u201Cpublic\\u201D, \\u201Coutbreak\\u201D, \\u201Cpandemic\\u201D, \\u201Csocial\\u201D, \\u201Cepidemic\\u201D, \\u201Cspread\\u201D, \\u201Cpopulation\\u201D, \\u201Ctransmission\\u201D, \\u201Cglobal\\u201D, \\u201Cdistancing\\u201D, \\u201Cresponse\\u201D, etc. We consider this is the most relevant theme for urban science.\"), mdx(\"li\", {\n    parentName: \"ol\"\n  }, mdx(\"strong\", {\n    parentName: \"li\"\n  }, \"Virological\"), \" research on SARS-CoV-2, including its genetic sequence, origin, evolution, and genomic differences by geography, transmission, incubation, mutation, and stability in various environments. This also includes material studies on viral shedding from humans (stool, urine, blood, nasal discharge), the persistence of virus on different surface material (e,g., copper, stainless steel, plastic), the virus\\u2019 susceptibility to cleaning or disinfecting agents, the physical science of the virus spread, and decontamination mechanics as well as virus transmission patterns involving seasonality, environment (e.g., humidity, temperature), community spread, and asymptomatic transmission during incubation. Popular terms representing this theme include\\u201Ccell\\u201D, \\u201Cprotein\\u201D, \\u201Cvirus\\u201D, \\u201Chost\\u201D, \\u201Cimmune\\u201D, \\u201Cintracellular\\u201D, \\u201Cgene\\u201D, \\u201Cantiviral\\u201D, \\u201Creplication\\u201D, etc.\"), mdx(\"li\", {\n    parentName: \"ol\"\n  }, mdx(\"strong\", {\n    parentName: \"li\"\n  }, \"Clinical\"), \" trials and medical evidence for therapeutic interventions including the efficacy of treatment, or diagnostic findings on infected patients and antibody testing. This also includes patient descriptions, virus incubation period, length of hospital stay, and asymptomatic likelihood. Studies regarding high-risk patient groups with a medical history and pre-existing conditions, such as hypertension, diabetes, heart disease, cardio and cerebrovascular diseases, respiratory diseases are included. Popular terms representing this theme include\\u201Csars\\u201D, \\u201Cinfluenza\\u201D, \\u201Cmers\\u201D, \\u201Cpatient\\u201D, \\u201Cacute\\u201D, \\u201Cclinical\\u201D, \\u201Cpathogen\\u201D, \\u201Csyndrome\\u201D, etc.\"), mdx(\"li\", {\n    parentName: \"ol\"\n  }, mdx(\"strong\", {\n    parentName: \"li\"\n  }, \"Others\"), \" represent miscellaneous themes besides the above three.\")), mdx(\"iframe\", {\n    src: \"https://public.tableau.com/views/COVID-19OpenResearchViz/Dashboard1?:showVizHome=no&:embed=true\",\n    width: \"100%\",\n    height: \"600\",\n    allowFullScreen: true\n  }), mdx(\"h2\", {\n    \"id\": \"visualizing-urban-related-manuscripts\"\n  }, \"Visualizing urban-related manuscripts\"), mdx(\"p\", null, \"The \", mdx(\"a\", _extends({\n    parentName: \"p\"\n  }, {\n    \"href\": \"https://public.tableau.com/views/COVID-19OpenResearchViz/Dashboard1?:language=en&:display_count=y&:toolbar=n&:origin=viz_share_link\",\n    \"target\": \"_blank\",\n    \"rel\": \"noreferrer\"\n  }), \"interactive dashboard\"), \" enables users to quickly browse urban-related manuscripts in different countries, sorted by relevance to epidemiology. In the next post, we will discuss two questions related to urban science:\"), mdx(\"ol\", null, mdx(\"li\", {\n    parentName: \"ol\"\n  }, \"How do epidemiological research findings on virus transmission and community spread indicate the new norm of urban life?\"), mdx(\"li\", {\n    parentName: \"ol\"\n  }, \"How does social science contribute to COVID-19 research, especially when addressing unexpected conflicts and controversies involving socioeconomic equity, environmental justice, data ethics, and policy fairness?