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328px"}}},"originalPath":"/authors/authors-yuan-lai","skip":6,"limit":6,"group":[{"id":"147bb4d2-b665-5684-896e-b62b873ed51e","slug":"/unpacking-covid-19-publication-research-themes-and-urban-indications-(part-ii)","secret":false,"title":"Unpacking COVID-19 publication research themes and urban indications (Part II)","author":"Yuan Lai","date":"July 1st, 2020","dateForSEO":"2020-07-01T00:00:00.000Z","timeToRead":4,"excerpt":"My  previous blog post  explored  COVID-19 AI OPEN Research Dataset Challenge  manuscript data and topic modeling technique for thematic…","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 II)\",\n  \"author\": \"Yuan Lai\",\n  \"date\": \"2020-07-01T00:00:00.000Z\",\n  \"tags\": [\"Covid\", \"Visualization\", \"Data\", \"Health\"],\n  \"hero\": \"images/network_overlay_100.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, \"My \", mdx(\"a\", _extends({\n    parentName: \"p\"\n  }, {\n    \"href\": \"https://blog.civicdatadesignlab.mit.edu/unpacking-covid-19-publication-research-themes-and-urban-indications-(part-i)\",\n    \"target\": \"_blank\",\n    \"rel\": \"noreferrer\"\n  }), \"previous blog post\"), \" explored \", 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\"), \" manuscript data and topic modeling technique for thematic structure discovery. This exploratory data analysis (EDA) identified three major themes: epidemiology, virology, clinical studies, and unidentified topics. Using this output, we built a \", mdx(\"a\", _extends({\n    parentName: \"p\"\n  }, {\n    \"href\": \"https://public.tableau.com/profile/yuan5273#!/vizhome/COVID-19OpenResearchViz/Dashboard1\",\n    \"target\": \"_blank\",\n    \"rel\": \"noreferrer\"\n  }), \"dashboard\"), \" to visualize manuscripts grouped by country with the quantified thematic composition (in percentage) for exploring scientific research. The topic modeling results revealed several insights: of all the manuscripts, 36% were related to \\u201Cclinical\\u201D studies (looking for a treatment), 27% to \\u201Cvirological\\u201D studies (understanding the biology of the virus), 19% to \\u201Cepidemiological\\u201D studies (understanding the spread of the virus), and 18% to other research studies\"), mdx(\"p\", null, \"Quantifying and visualizing the current COVID-19 research landscape is interesting, but how does it reflect non-clinical factors and intersect with urban science? To explore this question, we used cleaned abstract text data to further analyze the co-occurrence of keywords that are relevant to urban science. The formation of this keyword dictionary proceeds in two steps. First, we examined the top words ranked by their appearance in the abstracts of every paper. These words are the most common terms that are related to identified themes. Because \\u201Cepidemiological\\u201D studies relate most to urban science and that urban science may represent a small portion of the entire COVID-19 research scope, we then added urban-related vocabulary into the dictionary, such as \\u201Cplanning\\u201D, \\u201Chousing\\u201D, \\u201Csocioeconomic\\u201D, and \\u201Ctransportation\\u201D. By analyzing the inter-correlation between these two groups of words, we may understand how urban-related terms appear in scientific research manuscripts and underlying interconnections between health and urban factors.\"), 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\": \"1392px\"\n    }\n  }), \"\\n      \", mdx(\"span\", _extends({\n    parentName: \"span\"\n  }, {\n    \"className\": \"gatsby-resp-image-background-image\",\n    \"style\": {\n      \"paddingBottom\": \"61.7816091954023%\",\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/79727b32404579b5d040c905b7639e36/a8feb/screen-shot-2020-07-01-at-7.34.01-pm.webp 1392w\"],\n    \"sizes\": \"(max-width: 1392px) 100vw, 1392px\",\n    \"type\": \"image/webp\"\n  })), \"\\n        \", mdx(\"source\", _extends({\n    parentName: \"picture\"\n  }, {\n    \"srcSet\": [\"/static/79727b32404579b5d040c905b7639e36/3baca/screen-shot-2020-07-01-at-7.34.01-pm.png 1392w\"],\n    \"sizes\": \"(max-width: 1392px) 100vw, 1392px\",\n    \"type\": \"image/png\"\n  })), \"\\n        \", mdx(\"img\", _extends({\n    parentName: \"picture\"\n  }, {\n    \"className\": \"gatsby-resp-image-image\",\n    \"src\": \"/static/79727b32404579b5d040c905b7639e36/3baca/screen-shot-2020-07-01-at-7.34.01-pm.png\",\n    \"alt\": \"screen shot 2020 07 01 at 7 34 01 pm\",\n    \"title\": \"Figure 1. A dictionary with COVID-19 and urban-related vocabulary.