Corpus downloaded from PubMed July 6th: 28256 publications. There were 1732 keywords that occurred in five or more manuscripts. These divided into five main clusters. To create the map, we used the LinLog method.

Table of the most central keywords in each cluster

(Centrality measure used is weighted degree centrality/total link strength)

Cluster description in progress

To open the map directly in VOSviewer, please use these the latest MAP and NET files, available through the Open Science Foundation (OSF) repository:
https://osf.io/54gqw/ 

Corpus downloaded from PubMed June 29th: 26187 publications. There were 1789 keywords that occurred in five or more manuscripts. These divided into five main clusters. To create the map, we used the LinLog method.

Table of the most central keywords in each cluster

(Centrality measure used is weighted degree centrality/total link strength)

Cluster description in progress

To open the map directly in VOSviewer, please use these the latest MAP and NET files, available through the Open Science Foundation (OSF) repository:
https://osf.io/54gqw/ 

Corpus downloaded from PubMed June 22nd: 24167 publications. There were 1656 keywords that occurred in five or more manuscripts. These divided into five main clusters. To create the map, we used the LinLog method.

Table of the most central keywords in each cluster

(Centrality measure used is weighted degree centrality/total link strength)

Cluster description in progress

To open the map directly in VOSviewer, please use these the latest MAP and NET files, available through the Open Science Foundation (OSF) repository:
https://osf.io/54gqw/ 

Corpus downloaded from PubMed June 15th: 22147 publications. There were 1535 keywords that occurred in five or more manuscripts. These divided into five main clusters. To create the map, we used the LinLog method.

Table of the most central keywords in each cluster

(Centrality measure used is weighted degree centrality/total link strength)

Cluster description in progress

To open the map directly in VOSviewer, please use these two files.
KeywordCoOccurrance5Inst1Resol1_20200615NETb
KeywordCoOccurrance5Inst1Resol1_20200615MAPb

The maps are created with the following “thesaurus” to clean the data:
Thesaurus20200601

Corpus downloaded from PubMed June 8th: 19426 publications. There were 1814 keywords that occurred in four or more manuscripts. These divided into five main clusters. To create the map, we used the LinLog method.

Table of the most central keywords in each cluster

(Centrality measure used is weighted degree centrality/total link strength)

Cluster description in progress

To open the map directly in VOSviewer, please use these two files.
KeywordCoOccurrance4Inst1Resol1_20200608NET
KeywordCoOccurrance4Inst1Resol1_20200608MAP

The maps are created with the following “thesaurus” to clean the data:
Thesaurus20200601

Corpus downloaded from PubMed 1st June: 17346 publications. There were 1648 keywords that occurred in four or more manuscripts. These divided into five main clusters.

Table of the most central keywords in each cluster

(Centrality measure used is weighted degree centrality/total link strength)

Cluster description in progress

To open the map directly in VOSviewer, please use these two files.
CoOccurrance4Inst1Resol1_20200601MAP
CoOccurrance4Inst1Resol1_20200601NET

The maps are created with the following “thesaurus” to clean the data:
Thesaurus20200601

Corpus downloaded from PubMed 25th May: 15438 publications. There were 1515 keywords that occurred in four or more manuscripts. These divided into five main clusters.

Table of the most central keywords in each cluster

(Centrality measure used is weighted degree centrality)

Cluster description in progress

To open the map directly in VOSviewer, please use these two files.
KeywordCoOccurrance4Inst1Resol20200525MAP
KeywordCoOccurrance4Inst1Resol20200525NET

The maps are created with the following “thesaurus” to clean the data:
KeywordCoOccurrance_20200427_Thesaurus

Corpus downloaded from PubMed 18th May: 13103 publications. There were 1861 keywords that occurred in three or more manuscripts. These divided into four main clusters.

This week’s rapport strengthens our observation from last couple of weeks that the clusters and topics seem to vary less, signifying that cluster may be more robust. This is true even if cluster 1 and 2 have changed places. Maybe this switch could imply that research and publication is switching more to how to managing the disease, than mainly be focused on the understanding of the phenomenon. In cluster 3 the term(s) (hydroxyl)chloroquine is  rising on the list. Last week there was a fifth cluster that we called “clinical issues”, but the terms included in this cluster have returned to the third cluster where they originated.

Cluster 1 could be called “disease epidemiology” and includes terms like pneumonia, epidemiology, outbreak, infection, infectious diseases, novel coronavirus pneumonia, treatment and transmission. This cluster contains 2 major topics: a) the main term “the disease” which in addition to the above mentioned terms, but also b) “inpatient care” which contains terms like ARDS, critical illness and airway management. In this cluster some smaller topics are also present.

