Coronavirus Coronavirus disease (COVID-19) is an infectious disease caused by the SARS-CoV-2 virus. Most people infected with the virus will experience mild to moderate ... https://www.who.int/health-topics/coronavirus
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Top keywords (global influence):
Top topics (local contexts):
Explore the main topics and terms outlined above or see them in the excerpts from this text below.
See the relevant data in context: click here to show the excerpts from this text that contain these topics below.
Tip: use the form below to save the most relevant keywords for this search query. Or start writing your content and see how it relates to the existing search queries and results.
Tip: here are the keyword queries that people search for but don't actually find in the search results.
Coronavirus Disease 2019 (COVID-19) | CDC Find links to guidance and information on all topics related to COVID-19, including the COVID-19 vaccine, symptom self-check, data, and other topics. https://www.cdc.gov/coronavirus/2019-ncov/index.html
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COVID Live Update: 255,824,843 Cases and 5,141,615 Deaths from ... Live statistics and coronavirus news tracking the number of confirmed cases, recovered patients, tests, and death toll due to the COVID-19 coronavirus from ... https://www.worldometers.info/coronavirus/
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Coronavirus - Coronavirus Call the COVID-19 Hotline at 888-535-6136. Email COVID19@michigan.gov. MDHHS Epidemic Orders · FACE MASK RECOMMENDATIONS ... https://www.michigan.gov/coronavirus/
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COVID-19 Map - Johns Hopkins Coronavirus Resource Center Coronavirus COVID-19 Global Cases by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University (JHU) https://coronavirus.jhu.edu/map.html
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Coronavirus Tax Relief and Economic Impact Payments | Internal ... Oct 7, 2021 ... ... families, businesses, tax-exempt organizations and others – including health plans – affected by coronavirus (COVID-19). https://www.irs.gov/coronavirus-tax-relief-and-economic-impact-payments
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COVID-19 | Ohio.gov ... Health will continue to share COVID-19 cases, hospitalizations, deaths, current trends, key metrics, and vaccination data daily at coronavirus.ohio.gov. https://coronavirus.ohio.gov/wps/portal/gov/covid-19/home
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ArcGIS Dashboards ArcGIS Dashboards. https://gisanddata.maps.arcgis.com/apps/opsdashboard/index.html#/bda7594740fd40299423467b48e9ecf6
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Coronavirus News and updates about the coronavirus pandemic: Cases in the US, death toll, what you need to know about the virus, how to prepare, how to get tested. https://www.washingtonpost.com/coronavirus/
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Connecticut COVID-19 Response Have more questions about Coronavirus? Ask the CT Virtual Assistant now: (833) 250-7633. Or call the. 2-1-1 Connecticut Hotline. https://portal.ct.gov/coronavirus
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Connecticut COVID-19 Response Have more questions about Coronavirus? Ask the CT Virtual Assistant now: (833) 250-7633. Or call the. 2-1-1 Connecticut Hotline. https://portal.ct.gov/coronavirus
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Covid in the U.S.: Latest Map and Case Count - The New York Times Coronavirus in the U.S.: Latest Map and Case Count. Updated Nov. 17, 2021. New reported cases. All time. Last 90 days. https://www.nytimes.com/interactive/2021/us/covid-cases.html
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ISDH - Novel Coronavirus: Novel Coronavirus (COVID-19) Sign up here to get the latest novel coronavirus (COVID-19) news and updates from the Indiana State Department of Health. No Thanks Remind Me Later. https://coronavirus.in.gov/
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| coronavirus Mar 16, 2020 ... The vaccine is safe and lowers the chance of children getting and spreading COVID-19. The vaccine is 90% effective at preventing symptoms of ... https://coronavirus.dc.gov/
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Coronavirus Disease 2019 (COVID-19) 2 days ago ... A new coronavirus (2019-nCoV) was recently detected in Wuhan City, Hubei Province, China and is causing an outbreak of respiratory illness. https://www.dshs.texas.gov/coronavirus/
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WHO Coronavirus (COVID-19) Dashboard World Health Organization Coronavirus disease situation dashboard presents official daily counts of COVID-19 cases and deaths worldwide, ... https://covid19.who.int/
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Coronavirus - Maryland Department of Health Visit the Maryland Department of Health's official resource for the Coronavirus Disease 2019 (COVID-19) outbreak. https://coronavirus.maryland.gov/
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COVID-19 | USAGov Get coronavirus health information and learn where to get tested. COVID-19 Small Business Loans and Assistance. COVID-19 loans, debt relief, and grants can help ... https://www.usa.gov/coronavirus
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Coronavirus in Pennsylvania Coronavirus disease (COVID-19) is an infectious disease caused by a newly discovered coronavirus. Most people infected with the COVID-19 virus will ... https://www.health.pa.gov/topics/disease/coronavirus/Pages/Coronavirus.aspx
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What Is Coronavirus? | Johns Hopkins Medicine Coronaviruses are a type of virus. There are many different kinds, and some cause disease. A coronavirus identified in 2019, SARS-CoV-2, has caused a ... https://www.hopkinsmedicine.org/health/conditions-and-diseases/coronavirus
