CORD-19-Sentences  

CORD-19:ab8c488ab6d262aaaa2dda1f953257c5af588064 JSONTXT 7 Projects

Annnotations TAB TSV DIC JSON TextAE

Id Subject Object Predicate Lexical cue
TextSentencer_T1 0-118 Sentence denotes Spatial Visualization of Cluster-Specific COVID-19 Transmission Network in South Korea During the Early Epidemic Phase
TextSentencer_T2 120-128 Sentence denotes Abstract
TextSentencer_T3 129-251 Sentence denotes Coronavirus disease 2019 (COVID-19) has been rapidly spreading throughout China and other countries including South Korea.
TextSentencer_T4 252-340 Sentence denotes As of March 12, 2020, a total number of 7,869 cases and 66 deaths had been documented in
TextSentencer_T5 341-480 Sentence denotes Using spatial visualization, this paper identified two early transmission clusters in South Korea (Daegu cluster and capital area cluster).
TextSentencer_T6 481-611 Sentence denotes Using a degree-weighted centrality measure, this paper proposes potential super-spreaders of the virus in the visualized clusters.
TextSentencer_T7 612-919 Sentence denotes Compared to various epidemiological measures such as the basic reproduction number, spatial visualizations of the cluster-specific transmission networks and the proposed centrality measure may be more useful to characterize super-spreaders and the spread of the virus especially in the early epidemic phase.
TextSentencer_T8 921-1007 Sentence denotes The first pneumonia cases of unknown origin were identified in Wuhan in early December
TextSentencer_T9 1008-1056 Sentence denotes China and other countries including South Korea.
TextSentencer_T10 1057-1172 Sentence denotes As of March 17, 2020, a total of 198,181 laboratory-confirmed cases had been documented globally with 7,965 deaths.
TextSentencer_T11 1173-1348 Sentence denotes The World Health Organization (WHO) has declared COVID-19 an international public health concern.2 The confirmed patients in South Korea had either visited or came from China.
TextSentencer_T12 1349-1482 Sentence denotes Secondary and tertiary transmissions have occurred since then, which have led to an accelerating rate of transmission in South Korea.
TextSentencer_T13 1483-1584 Sentence denotes As of March 17, 2020, a total number of 8,320 cases and 81 deaths had been documented in South Korea.
TextSentencer_T14 1585-1823 Sentence denotes With the launch of COVID-19 data hub, officials from the White House and other national organizations issued a call to action for researchers in a multitude of disciplines such as computer science, epidemiology, economics, and statistics.
TextSentencer_T15 1824-2463 Sentence denotes Open access data such as epidemiological data, interactive web-based dashboards, and descriptive statistics have informed many about the current state of the pandemic.3,4 With a concomitant effort to combat the virus and to better understand virus etiologies, Korea Centers for Disease Control and Prevention (KCDC), an organization under the South Korean Ministry of Welfare and Health, has made many datasets available online that are unique to COVID-19 confirmed South Korea cases.5 The datasets only include confirmed COVID-19 patients with unique numeric patient identifiers, geographical data, and infection information if available.
TextSentencer_T16 2464-2601 Sentence denotes In an epidemiological dataset, they released the region of the affected patient, the identifier of the person who infected the patient, .
TextSentencer_T17 2602-2750 Sentence denotes CC-BY 4.0 International license It is made available under a author/funder, who has granted medRxiv a license to display the preprint in perpetuity.
TextSentencer_T18 2751-2940 Sentence denotes is the (which was not peer-reviewed) The copyright holder for this preprint . https://doi.org/10.1101/2020.03.18.20038638 doi: medRxiv preprint and the number of contacts with other people.
TextSentencer_T19 2941-3131 Sentence denotes The aim of this report is to create spatial visualizations of early COVID-19 transmission networks in South Korea using these data, which may indicate transmission patterns for each network.
TextSentencer_T20 3132-3229 Sentence denotes The time series data of COVID-19 status in South Korea is analyzed to provide updated statistics.
TextSentencer_T21 3230-3348 Sentence denotes Using a spatial visualization of confirmed patients during an early epidemic phase, two major clusters are identified.
TextSentencer_T22 3349-3501 Sentence denotes As of March 12, 7,869 positive cases had been documented in South Korea, and 70 positive cases have information of the identifiers of who infected them.
TextSentencer_T23 3502-3729 Sentence denotes Although the first confirmed case in South Korea was identified on January 20, 2020, the number of confirmed cases showed a rapid growth on February 19, 2020 with a total number of 1,261 cases with 12 deaths based on the KCDC.6
TextSentencer_T24 3730-3894 Sentence denotes As of March, newly reported cases in South Korea show that the numbers of positive cases and deaths seem to be declining and new cases remain within known clusters.
TextSentencer_T25 3895-4013 Sentence denotes Therefore, identifying early clusters and examining the confirmed cases in these early clusters, from January 20, 2020
TextSentencer_T26 4014-4121 Sentence denotes to February 19, 2020 are crucial because these clusters remain the longest lasting sources of transmission.
TextSentencer_T27 4122-4396 Sentence denotes Out of 70 patients, only a subset of patients infected from confirmed cases from an early epidemic phase (January 20, 2020 to February 19, 2020) is used to create the network from the epidemiological data to further visualize the transmission networks of these two clusters.
TextSentencer_T28 4397-4503 Sentence denotes All the analysis and visualizations are performed using the ggplot2 software in R as well as Cytoscape.7,8
TextSentencer_T29 4504-4505 Sentence denotes .
TextSentencer_T30 4506-4654 Sentence denotes CC-BY 4.0 International license It is made available under a author/funder, who has granted medRxiv a license to display the preprint in perpetuity.
TextSentencer_T31 4655-4798 Sentence denotes is the (which was not peer-reviewed) The copyright holder for this preprint . https://doi.org/10.1101/2020.03.18.20038638 doi: medRxiv preprint
TextSentencer_T32 4799-4969 Sentence denotes The time series data contains both overall statistics such as the number of tests as well as geographical data within South Korea from January 20, 2020 to March 12, 2020.
TextSentencer_T33 4970-5172 Sentence denotes Figure 1 shows the time series data of the cumulative COVID-19 statistics from January 20, 2020 to March 12, 2020. author/funder, who has granted medRxiv a license to display the preprint in perpetuity.
TextSentencer_T34 5173-5391 Sentence denotes is the (which was not peer-reviewed) The copyright holder for this preprint . https://doi.org/10.1101/2020.03.18.20038638 doi: medRxiv preprint probability distributions, which further complicates the interpretability.
TextSentencer_T35 5392-5493 Sentence denotes Instead, visualizing the transmission networks could be useful to understand the spread of the virus.
TextSentencer_T36 5494-5650 Sentence denotes Although it may be clear that the 31st case in the Daegu cluster is a super-spreader, it is uncertain who is the super-spreader in the capital area cluster.
TextSentencer_T37 5651-5940 Sentence denotes Out of 15 distinct cases in the network, nine cases have reported degrees in the dataset (number of contacts), which allows the use of centrality algorithms to understand the role of particle nodes in a graph and their impact on this transmission network. denotes the degree of the ℎ case.
TextSentencer_T38 5941-6053 Sentence denotes Since six nodes are missing degrees, the population average degree is used to impute missing degree information.
TextSentencer_T39 6054-6216 Sentence denotes We define a population degree , which is calculated after imputing missing degrees with an assumption that every node in the network is independent of each other.
TextSentencer_T40 6217-6494 Sentence denotes Table 1 shows the number of degrees for each case before and after imputation. case number degree degree imputed 3 16 16 6 17 17 10 43 43 11 0 0 21 6 6 28 1 1 29 117 117 30 27 27 56 32 83 32 112 32 136 32 362 32 1257 32 1913 61 61 Table 1 .