\")), mdx(\"p\", null, \"Since the beginning of the pandemic, we have witnessed \", mdx(\"a\", _extends({\n    parentName: \"p\"\n  }, {\n    \"href\": \"https://nationalpost.com/news/a-matter-of-trust-covid-19-pandemic-has-tested-public-confidence-in-science-like-never-before\",\n    \"target\": \"_blank\",\n    \"rel\": \"noreferrer\"\n  }), \"debates and mistrust in science amid this pandemic\"), \", \", mdx(\"a\", _extends({\n    parentName: \"p\"\n  }, {\n    \"href\": \"https://www.thelancet.com/journals/langlo/article/PIIS2214-109X(20)30249-7/fulltext\",\n    \"target\": \"_blank\",\n    \"rel\": \"noreferrer\"\n  }), \"revealing injustice,\"), \" \", mdx(\"a\", _extends({\n    parentName: \"p\"\n  }, {\n    \"href\": \"https://www.npr.org/sections/health-shots/2020/04/21/838763690/opinion-u-s-must-avoid-building-racial-bias-into-covid-19-emergency-guidance\",\n    \"target\": \"_blank\",\n    \"rel\": \"noreferrer\"\n  }), \"biases\"), \", and \", mdx(\"a\", _extends({\n    parentName: \"p\"\n  }, {\n    \"href\": \"https://www.thelancet.com/journals/langlo/article/PIIS2214-109X(20)30191-1/fulltext\",\n    \"target\": \"_blank\",\n    \"rel\": \"noreferrer\"\n  }), \"uncertainty\"), \" in treatment, testing, policy, and public services. In MIT Course 11-6 (\", mdx(\"a\", _extends({\n    parentName: \"p\"\n  }, {\n    \"href\": \"https://urban-science.mit.edu/\",\n    \"target\": \"_blank\",\n    \"rel\": \"noreferrer\"\n  }), \"Urban Science and Planning with Computer Science\"), \"), we believe in the importance of addressing broader social, environmental, and political challenges at an urban scale through both technology and planning methods. In Part II, we will further discuss how cities and urban science experts can integrate scientific insights with action and further contribute to collective research, as well as note the impact of potential missing data on under-represented population groups. 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Region","author":"Griffin Kantz","date":"June 15th, 2020","dateForSEO":"2020-06-15T00:00:00.000Z","timeToRead":5,"excerpt":"When comparing how different regions have been impacted by the coronavirus over time, it is important to define a \"starting point\": an early…","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\": \"The Importance of the \\\"Starting Point\\\" in Tracking COVID by Region\",\n  \"author\": \"Griffin Kantz\",\n  \"date\": \"2020-06-15T00:00:00.000Z\",\n  \"excerpt\": \"\",\n  \"tags\": [\"Covid\", \"Data\", \"Visualization\"],\n  \"hero\": \"images/covid-19-critical-mass_graphic.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, \"When comparing how different regions have been impacted by the coronavirus over time, it is important to define a \\u201Cstarting point\\u201D: an early timepoint in the spread of the virus from which the timepoints of future observations can be measured. Although one may think that the natural place to start would be a region\\u2019s first recorded COVID case or the first COVID fatality, this can lead to improper or uninformative region-to-region comparisons. Testing at the beginning of a local COVID outbreak can be seriously unreliable, and the spread of the disease from the first handful of cases to the next is tied to the \", mdx(\"a\", _extends({\n    parentName: \"p\"\n  }, {\n    \"href\": \"https://www.cdc.gov/mmwr/volumes/69/wr/mm6915e1.htm\",\n    \"target\": \"_blank\",\n    \"rel\": \"noreferrer\"\n  }), \"idiosyncrasies of people\\u2019s daily behaviors\"), \".\"), mdx(\"p\", null, \"In New York state, where COVID \", mdx(\"a\", _extends({\n    parentName: \"p\"\n  }, {\n    \"href\": \"https://www.cdc.gov/mmwr/volumes/69/wr/mm6922e1.htm?s_cid=mm6922e1_w\",\n    \"target\": \"_blank\",\n    \"rel\": \"noreferrer\"\n  }), \"may have arrived more than one month before the first confirmed case\"), \" on March 1, 2020, confirmed fatalities soared from 1 death to nearly 500 in just two weeks. Conversely, the Norfolk-Newport News area of Virginia had still not witnessed ten deaths one month after its first. \"), mdx(\"p\", null, \"COVID metrics between different afflicted regions appear to begin behaving in a more predictable manner once the disease has reached a critical mass and begun spreading widely. COVID data analysts try to set the starting point for their measurements at some level where this critical mass has likely been reached and the initial random variation has dissipated.\"), mdx(\"p\", null, \"Different modelers will choose different thresholds. The \", mdx(\"em\", {\n    parentName: \"p\"\n  }, mdx(\"a\", _extends({\n    parentName: \"em\"\n  }, {\n    \"href\": \"https://ig.ft.com/coronavirus-chart/?areas=usa&areas=gbr&cumulative=0&logScale=1&perMillion=0&values=deaths\",\n    \"target\": \"_blank\",\n    \"rel\": \"noreferrer\"\n  }), \"Financial Times\")), \" measures new cases or deaths by country from the day 10 cases/day or 3 deaths/day was reached, and cumulative cases/deaths from the day of the 100th case or death. The \", mdx(\"a\", _extends({\n    parentName: \"p\"\n  }, {\n    \"href\": \"http://91-divoc.com/pages/covid-visualization/\",\n    \"target\": \"_blank\",\n    \"rel\": \"noreferrer\"\n  }), \"data visualization at 91-divoc.com\"), \" measures by nation from the day of the 100th case or the 10th death; for regions, it starts at 20 cases or 5 deaths.\"), mdx(\"h2\", {\n    \"id\": \"what-threshold-makes-the-most-sense\"\n  }, \"What threshold makes the most sense?\"), mdx(\"p\", null, \"In our analysis, we examined the \", mdx(\"a\", _extends({\n    parentName: \"p\"\n  }, {\n    \"href\": \"https://github.com/CSSEGISandData/COVID-19/tree/master/csse_covid_19_data/csse_covid_19_time_series\",\n    \"target\": \"_blank\",\n    \"rel\": \"noreferrer\"\n  }), \"Johns Hopkins CSSE time series dataset\"), \" (up to 5/31/20) to determine how varying the choice of starting point affects the precision of forecasts. We chose to analyze counts of confirmed COVID fatalities, which are more reliable early-stage figures than confirmed COVID cases. (However, deaths always lag cases by up to two weeks.)\"), mdx(\"p\", null, \"First, we grouped U.S. county-level COVID death counts into the top 100 most populous Census metropolitan statistical areas (MSAs), which adhere to county boundaries. By population, the largest of these is New York-Newark-Jersey City and the smallest is Chattanooga.\"), mdx(\"p\", null, \"Next, we shifted the daily figures for the MSAs to synchronize the two weeks before and two weeks after the day \", mdx(\"em\", {\n    parentName: \"p\"\n  }, \"X\"), \" total deaths was reached, where \", mdx(\"em\", {\n    parentName: \"p\"\n  }, \"X\"), \" = 1, 2, 5, 10, 20, 50, 100, 200, 500, 1,000, and 2,000.\"), mdx(\"p\", null, mdx(\"em\", {\n    parentName: \"p\"\n  }, \"(Why these amounts? Contagions spread exponentially, and these numbers break the span from 1 to 2,000 into roughly equal logarithmic intervals. As of today, only three MSAs have reached 5,000 total deaths. And why two weeks? That is roughly the duration of a COVID infection.)\")), mdx(\"p\", null, \"To determine the efficacy of each threshold of \", mdx(\"em\", {\n    parentName: \"p\"\n  }, \"X\"), \", we created regressions for the \\u201Cbefore\\u201D and \\u201Cafter\\u201D weeks, based on the linear formula and its exponential transformation shown below:\"), 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\": \"1375px\"\n    }\n  }), \"\\n      \", mdx(\"span\", _extends({\n    parentName: \"span\"\n  }, {\n    \"className\": \"gatsby-resp-image-background-image\",\n    \"style\": {\n      \"paddingBottom\": \"14.399999999999999%\",\n      \"position\": \"relative\",\n      \"bottom\": \"0\",\n      \"left\": \"0\",\n      \"backgroundImage\": \"url('data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABQAAAADCAYAAACTWi8uAAAACXBIWXMAAA7DAAAOwwHHb6hkAAAAaUlEQVQI12WOSwqAMAwF041oF/VXFcSVqAsP1d7/Fk4gBdHCMJS+vFSEk3MWc51SuvEFB+wwGBus9hahJeve8/Ipc9DARDDgQikMttBjb65e879CDZwK4RF3Wg4L9xkrvWWD/TiWjtLzABCJOGODeCV/AAAAAElFTkSuQmCC')\",\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/c6ac1b1d9779498361dbb02cc978e5f5/0fffa/covid-19-critical-mass_equations.webp 1375w\"],\n    \"sizes\": \"(max-width: 1375px) 100vw, 1375px\",\n    \"type\": \"image/webp\"\n  })), \"\\n        \", mdx(\"source\", _extends({\n    parentName: \"picture\"\n  }, {\n    \"srcSet\": [\"/static/c6ac1b1d9779498361dbb02cc978e5f5/8ff9b/covid-19-critical-mass_equations.png 1375w\"],\n    \"sizes\": \"(max-width: 1375px) 100vw, 1375px\",\n    \"type\": \"image/png\"\n  })), \"\\n        \", mdx(\"img\", _extends({\n    parentName: \"picture\"\n  }, {\n    \"className\": \"gatsby-resp-image-image\",\n    \"src\": \"/static/c6ac1b1d9779498361dbb02cc978e5f5/8ff9b/covid-19-critical-mass_equations.png\",\n    \"alt\": \"Equations: log(deaths) = alpha + beta*days + epsilon. Deaths = lambda*e^(beta*days) + epsilon, lambda = e^alpha.\",\n    \"title\": \"Equations: log(deaths) = alpha + beta*days + epsilon. Deaths = lambda*e^(beta*days) + epsilon, lambda = e^alpha.\",\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  }, \"(Here,\"), \" \\u03B1 \", mdx(\"em\", {\n    parentName: \"p\"\n  }, \"and\"), \" \\u03B2 \", mdx(\"em\", {\n    parentName: \"p\"\n  }, \"are the y-intercept and slope of the regression and\"), \" \\u03B5 \", mdx(\"em\", {\n    parentName: \"p\"\n  }, \"is the residual error.)\")), mdx(\"p\", null, \"Below in Chart 1, see an interactive graph illustrating the data for \", mdx(\"em\", {\n    parentName: \"p\"\n  }, \"X\"), \" = 1 death (the day of the first recorded COVID death in each MSA).\"), mdx(\"iframe\", {\n    width: \"500\",\n    height: \"400\",\n    frameBorder: \"0\",\n    scrolling: \"no\",\n    src: \"//plotly.com/~GriffinK/3.embed\"\n  }), mdx(\"p\", null, mdx(\"em\", {\n    parentName: \"p\"\n  }, \"Chart 1. Rendered in R, ggplot2, and Plotly. Hover over points to see more information.\")), mdx(\"p\", null, \"When \", mdx(\"em\", {\n    parentName: \"p\"\n  }, \"X\"), \" = 1, the trajectories on the right (\", mdx(\"em\", {\n    parentName: \"p\"\n  }, \"after\"), \") side of the graph diverge widely, signifying that this observation point occurs too early in the spread of the virus to meaningfully predict or compare trajectories. Additionally, for this and the next few values of \", mdx(\"em\", {\n    parentName: \"p\"\n  }, \"X\"), \", any data that would appear on the left (\", mdx(\"em\", {\n    parentName: \"p\"\n  }, \"before\"), \") side of the graph are inscrutable, since counts of zero deaths have an infinitesimal logarithmic value and must therefore be discarded.\"), mdx(\"p\", null, \"When moving to higher thresholds of \", mdx(\"em\", {\n    parentName: \"p\"\n  }, \"X\"), \", we are able to see the trajectories on the \", mdx(\"em\", {\n    parentName: \"p\"\n  }, \"after\"), \" side begin to coalesce and the data on the \", mdx(\"em\", {\n    parentName: \"p\"\n  }, \"before\"), \" side start to grow. For the highest \", mdx(\"em\", {\n    parentName: \"p\"\n  }, \"X\"), \", few MSAs have yet reached those death counts, so the trajectories on both sides of the graph are much fewer in number. Here are selected values of \", mdx(\"em\", {\n    parentName: \"p\"\n  }, \"X\"), \", visualized:\"), mdx(\"iframe\", {\n    width: \"500\",\n    height: \"400\",\n    frameBorder: \"0\",\n    scrolling: \"no\",\n    src: \"//plotly.com/~GriffinK/5.embed\"\n  }), mdx(\"iframe\", {\n    width: \"500\",\n    height: \"400\",\n    frameBorder: \"0\",\n    scrolling: \"no\",\n    src: \"//plotly.com/~GriffinK/9.embed\"\n  }), mdx(\"iframe\", {\n    width: \"500\",\n    height: \"400\",\n    frameBorder: \"0\",\n    scrolling: \"no\",\n    src: \"//plotly.com/~GriffinK/15.embed\"\n  }), mdx(\"p\", null, mdx(\"em\", {\n    parentName: \"p\"\n  }, \"Charts 2-4. Rendered in R, ggplot2, and Plotly. Hover over points to see more information.\")), mdx(\"p\", null, \"Across all these test values of \", mdx(\"em\", {\n    parentName: \"p\"\n  }, \"X\"), \", the mean square error (MSE) of the observations to the \", mdx(\"em\", {\n    parentName: \"p\"\n  }, \"before\"), \" and \", mdx(\"em\", {\n    parentName: \"p\"\n  }, \"after\"), \" regression lines is what attracts our attention, more so than the regressions themselves. The MSE allows us to understand how closely the trajectories coalesce around the regression line, and thus around each other. A low MSE implies that the observations fit well around the line, indicating better predictability. For the first few values of \", mdx(\"em\", {\n    parentName: \"p\"\n  }, \"X\"), \", the MSE is lower on the \", mdx(\"em\", {\n    parentName: \"p\"\n  }, \"before\"), \" side than on the \", mdx(\"em\", {\n    parentName: \"p\"\n  }, \"after\"), \" side. For larger \", mdx(\"em\", {\n    parentName: \"p\"\n  }, \"X\"), \", the opposite is true: the \", mdx(\"em\", {\n    parentName: \"p\"\n  }, \"after\"), \" MSE is lower and the \", mdx(\"em\", {\n    parentName: \"p\"\n  }, \"before\"), \" MSE is higher.