Cluster 2 could be called «global virus pandemic» and includes terms (beta)coronavirus infection, viral pneumonia, pandemic/epidemic, public health, infection control and quarantine. This cluster contains 3 main topics a) “preventive epidemiology”, which in addition to the terms public health and quarantine contains terms like disease outbreak, travel and contact tracing, b) “care management” which includes terms like infection control, practice guidelines and health personnel and c) “vulnerability” that contains terms like mental health, psychological stress and health service accessibility.

Cluster 3 could be called “pathophysiology and origin” and includes terms like SARS, animals, zoonosis, antiviral agents, viral genome, phylogeny and chiroptera. This cluster contains to major topics: a) “viral biology” which in addition to terms like SARS, animals, viral genome and phylogeny includes terms like spike glycoprotein and peptidyl-dipeptidase a and b) “drug treatment” which in addition to the term antiviral agent, (hydroxyl)chloroquine and chiroptera contains terms like traditional Chinese medicine and lopinavir.

Cluster 4 could be named “clinical epidemiology” and contains terms like mortality, age, gender, clinical laboratory techniques and fever. Also in this cluster, there is only this major topic. Smaller topics include “population care”, “serology” and “pregnancy”.

Table of the most central keywords in each cluster

(Centrality measure used is weighted degree centrality)

Cluster description in progress

To open the map directly in VOSviewer, please use these two files.

KeywordCoOccurrance3Inst1Resol20200518MAP
KeywordCoOccurrance3Inst1Resol20200518NET

The maps are created with the following “thesaurus” to clean the data:
KeywordCoOccurrance_20200427_Thesaurus

Corpus downloaded from PubMed 11th May: 11005 publications. There were 1634 keywords that occurred in three or more manuscripts. These divided into four main clusters.

Table of the most central keywords in each cluster

(Centrality measure used is weighted degree centrality)

To open the map directly in VOSviewer, please use these two files.

KeywordCoOccurrance_20200511_NET
KeywordCoOccurrance_20200511_MAP

The maps are created with the following “thesaurus” to clean the data:
KeywordCoOccurrance_20200427_Thesaurus

Corpus downloaded from PubMed 4th May: 8791 publications. There were 1381 keywords that occurred in three or more manuscripts. These divided into five main clusters.

Cluster descriptions

In progress..

Table of the most central keywords in each cluster

(Centrality measure used is weighted degree centrality)

To open the map directly in VOSviewer, please use these two files.

KeywordCoOccurrance_20200505_MAP

KeywordCoOccurrance_20200505_NET

The maps are created with the following “thesaurus” to clean the data:
KeywordCoOccurrance_20200427_Thesaurus

Corpus downloaded from PubMed 27th April: 6958 publications. There were 1082 keywords that occurred in three or more manuscripts. These divided into four main clusters.

Cluster descriptions

This week’s rapport confirms what we suspected last week, that the clusters and topics seem to vary less. Clusters 1, 3 and 4 were quite stable, while cluster 2 saw a bit of change, mostly in the addition of a new topic (“inpatient care”). New from this week is that we in cluster 4 mention 3 upcoming topics with increasing number of publications. Finally, the last cluster (cluster 5) seemed to take a bit different course, mostly because most of the terms here moved to be included in cluster 1 in a new topic. Cluster 5 now is more about the disease.

Cluster 1 could be called «global virus pandemic» and includes terms like (beta)coronavirus infection, viral pneumonia, disease outbreak and public health. This cluster contains 3 major topics: a) “preventive epidemiology”, which in addition to the terms like disease outbreak and public health includes terms like travel, epidemic, global heath and civil defence, b) “care management” which includes terms like infection control, practice guidelines and health personnel and c) “vulnerability” that contains terms like mental health, psychological stress and health service accessibility.

Cluster 2 could be called “disease epidemiology” and includes terms like pneumonia, epidemiology, outbreak, infection, infectious diseases, novel coronavirus pneumonia, treatment and transmission. This cluster contains 2 major topics: a) the main term “the disease” which in addition to the above mentioned terms, but also b) “inpatient care” which contains terms like ARDS, critical illness and airway management. In this cluster some smaller topics are also present.

Cluster 3 could be called “pathophysiology and origin” and includes terms like SARS, animals, zoonosis, antiviral agents, viral genome, phylogeny and chiroptera. This cluster contains to major topics: a) “viral biology” which in addition to terms like SARS, animals, viral genome and phylogeny includes terms like spike glycoprotein and peptidyl-dipeptidase a and b) “drug treatment” which in addition to the term antiviral agent and chiroptera contains terms like traditional Chinese medicine, chloroquine and lopinavir.

Cluster 4 could be named “clinical epidemiology” and contains terms like mortality, age, gender, clinical laboratory techniques and fever. Also in this cluster, there is only this major topic. Smaller topics include “population care”, “serology” and “pregnancy”.