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Coronavirus & COVID-19 Overview: Symptoms, Risks, Prevention ... Oct 28, 2021 ... What Is COVID-19? A coronavirus is a kind of common virus that causes an infection in your nose, sinuses, or upper throat. Most coronaviruses ... https://www.webmd.com/lung/coronavirus
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Coronavirus Disease 2019 (COVID-19) | SCDHEC Coronavirus Disease 2019 (COVID-19). English | Español. Every person who's fully vaccinated puts us one step closer to ending the pandemic. https://scdhec.gov/covid19
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Tracking Covid-19 cases in the US Track the spread of coronavirus in the United States with maps and updates on cases and deaths. https://www.cnn.com/interactive/2020/health/coronavirus-us-maps-and-cases/
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COVID-19.CA.gov Official website for California Coronavirus (COVID-19) Response daily updates and resources. Your actions save lives. Find information and services to help ... https://covid19.ca.gov/
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COVID-19 in Virginia - Coronavirus Official website for Virginia Coronavirus (COVID-19) Data, information and additional resources. Find out how to get your free COVID-19 vaccination. https://www.vdh.virginia.gov/coronavirus/
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Novel Coronavirus (COVID-19) | Department of Health The COVID-19 vaccine is safe, effective and will help protect eligible children and adolescents ages 5 – 17. https://coronavirus.health.ny.gov/home
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COVID-19 Media Page - LA County Department of Public Health The virus that causes COVID-19 is a novel coronavirus that was first identified during an investigation into an outbreak in Wuhan, China and is now ... http://publichealth.lacounty.gov/media/Coronavirus/
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coronavirus Riverside County COVID-19 Indicators (Updated Daily M-F) Map/Geographic Data and Reports (Updated Mondays). Reports. Summary Reports, Special Reports ... https://rivcoph.org/coronavirus
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Coronavirus | United Nations Coronaviruses (CoV) are a large family of viruses that cause illness ranging from the common cold to more severe diseases. Find out more about this novel ... https://www.un.org/en/coronavirus
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coronavirus | Keeping Utah Informed on the Latest Coronavirus ... Coronavirus.utah.gov Navigation. Home Page · Health Guidance Levels; Updates. All Utah Updates · Case Counts · Legislative Timeline · Vaccine. https://coronavirus.utah.gov/
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COVID-19 Emergency Relief and Federal Student Aid | Federal ... Coronavirus.gov—The Centers for Disease Control and Prevention offers this site, which features everything from prevention tips, common symptoms, ... https://studentaid.gov/announcements-events/covid-19
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About List N: Disinfectants for Coronavirus (COVID-19) | US EPA Sep 7, 2021 ... EPA expects all products on List N to kill the coronavirus SARS-CoV-2 (COVID-19) when used according to the label directions. https://www.epa.gov/coronavirus/about-list-n-disinfectants-coronavirus-covid-19-0
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Coronavirus Disease 2019 County of San Diego public health information on COVID-19. Case data, testing sites, health order, guidance on reopening and gatherings. https://www.sandiegocounty.gov/coronavirus.html
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Coronavirus Resources | U.S. Department of Labor Watch coronavirus videos on Labor Department guidance and resources. covid icon Workplace Safety. Report a workplace safety issue at www.osha ... https://www.dol.gov/coronavirus
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Coronavirus All New Yorkers should get tested for COVID-19, whether or not you have symptoms or are at increased risk. NYC Health Department: coronavirus disease 2019 ( ... https://www1.nyc.gov/site/coronavirus/index.page
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Coronavirus disease 2019 (COVID-19) - Symptoms and causes ... Coronaviruses are a family of viruses that can cause illnesses such as the common cold, severe acute respiratory syndrome (SARS) and Middle East respiratory ... https://www.mayoclinic.org/diseases-conditions/coronavirus/symptoms-causes/syc-20479963
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Coronaviruses | NIH: National Institute of Allergy and Infectious ... Coronaviruses are a large family of viruses that usually cause mild to moderate upper-respiratory tract illnesses, like the common cold. https://www.niaid.nih.gov/diseases-conditions/coronaviruses
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Coronavirus Disease (COVID-19) | Occupational Safety and Health ... Coronavirus Disease (COVID-19). Workers from various industries wearing masks. Learn about the new Vaccination and Testing Emergency Temporary Standard ... https://www.osha.gov/coronavirus
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Coronavirus Pandemic (COVID-19) - Statistics and Research - Our ... Coronavirus Country Profiles. We built 207 country profiles which allow you to explore the statistics on the coronavirus pandemic for every country in the world ... https://ourworldindata.org/coronavirus
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Covid-19 Economic Relief | U.S. Department of the Treasury Coronavirus State and Local Fiscal Recovery Funds. The American Rescue Plan provides $350 billion in emergency funding for eligible state, local, ... https://home.treasury.gov/policy-issues/coronavirus
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The higher is the network's structure diversity and the higher is the alpha in the influence propagation score, the higher is its mind-viral immunity — that is, such network will be more resilient and adaptive than a less diverse one.
In case of a discourse network, high mind-viral immunity means that the text proposes multiple points of view and propagates its influence using both highly influential concepts and smaller, secondary topics.
In case of a discourse network, high mind-viral immunity means that the text proposes multiple points of view and propagates its influence using both highly influential concepts and smaller, secondary topics.
The higher is the diversity, the more distinct communities (topics) there are in this network, the more likely it will be pluralist.
The network structure indicates the level of its diversity. It is based on the modularity measure (>0.4 for medium, >0.65 for high modularity, measured with Louvain (Blondel et al 2008) community detection algorithm) in combination with the measure of influence distribution (the entropy of the top nodes' distribution among the top clusters), as well as the the percentage of nodes in the top community.
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Narrative Influence Propagation:
? The chart above shows how influence propagates through the network. X-axis: lemma to lemma step (narrative chronology). Y-axis: change of influence.
The more even and rhythmical this propagation is, the stronger is the central idea or agenda (see alpha exponent below ~ 0.5 or less).
The more variability can be seen in the propagation profile, the less is the reliance on the main concepts (agenda), the stronger is the role of secondary topical clusters in the narrative.
The more even and rhythmical this propagation is, the stronger is the central idea or agenda (see alpha exponent below ~ 0.5 or less).
The more variability can be seen in the propagation profile, the less is the reliance on the main concepts (agenda), the stronger is the role of secondary topical clusters in the narrative.
We plot the narrative as a time series of influence (using the words' betweenness score). We then apply detrended fluctuation analysis to identify fractality of this time series, plotting the log2 scales (x) to the log2 of accumulated fluctuations (y). If the resulting loglog relation can be approximated on a linear polyfit, there may be a power-law relation in how the influence propagates in this narrative over time (e.g. most of the time non-influential words, occasionally words with a high influence).
Using the alpha exponent of the fit (which is closely related to Hurst exponent)), we can better understand the nature of this relation: uniform (pulsating | alpha <= 0.65), variable (stationary, has long-term correlations | 0.65 < alpha <= 0.85), fractal (adaptive | 0.85 < alpha < 1.15), and complex (non-stationary | alpha >= 1.15).
For maximal diversity, adaptivity, and plurality, the narrative should be close to "fractal" (near-critical state). For fiction, essays, and some forms of poetry — "uniform". Informative texts will often have "variable + stationary" score. The "complex" state is an indicator that the text is always shifting its state.
Using the alpha exponent of the fit (which is closely related to Hurst exponent)), we can better understand the nature of this relation: uniform (pulsating | alpha <= 0.65), variable (stationary, has long-term correlations | 0.65 < alpha <= 0.85), fractal (adaptive | 0.85 < alpha < 1.15), and complex (non-stationary | alpha >= 1.15).
For maximal diversity, adaptivity, and plurality, the narrative should be close to "fractal" (near-critical state). For fiction, essays, and some forms of poetry — "uniform". Informative texts will often have "variable + stationary" score. The "complex" state is an indicator that the text is always shifting its state.
Degree Distribution:
? (based on kolmogorov-smirnov test) ? switch to linear
Using this information, you can identify whether the network has scale-free / small-world (long-tail power law distribution) or random (normal, bell-shaped distribution) network properties.
This may be important for understanding the level of resilience and the dynamics of propagation in this network. E.g. scale-free networks with long degree tails are more resilient against random attacks and will propagate information across the whole structure better.
This may be important for understanding the level of resilience and the dynamics of propagation in this network. E.g. scale-free networks with long degree tails are more resilient against random attacks and will propagate information across the whole structure better.
If a power-law is identified, the nodes have preferential attachment (e.g. 20% of nodes tend to get 80% of connections), and the network may be scale-free, which may indicate that it's more resilient and adaptive. Absence of power law may indicate a more equalized distribution of influence.
Kolmogorov-Smirnov test compares the distribution above to the "ideal" power-law ones (^1, ^1.5, ^2) and looks for the best fit. If the value d is below the critical value cr it is a sign that the both distributions are similar.
Kolmogorov-Smirnov test compares the distribution above to the "ideal" power-law ones (^1, ^1.5, ^2) and looks for the best fit. If the value d is below the critical value cr it is a sign that the both distributions are similar.
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We provide LDA stats for comparison purposes only. It works with English-language texts at the moment. More languages are coming soon, subscribe @noduslabs to be informed.
positive: | negative: | neutral:
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Sentiment analysis works for English language only. Contact us @noduslabs to propose a language and to get updated about the new features.
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Main Topical Groups:
N/A
+ ⤓ ? ⤓The topics are the nodes (words) that tend to co-occur together in the same context (next to each other).
We use a combination of clustering and graph community detection algorithm (Blondel et al based on Louvain) to identify the groups of nodes are more densely connected together than with the rest of the network. They are aligned closer to each other on the graph and are given a distinct color.
We use a combination of clustering and graph community detection algorithm (Blondel et al based on Louvain) to identify the groups of nodes are more densely connected together than with the rest of the network. They are aligned closer to each other on the graph and are given a distinct color.
Most Influential Elements:
N/A
+ ⤓ ↻ ?We use the Jenks elbow cutoff algorithm to select the top prominent nodes that have significantly higher influence than the rest.
Click the Reveal Non-obvious button to remove the most influential words (or the ones you select) from the graph, to see what terms are hiding behind them.
The most influential nodes are either the ones with the highest betweenness centrality — appearing most often on the shortest path between any two randomly chosen nodes (i.e. linking the different distinct communities) — or the ones with the highest degree.
Click the Reveal Non-obvious button to remove the most influential words (or the ones you select) from the graph, to see what terms are hiding behind them.
The most influential nodes are either the ones with the highest betweenness centrality — appearing most often on the shortest path between any two randomly chosen nodes (i.e. linking the different distinct communities) — or the ones with the highest degree.
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Structural Gap
(ask a research question that would link these two topics):N/A
?A structural gap shows the two distinct communities (clusters of words) in this graph that are important, but not yet connected. That's where the new potential and innovative ideas may reside.
This measure is based on a combination of the graph's connectivity and community structure, selecting the groups of nodes that would either make the graph more connected if it's too dispersed or that would help maintain diversity if it's too connected.
This measure is based on a combination of the graph's connectivity and community structure, selecting the groups of nodes that would either make the graph more connected if it's too dispersed or that would help maintain diversity if it's too connected.
Latent Topical Brokers
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N/A
?These are the latent brokers between the topics: the nodes that have an unusually high rate of influence (betweenness centrality) to their freqency — meaning they may appear not as often as the most influential nodes but they are important narrative shifting points.
These are usually brokers between different clusters / communities of nodes, playing not easily noticed and yet important role in this network, like the "grey cardinals" of sorts.
These are usually brokers between different clusters / communities of nodes, playing not easily noticed and yet important role in this network, like the "grey cardinals" of sorts.
Emerging Keywords
N/A
Evolution of Topics
(frequency / time) ?
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The chart shows how the main topics and the most influential keywords evolved over time. X-axis: time period (split into 10% blocks). Y-axis: cumulative frequency of occurrence.
Drag the slider to see how the narrative evolved over time. Select the checkbox to recalculate the metrics at every step (slower, but more precise).
Drag the slider to see how the narrative evolved over time. Select the checkbox to recalculate the metrics at every step (slower, but more precise).
Main Topics
(according to Latent Dirichlet Allocation):loading...
⤓ ? ⤓LDA stands for Latent Dirichlet Allocation — it is a topic modelling algorithm based on calculating the maximum probability of the terms' co-occurrence in a particular text or a corpus.
We provide this data for you to be able to estimate the precision of the default InfraNodus topic modeling method based on text network analysis.
We provide this data for you to be able to estimate the precision of the default InfraNodus topic modeling method based on text network analysis.
Most Influential Words
(main topics and words according to LDA):loading...
We provide LDA stats for comparison purposes only. It works with English-language texts at the moment. More languages are coming soon, subscribe @noduslabs to be informed.
Sentiment Analysis
positive: | negative: | neutral:
⤓ ? ⤓
We analyze the sentiment of each statement to see whether it's positive, negative, or neutral. You can filter the statements by sentiment (clicking above) and see what kind of topics correlate with every mood.
The approach is based on AFINN and Emoji Sentiment Ranking
The approach is based on AFINN and Emoji Sentiment Ranking
Sentiment analysis works for English language only. Contact us @noduslabs to propose a language and to get updated about the new features.
Network Statistics:
Top Relations / Bigrams
(both directions):
The most prominent relations between the nodes that exist in this graph are shown above. We treat the graph as undirected by default as it allows us to better detect general patterns.
As an option, you can also downloaded directed bigrams above, in case the direction of the relations is important (for any application other than language).
As an option, you can also downloaded directed bigrams above, in case the direction of the relations is important (for any application other than language).