\",\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, \"With this dictionary, we then used network analysis to investigate how these terms are connected. The concept of text network analysis derives from graph theory. By associating pairs of words based on their co-appearance in a text file, network visualizations reveal complex structures and underlying patterns. Computationally, we used Sklearn in Python to convert the cleaned corpus (from Part I) into a matrix of token counts. The first output is aNbyNmatrix, where N is the number of unique vocabulary extracted from all texts. Because this matrix will be huge when analyzing thousands of abstracts a subset was extracted which represents the matrix related to the defined dictionary for the final graph visualization.\"), mdx(\"p\", null, mdx(\"img\", _extends({\n    parentName: \"p\"\n  }, {\n    \"src\": \"/5523373a1b88423a1de05084c8be844c/network_growth_animation.gif\",\n    \"alt\": null,\n    \"title\": \"Figure 2. An animated COVID-19 text network visualization.\"\n  }))), mdx(\"p\", null, \"Now let\\u2019s look into this graph step-by-step based on the rendering completeness of graph representation. In general, graph rendering with lower completeness highlights a fundamental structure of the graph. Increasing rendering completeness extends the network complexity by including more nuanced nodes and edges. The weight of an edge, visualized as the width, indicates the prevalence of co-occurrence between any two terms. A graph with 20% completeness reveals core connections (or research interests) focusing on infection and patient, as well as some clinical and epidemiological research compared to \\u201CSARS\\u201D and \\u201CInfluenza.\\u201D Multiple urban-related terms are included here, such as \\u201Cpoverty\\u201D, \\u201Cjob\\u201D, \\u201Cmobility\\u201D, and \\u201Cpolicy\\u201D. Some terms such as design and response, may carry different urban or clinical meanings depending on specific context.\"), 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\": \"2212px\"\n    }\n  }), \"\\n      \", mdx(\"span\", _extends({\n    parentName: \"span\"\n  }, {\n    \"className\": \"gatsby-resp-image-background-image\",\n    \"style\": {\n      \"paddingBottom\": \"59.76491862567812%\",\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/bdbc0523df844521267497d91108bfa2/8c820/network_overlay_20.webp 2212w\"],\n    \"sizes\": \"(max-width: 2212px) 100vw, 2212px\",\n    \"type\": \"image/webp\"\n  })), \"\\n        \", mdx(\"source\", _extends({\n    parentName: \"picture\"\n  }, {\n    \"srcSet\": [\"/static/bdbc0523df844521267497d91108bfa2/91fce/network_overlay_20.png 2212w\"],\n    \"sizes\": \"(max-width: 2212px) 100vw, 2212px\",\n    \"type\": \"image/png\"\n  })), \"\\n        \", mdx(\"img\", _extends({\n    parentName: \"picture\"\n  }, {\n    \"className\": \"gatsby-resp-image-image\",\n    \"src\": \"/static/bdbc0523df844521267497d91108bfa2/91fce/network_overlay_20.png\",\n    \"alt\": \"network overlay 20\",\n    \"title\": \"Figure 3. COVID-19 text network visualization with 20% completeness.\",\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, \"The graph with 60% completeness shows how different health conditions relate to other health, behavioral, socioeconomic, and policy factors. Some additional words indicate socioeconomic vulnerability, such as \\u201Csocial, \\u201Cincome,\\u201D \\u201Cjob,\\u201D \\u201Cinsurance,\\u201D and \\u201Cunemployment \\u201CAsthma,\\u201D one of the top respiratory pre-existing conditions, is associated with indoor living quality, air pollution, and insurance status. \\u201CObesity,\\u201D as another comorbidity found in COVID-19 patients, is associated with \\u201Cdrinking,\\u201D \\u201Cunemployment,\\u201D \\u201Cpoverty,\\u201D and \\u201Cmobility.\\u201D\"), 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\": \"2212px\"\n    }\n  }), \"\\n      \", mdx(\"span\", _extends({\n    parentName: \"span\"\n  }, {\n    \"className\": \"gatsby-resp-image-background-image\",\n    \"style\": {\n      \"paddingBottom\": \"59.76491862567812%\",\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/337aaa6dca07abdc2e5396bc5adb7476/8c820/network_overlay_60.webp 2212w\"],\n    \"sizes\": \"(max-width: 2212px) 100vw, 2212px\",\n    \"type\": \"image/webp\"\n  })), \"\\n        \", mdx(\"source\", _extends({\n    parentName: \"picture\"\n  }, {\n    \"srcSet\": [\"/static/337aaa6dca07abdc2e5396bc5adb7476/91fce/network_overlay_60.png 2212w\"],\n    \"sizes\": \"(max-width: 2212px) 100vw, 2212px\",\n    \"type\": \"image/png\"\n  })), \"\\n        \", mdx(\"img\", _extends({\n    parentName: \"picture\"\n  }, {\n    \"className\": \"gatsby-resp-image-image\",\n    \"src\": \"/static/337aaa6dca07abdc2e5396bc5adb7476/91fce/network_overlay_60.png\",\n    \"alt\": \"network overlay 60\",\n    \"title\": \"Figure 4. COVID-19 text network visualization with 60% completeness.\",\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, \"The graph with 100% completeness reveals broader racial disparities and racism during this pandemic. \", mdx(\"a\", _extends({\n    parentName: \"p\"\n  }, {\n    \"href\": \"https://www.scientificamerican.com/article/why-racism-not-race-is-a-risk-factor-for-dying-of-covid-191/\",\n    \"target\": \"_blank\",\n    \"rel\": \"noreferrer\"\n  }), \"A recent interview with public health specialist and physician Camara Phyllis Jones\"), \" revealed that occupations, communities, and health care leave people of color, especially Black Americans, more exposed and less protected. The data visualizations we produced resonate with her insights on systemic racism and its impact on almost every aspect of peoples\\u2019 lives. The words\\u201Cneighborhood,\\u201D \\u201Cenvironment,\\u201D \\u201Chospital,\\u201D \\u201Cgovernment \\u201Cmobility,\\u201D \\u201Cplanning,\\u201D and \\u201Cdesign\\u201D imply inequities in community resources and living conditions. Others imply population health disparities, especially in pre-existing conditions such as \\u201Cobesity,\\u201D \\u201Cdiabetes,\\u201D and \\u201Casthma.\\u201D The Asian population, on the other hand, is associated explicitly with \", mdx(\"a\", _extends({\n    parentName: \"p\"\n  }, {\n    \"href\": \"https://items.ssrc.org/covid-19-and-the-social-sciences/the-rise-of-anti-asian-hate-in-the-wake-of-covid-19/\",\n    \"target\": \"_blank\",\n    \"rel\": \"noreferrer\"\n  }), \"racial discimination, negative sentiments on social media, and hate crimes\"), \". This indicates that people of color in different ethnic groups may face different discimination and injustice. Additional words may look nuanced in the entire graph, but represent specific population groups, such as \\u201Chomeless\\u201D and frontline\\u201D workers.\"), 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\": \"2212px\"\n    }\n  }), \"\\n      \", mdx(\"span\", _extends({\n    parentName: \"span\"\n  }, {\n    \"className\": \"gatsby-resp-image-background-image\",\n    \"style\": {\n      \"paddingBottom\": \"59.76491862567812%\",\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/0904d0c65999b35e30602cc297e3d935/8c820/network_overlay_100.webp 2212w\"],\n    \"sizes\": \"(max-width: 2212px) 100vw, 2212px\",\n    \"type\": \"image/webp\"\n  })), \"\\n        \", mdx(\"source\", _extends({\n    parentName: \"picture\"\n  }, {\n    \"srcSet\": [\"/static/0904d0c65999b35e30602cc297e3d935/91fce/network_overlay_100.png 2212w\"],\n    \"sizes\": \"(max-width: 2212px) 100vw, 2212px\",\n    \"type\": \"image/png\"\n  })), \"\\n        \", mdx(\"img\", _extends({\n    parentName: \"picture\"\n  }, {\n    \"className\": \"gatsby-resp-image-image\",\n    \"src\": \"/static/0904d0c65999b35e30602cc297e3d935/91fce/network_overlay_100.png\",\n    \"alt\": \"network overlay 100\",\n    \"title\": \"Figure 5. COVID-19 text network visualization with 100% completeness.