Cluster 5 could be named “clinical issues” and contains terms like cardiovascular diseases, hypertension, angiotensin-converting enzyme inhibitors and diabetes mellitus. This is the smallest cluster with only one topic.

Table of the most central keywords in each cluster

(Centrality measure used is weighted degree centrality)

VOSViewer files:

To open the map directly in VOSviewer, please use these two files.

KeywordCoOccurrance_20200427_MAP
KeywordCoOccurrance_20200427_NET

The maps are created with the following “thesaurus” to clean the data:
KeywordCoOccurrance_20200427_Thesaurus

Corpus downloaded from PubMed 20th April: 5228 publications. There were 853 keywords that occurred in three or more manuscripts. These divided into four main clusters.

Cluster descriptions

Cluster 1 could be called «A global virus pandemic» and includes terms like (beta)coronavirus infection, viral pneumonia, disease outbreak and public health. This cluster contains 2 major topics: a) “preventive epidemiology”, which in addition to the terms like disease outbreak and public health includes terms like travel, global heath and civil defence and b) “care management” which includes terms like infection control, practice guidelines and health personnel

Cluster 2 could be called “disease epidemiology” and includes terms like epidemiology, outbreak, infection, infectious diseases, novel coronavirus pneumonia, treatment and transmission. This is a small cluster with only this major topic

Cluster 3 could be called “pathophysiology and origin” and includes terms like SARS, animals, zoonosis, antiviral agents, viral genome, phylogeny and chiroptera. This cluster contains to major topics: a) “viral biology” which in addition to terms like SARS, animals, viral genome, phylogeny and chiroptera includes terms like spike glycoprotein and peptidyl-dipeptidase a and b) “drug treatment” which in addition to the term antiviral agent contains terms like traditional Chinese medicine, chloroquine and lopinavir

Cluster 4 could be named “clinical epidemiology” and contains terms like mortality, age, gender, clinical laboratory techniques and fever. Also in this cluster, there is only this major topic.

Cluster 5 could be named “related topics” and contains terms like mental health and anxiety and depression one side , but also terms like surveys and questionnaires, cross-sectional studies and health knowledge; attitudes and practice on the other. This is the smallest cluster with only one topic

Table of ten most central keywords in each cluster

(Centrality measure used is weighted degree centrality)

VOSViewer files:

To open the map directly in VOSviewer, please use these two files.

CoOccurrance3Inst1Resol20thApril20NET

CoOccurrance3Inst1Resol20thApril20MAP

The maps are created with the following “thesaurus” to clean the data: Thesaurus13thApril

Corpus downloaded from PubMed 13th April: 3683 publications. There were 607 keywords that occurred in three or more manuscripts. These divided into four main clusters.

Cluster descriptions

Cluster descriptions

Cluster 1 (red) relates to “Health and pandemic management”, and include topics like the epidemic, the impact on global health, the public health, infection control and health personnel. Within this cluster, we found the topic of “Global health politics”, which in addition to terms on the epidemic, the impact on global health and public health included terms like population surveillance, civil defense and risk assessment. Further, within this cluster, we found the topic of “Pandemic management”, which in addition to the terms infection control and health personnel included terms like practice guidelines and patient isolation.

Cluster 2 (green) related to “The disease and its treatment”, including topics like its pneumonia and diagnosis, but also topics like antiviral drugs and artificial respiration. Within this cluster, we found the topic of “Clinical picture”, which in addition to terms on pneumonia and diagnosis, included terms like corona virus and infectious disease. Further, within this cluster, we find the topic of “Drug treatment”, which in addition to the terms antiviral drugs and artificial respiration, includes terms like lopinavir, alanine, chloroquine and clinical trials.

Cluster 3 (blue) related to the “The virus and its epidemiology”, including terms like SARS, it being a zoonosis, its genome and the outbreak in Wuhan. Two major topics were identified within this cluster: “Clinical epidemiology”, which in addition to it being a zoonosis and broke out in Wuhan, includes terms like transmission and basic reproduction rate. The other major topic identified is on the “Virus biology”, which in addition to the terms SARS and genome, included terms like phylogeny, glycoproteins and antibodies.

Cluster 4 (yellow) related to “Clinical epidemiology”, with terms relating to age, gender, and basic clinical signs and procedures (like x-ray, CT, fever).

Table of ten most central keywords in each cluster

(Centrality measure used is weighted degree centrality)

VOSViewer files:

To open the map directly in VOSviewer, please use these two files.

CoOccurrance3Inst1Resol13thApril20MAP

CoOccurrance3Inst1Resol13thApril20NET

The maps are created with the following “thesaurus” to clean the data: Thesaurus13thApril

Keyword Co-Occurrence Network Graph for the Overall Research Field on COVID-19 up to April 6th, 2020.

Corpus downloaded from PubMed 6th April: 2684 publications. There were 453 keywords that occurred in three or more manuscripts. These divided into four main clusters.