\"), mdx(\"p\", null, \"As \", mdx(\"em\", {\n    parentName: \"p\"\n  }, \"X\"), \" increases, the point at which the \", mdx(\"em\", {\n    parentName: \"p\"\n  }, \"after\"), \" MSE becomes less than the \", mdx(\"em\", {\n    parentName: \"p\"\n  }, \"before\"), \" MSE is where the near future becomes more predictable than the near past. In our data, this point occurs just shy of \", mdx(\"em\", {\n    parentName: \"p\"\n  }, \"X\"), \" = 50, as visualized in the graph below:\"), mdx(\"iframe\", {\n    width: \"500\",\n    height: \"300\",\n    frameBorder: \"0\",\n    scrolling: \"no\",\n    src: \"//plotly.com/~GriffinK/19.embed\"\n  }), mdx(\"p\", null, mdx(\"em\", {\n    parentName: \"p\"\n  }, \"Chart 5. Mean square error for each value of\"), \" X\", mdx(\"em\", {\n    parentName: \"p\"\n  }, \".\")), mdx(\"p\", null, \"The data shown in this graphic imply that the fatality trajectories have lost most of their early-stage variability around the time of the 50th death. After reaching this threshold, the trajectories behave more consistently \\u2014 not totally in lockstep, in fact far from it, but more consistently than at any point before.\"), mdx(\"p\", null, \"The widening difference between the \", mdx(\"em\", {\n    parentName: \"p\"\n  }, \"before\"), \" and \", mdx(\"em\", {\n    parentName: \"p\"\n  }, \"after\"), \" MSE curves beyond \", mdx(\"em\", {\n    parentName: \"p\"\n  }, \"X\"), \" = 50 indicates that 100, 200, or 500 might be even better thresholds, but we must bear in mind the passage of time. The higher we set \", mdx(\"em\", {\n    parentName: \"p\"\n  }, \"X\"), \" as the starting point, the more precision we gain in modeling the future, but the more data we are willingly discarding. We should not choose a starting point so late in the outbreak that we end up ignoring weeks of mid-phase growth in the fatality count for the sake of a more precise model.\"), mdx(\"p\", null, \"The steep decline in both MSE curves at \", mdx(\"em\", {\n    parentName: \"p\"\n  }, \"X\"), \" = 1,000 and 2,000 is an artifact of the rarity of those high death counts as of this month (June); data is simply too scarce for these \", mdx(\"em\", {\n    parentName: \"p\"\n  }, \"X\"), \". If more regions across the U.S. were suffering COVID fatality rates that severe, we could expect to see the \", mdx(\"em\", {\n    parentName: \"p\"\n  }, \"before\"), \" MSE curve trend further and further upwards.\"), mdx(\"h2\", {\n    \"id\": \"what-we-can-learn\"\n  }, \"What we can learn\"), mdx(\"p\", null, \"This analysis shows how the growth in COVID fatalities in U.S. urban regions reaches a \\u201Ccritical mass\\u201D and loses its early-stage variability at some time around the 50th death. Graphing MSE shows that setting a higher starting point for measurements enables greater precision, but this comes with the price of discarding informative data.\"), mdx(\"p\", null, \"This is an important finding for comparative analysis and future COVID time-series data visualizations. Yet, we must caution against overstating the rigor of this analysis. We are not epidemiologists and this is not a professional epidemiological study. We have regressed over the variable of time but not over variables of human behavior or system factors. Hopefully this analysis, albeit rough, can impart some mathematical basis to the assumptions underlying future analysis.\"), mdx(\"p\", null, \"Download our data tables for this post \", mdx(\"a\", _extends({\n    parentName: \"p\"\n  }, {\n    \"href\": \"https://github.com/civic-data-design-lab/COVID-critical-mass\",\n    \"target\": \"_blank\",\n    \"rel\": \"noreferrer\"\n  }), \"here\"), \".\"));\n}\n;\nMDXContent.isMDXComponent = 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