TextSentencer_T41 6495-6561 Sentence denotes Number of degrees in the capital cluster before after imputation .
TextSentencer_T42 6562-6710 Sentence denotes CC-BY 4.0 International license It is made available under a author/funder, who has granted medRxiv a license to display the preprint in perpetuity.
TextSentencer_T43 6711-7104 Sentence denotes is the (which was not peer-reviewed) The copyright holder for this preprint . https://doi.org/10.1101/2020.03.18.20038638 doi: medRxiv preprint Betweenness centrality is another graph centrality measure that captures the influence of a node over the flow of information between every pair of nodes in the network with the assumption that information flows over the shortest paths between them.
TextSentencer_T44 7105-7152 Sentence denotes Between centrality ( ) for a node is defined as
TextSentencer_T45 7153-7292 Sentence denotes where is the number of shortest paths with edges and as their end edges while ( ) is the number of those shortest paths that include node .
TextSentencer_T46 7293-7496 Sentence denotes 11 We propose a degree-weighted betweenness centrality ( ), which prioritizes nodes with high degrees while penalizing them by ( ) = ∑ ( ) ≠ ≠ * to capture the super-spreader in the capital area network.
TextSentencer_T47 7497-7641 Sentence denotes By looking at betweenness centrality only, the 6th case who transmitted the virus to five distinct cases has the highest betweenness centrality.
TextSentencer_T48 7642-7772 Sentence denotes However, a degree-weighted measure indicates that the 29th case with a much larger degree is the most central node in the network.
TextSentencer_T49 7773-7906 Sentence denotes This metric may be useful for small networks with limited information to identify super-spreaders in the early transmission networks.
TextSentencer_T50 7907-8130 Sentence denotes What happened in China shows that quarantine, social distancing, and isolation of infected populations may be able to contain the epidemic.12 This is encouraging for the many countries where COVID-19 is beginning to spread.
TextSentencer_T51 8131-8207 Sentence denotes South Korea once had the fastest growing rate of infection outside of China.
TextSentencer_T52 8208-8335 Sentence denotes Korea's confirmed cases have risen rapidly since the identification of the super node in the Daegu cluster since late February.
TextSentencer_T53 8336-8459 Sentence denotes Since then, the country has shown success in its mitigation efforts in both the number of newly confirmed cases and deaths.
TextSentencer_T54 8460-8618 Sentence denotes The majority of new cases originate from those original clusters, one of which is likely a superspreader, which is suggested by the spatial network generated.
TextSentencer_T55 8619-8959 Sentence denotes Similar observations were seen during the Middle East respiratory syndrome (MERS) in South Korea where the syndrome was spread rapidly by super-spreaders.13 Therefore, it is important to have a better understanding of these clusters during the early epidemic phase, and visualizing them may help us understand how the virus is being spread.
TextSentencer_T56 8960-9185 Sentence denotes Spatial networks can visualize early transmission clusters, and the proposed degree-weighted betweenness centrality measure can further help identify super-spreaders in the identified clusters, which may not only reduce the .
TextSentencer_T57 9186-9334 Sentence denotes CC-BY 4.0 International license It is made available under a author/funder, who has granted medRxiv a license to display the preprint in perpetuity.
TextSentencer_T58 9335-9586 Sentence denotes is the (which was not peer-reviewed) The copyright holder for this preprint . https://doi.org/10.1101/2020.03.18.20038638 doi: medRxiv preprint spread of the virus but may also help with policymaking such as enforced social distancing or quarantining.
TextSentencer_T59 9587-9588 Sentence denotes .
TextSentencer_T60 9589-9737 Sentence denotes CC-BY 4.0 International license It is made available under a author/funder, who has granted medRxiv a license to display the preprint in perpetuity.
TextSentencer_T61 9738-9881 Sentence denotes is the (which was not peer-reviewed) The copyright holder for this preprint . https://doi.org/10.1101/2020.03.18.20038638 doi: medRxiv preprint