\",\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, \"While the goal of this study was to unpack the complex research landscape, there are limitations when considering data sources as a collection of scientific research manuscripts. This partially explains why we see relatively weak links among the terms representing social, economic, demographic, and political factors. To some extent, we suspect that broader social science studies on COVID-19 may be underrepresented in the original pool of manuscripts and preprints. Novel information mining, data analytics, and knowledge discovery methods are needed for more in-depth investigation for more comprehensive understandings in this pandemic.\"), mdx(\"p\", null, \"In conclusion, this project used Natural Language Processing and topic modeling to identify the underlying thematic structure based on approximate 47,731 manuscripts. Network analysis and visualization of keywords further revealed the intertwined virological, clinical, and epidemiological complexities involving the spread, infection, and mortality of this infectious disease. Using text mining and graph theory, we built a network of critical terms that are relevant to both this pandemic and urban science. During this pandemic, while different groups of people may face different hardships and threats, the underlying network structure and patterns of academic manuscripts reveal prevalent inequalities and injustice that contribute to disease vulnerability and exposure risks.\"), mdx(\"p\", null, mdx(\"strong\", {\n    parentName: \"p\"\n  }, \"References\")), mdx(\"p\", null, \"Brainard, J., 2020. New tools aim to tame pandemic paper tsunami.Science. Available: \", mdx(\"a\", _extends({\n    parentName: \"p\"\n  }, {\n    \"href\": \"https://science.sciencemag.org/content/368/6494/924/tab-pdf\",\n    \"target\": \"_blank\",\n    \"rel\": \"noreferrer\"\n  }), \"https://science.sciencemag.org/content/368/6494/924/tab-pdf\")), mdx(\"p\", null, \"Feagin, J. and Bennefield, Z., 2014. Systemic racism and US health care.Social science & medicine, 103, pp.7-14.\"), mdx(\"p\", null, \"Gray, D.M., Anyane-Yeboa, A., Balzora, S., Issaka, R.B. and May, F.P., 2020. COVID-19 and the other pandemic: populations made vulnerable by systemic inequity.Nature Reviews Gastroenterology & Hepatology, pp.1-3.\"), mdx(\"p\", null, \"Hutson, M. 2020. Artificial-intelligence tools aim to tame the coronavirus literature.Nature. Available: \", mdx(\"a\", _extends({\n    parentName: \"p\"\n  }, {\n    \"href\": \"https://www.nature.com/articles/d41586-020-01733-7\",\n    \"target\": \"_blank\",\n    \"rel\": \"noreferrer\"\n  }), \"https://www.nature.com/articles/d41586-020-01733-7\")), mdx(\"p\", null, \"Laster Pirtle, W.N., 2020. Racial capitalism: a fundamental cause of novel coronavirus (COVID-19) pandemic inequities in the United States. Health Education & Behavior, p.1090198120922942.\"), mdx(\"p\", null, \"Lee, J. and Yadav M. 2020. The Rise of Anti-Asian Hate in the Wake of Covid-19. Insights from the Social Sciences. Available: \", mdx(\"a\", _extends({\n    parentName: \"p\"\n  }, {\n    \"href\": \"https://items.ssrc.org/covid-19-and-the-social-sciences/the-rise-of-anti-asian-hate-in-the-wake-of-covid-19/\",\n    \"target\": \"_blank\",\n    \"rel\": \"noreferrer\"\n  }), \"https://items.ssrc.org/covid-19-and-the-social-sciences/the-rise-of-anti-asian-hate-in-the-wake-of-covid-19/\")));\n}\n;\nMDXContent.isMDXComponent = 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_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. We hope this data visualization can support researchers who are interested in cities and further connect scientific insights with local community actions.\"));\n}\n;\nMDXContent.isMDXComponent = true;","hero":{"full":{"base64":"data:image/png;base64,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","aspectRatio":1.7352941176470589,"src":"/static/0588e03c5c47c7e8fed63df5e468ee76/a1946/dashboard.png","srcSet":"/static/0588e03c5c47c7e8fed63df5e468ee76/5b37e/dashboard.png 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