Cluster descriptions

Cluster descriptions

Cluster 1 (red) relates to “Health and pandemic management”, and include topics like the epidemic, the impact on global health, the public health, infection control and health personnel. Within this cluster, we found the topic of “Global health politics”, which in addition to terms on the epidemic, the impact on global health and public health included terms like population surveillance, civil defense and risk assessment. Further, within this cluster, we found the topic of “Pandemic management”, which in addition to the terms infection control and health personnel included terms like practice guidelines and patient isolation.

Cluster 2 (green) related to “The disease and its treatment”, including topics like its pneumonia and diagnosis, but also topics like antiviral drugs and artificial respiration. Within this cluster, we found the topic of “Clinical picture”, which in addition to terms on pneumonia and diagnosis, included terms like corona virus and infectious disease. Further, within this cluster, we find the topic of “Drug treatment”, which in addition to the terms antiviral drugs and artificial respiration, includes terms like lopinavir, alanine, chloroquine and clinical trials.

Cluster 3 (blue) related to the “The virus and its epidemiology”, including terms like SARS, it being a zoonosis, its genome and the outbreak in Wuhan. Two major topics were identified within this cluster: “Clinical epidemiology”, which in addition to it being a zoonosis and broke out in Wuhan, includes terms like transmission and basic reproduction rate. The other major topic identified is on the “Virus biology”, which in addition to the terms SARS and genome, included terms like phylogeny, glycoproteins and antibodies.

Cluster 4 (yellow) related to “Clinical epidemiology”, with terms relating to age, gender, and basic clinical signs and procedures (like x-ray, CT, fever).

Table of ten most central keywords in each cluster

(Centrality measure used is weighted degree centrality)

VOSViewer files:

To open the map directly in VOSviewer, please use these two files.

CoOccurrance3Inst1Resol_6April20MAP

CoOccurrance3Inst1Resol_6April20NET

The maps are created with the following “thesaurus” to clean the data:  Thesaurus6April

Corpus downloaded from PubMed 30th March: 1788 publications. There were 308 keywords that occurred in three or more manuscripts. These divided into four clusters.

Cluster descriptions

Cluster 1 (red) relates to “Health and pandemic management”, and include topics like the pandemic, the impact on global health, the disease outbreak, infection control and travel. Within this cluster, we found the topic of “Global health politics”, which in addition to terms on the pandemic and the impact on global health included terms on countries, public health, disease transmission and health planning. Further, within this cluster, we found the topic of “Pandemic politics”, which in addition to the terms disease outbreak and infection control included terms like mass screening and health personnel.

Cluster 2 (green) related to “The disease and its pathophysiology” of COVID-19, including topics like its genome and its relationship with SARS, but also topics like epidemiology and its outbreak and that it is a zoonosis. Within this cluster, we found the topic of “Viral biology”, which in addition to terms on its genome and SARS, included terms like phylogeny and disease reservoirs. Further, within this cluster, we find the topic of “Viral spread”, which in addition to the term epidemiology, includes terms like quarantine, importation and incubation period. Lastly, this cluster contained the topic “Basic clinical medicine”, which in addition to the terms outbreak and zoonosis, included terms like transmission and mortality.

Cluster 3 (blue) related to the “Clinical epidemiology of the disease”, including topics like age (aged, children, adolescent) and gender (male and female), risk factor, population surveillance and pregnancy. Only one major topic was identified within this cluster: “Clinical characteristics”. In addition to the terms on age and gender, this topic included terms like prognosis, myalgia, biomarkers and laboratory medicine.

Cluster 4 (yellow) related to “Treatment of the disease”, with terms like antiviral agents, diagnosis, ritonavir and drug combinations.

Table of ten most central keywords in each cluster

(Centrality measure used is weighted degree centrality)

VOSViewer files:

To open the map directly in VOSviewer, please use these two files.

CoOccurrance3Inst1Resol30March20MAP

CoOccurrance3Inst1Resol30March20NET

The maps are created with the following “thesaurus” to clean the data:  Thesaurus 23 March

Corpus downloaded from PubMed 23rd March: 1323 publications. There were 225 keywords that occurred in three or more manuscripts. These divided into four clusters.

Table of ten most central keywords in each cluster

(Centrality measure used is weighted degree centrality)

VOSViewer files:

To open the map directly in VOSviewer, please use these two files.

CoOccurrance3Inst1Resol23March20MAP

CoOccurrance3Inst1Resol23March20NET

The maps are created with the following “thesaurus” to clean the data:  Thesaurus 23 March

Corpus downloaded from PubMed 16th March: 992 publications)

The table below provides an overview of the most central keywords from each of the clusters in the keyword co-occurrence network graph above.

Table of most central keywords in each cluster from the network graph: