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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InfraNodus
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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.
COVID Live - Coronavirus Statistics - Worldometer 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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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 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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Coronavirus Case and death data will be inclusive of both confirmed and probable cases and deaths. The cumulative county COVID-19 case rate map has been replaced with a map https://www.michigan.gov/coronavirus
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Symptoms of COVID-19 | CDC COVID-19 is caused by infection with a coronavirus first identified in 2019, and flu is caused by infection with influenza viruses. https://www.cdc.gov/coronavirus/2019-ncov/symptoms-testing/symptoms.html
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Covid in the U.S.: Latest Maps, Case and Death Counts - The New ... Coronavirus in the U.S.: Latest Map and Case Count. Updated May 28, 2022. New reported cases. All time. Last 90 days. https://www.nytimes.com/interactive/2021/us/covid-cases.html
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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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Coronavirus Tax Relief and Economic Impact Payments | Internal ... Coronavirus Tax Relief · Child Tax Credit. The 2021 Child Tax Credit is up to $3,600 for each qualifying child. · IRS Mission-Critical Functions Continue. We https://www.irs.gov/coronavirus-tax-relief-and-economic-impact-payments
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Coronavirus in Pennsylvania Get all the latest, up-to-date information on COVID-19 from the Pennsylvania Department of Health. https://www.health.pa.gov/topics/disease/coronavirus/Pages/Coronavirus.aspx
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Coronavirus Disease 2019 (COVID-19) May 19, 2022 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.state.tx.us/coronavirus/
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Coronavirus Disease (COVID-19) | SSA About Coronavirus Disease (COVID-19) and Social Security services and help during the pandemic. https://www.ssa.gov/coronavirus/
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Coronavirus (COVID-19) Response | FEMA.gov Coronavirus (COVID-19) Response. In alignment with President Biden's COVID-19 response plan, FEMA will work with the CDC and other federal agencies to https://www.fema.gov/disaster/coronavirus
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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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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 in humans. Building on previous research on https://www.niaid.nih.gov/diseases-conditions/coronaviruses
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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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Coronavirus (COVID-19) | US EPA View the latest information from EPA and find resources related to Coronavirus (COVID-19). https://www.epa.gov/coronavirus
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Louisiana Coronavirus | La Dept. of Health COVID-19 Information · Questions about Coronavirus? · Feeling stressed or anxious? We're here to talk. · Need a listening ear before a crisis occurs? https://ldh.la.gov/Coronavirus/
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Assistance for State, Local, and Tribal Governments | U.S. ... Through the Coronavirus Relief Fund, the CARES Act provides for payments to State, Local, and Tribal governments navigating the impact of the COVID-19 outbreak. https://home.treasury.gov/policy-issues/coronavirus/assistance-for-state-local-and-tribal-governments
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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 in Pennsylvania Get all the latest, up-to-date information on COVID-19 from the Pennsylvania Department of Health. https://www.health.pa.gov/topics/disease/coronavirus/Pages/Coronavirus.aspx
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Connecticut COVID-19 Portal: Information, Resources and Assistance Picture of a coronavirus. Hospital Capacity Data Have more questions about Coronavirus? Ask the CT Virtual Assistant now: https://portal.ct.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 Ohio Dr. Bertley, COSI's president, visits an Ohio barbershop to discuss immune system response to COVID-19. To learn more visit coronavirus.ohio.gov. 4/15/22. https://coronavirus.ohio.gov/home
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COVID-19 | Department of Health If you test positive, stay home and talk to your healthcare provider about treatment. Masks are still required in healthcare facilities, nursing homes, https://coronavirus.health.ny.gov/home
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| coronavirus New DC Government sponsored vaccination clinics are posted daily, or visit one of the District's 8 COVID Centers. Booster doses are now available for children 5 https://coronavirus.dc.gov/
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Coronavirus: Latest news and breaking stories | NBC News A deadly coronavirus, which causes respiratory illness and pneumonia, is spreading around the world. https://www.nbcnews.com/health/coronavirus
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What Is Coronavirus? | Johns Hopkins Medicine Feb 24, 2022 Coronaviruses are a type of virus. There are many different kinds, and some cause disease. A coronavirus identified in 2019, SARS-CoV-2, https://www.hopkinsmedicine.org/health/conditions-and-diseases/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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Novel Coronavirus (COVID-19): Home Indiana's Novel Coronavirus Response. On March 6, 2020, the state Department of Health confirmed Indiana's first case of COVID-19, a novel respiratory virus https://www.coronavirus.in.gov/
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Coronavirus News and Latest Updates The latest news and updates on the coronavirus outbreak from CNBC's global teams in Asia, Europe and the U.S.. https://www.cnbc.com/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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| Washington State Coronavirus Response (COVID-19) For testing inquiries or results, please contact your health care provider. You can also text the word “Coronavirus” to 211-211 to receive information and https://coronavirus.wa.gov/
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Coronavirus Pandemic: COVID-19 Pandemic News | AP News Keep up with coronavirus pandemic updates with AP News. Don't miss the latest COVID-19 pandemic news in the US and internationally. https://apnews.com/hub/coronavirus-pandemic
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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 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 One of the most important ways we have to combat the spread of illness like coronavirus is through education and information from reliable sources. https://rivcoph.org/coronavirus
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Coronavirus | Virginia.gov 2 days ago Coronavirus (COVID-19) in Virginia. COVID-19 Guidance. COVID-19 Action Plan & Executive Order #11 - Governor Glenn Youngkin announced January https://www.virginia.gov/coronavirus/
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The Coronavirus Crisis | The New Yorker The Coronavirus Crisis. Coverage of the COVID-19 outbreak, from the science of testing and treatment to the fight for herd immunity. https://www.newyorker.com/tag/coronavirus
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Welcome to Novel Coronavirus (COVID-19) **”Fully vaccinated” includes people who have received two doses of Moderna or Pfizer vaccines or a single dose of Janssen vaccine. ***A booster dose is https://coronavirus.idaho.gov/
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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:
⤓ ? ⤓
Use the Bert AI model for English, Dutch, German, French, Spanish and Italian to get better results (slower). Standard model is based on a fixed AFINN dictionary and works faster, but for English only.
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Main Topical Groups:
please, add your data to display the stats...
+ ⤓ ? ⤓ 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 using the Force Atlas algorithm (Jacomy et al) 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 using the Force Atlas algorithm (Jacomy et al) and are given a distinct color.
Most Influential Elements:
please, add your data to display the stats...
+ ⤓ ↻ ?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.
Network Structure:
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?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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Action Advice:
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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
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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
Use the Bert AI model for English, Dutch, German, French, Spanish and Italian to get better results (slower). Standard model is based on a fixed AFINN dictionary and works faster, but for English only.
Text Statistics:
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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).