PMC:7062829 / 1429-8743 JSON TXT

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LitCovid-PubTator

LitCovid-PD-FMA-UBERON

{"project":"LitCovid-PD-FMA-UBERON","denotations":[{"id":"T1","span":{"begin":2098,"end":2102},"obj":"Body_part"}],"attributes":[{"id":"A1","pred":"fma_id","subj":"T1","obj":"http://purl.org/sig/ont/fma/fma9712"}],"text":"Introduction\nTransmission of infectious disease through contact among individuals increases the risk of outbreaks with epidemic potential. However, understanding how diseases spread over networks of contacts remains a challenge. In particular, outbreaks of potentially devastating infections, such as SARS (2003), Ebola (2014–2015), and Zika (2015–2016), have shown that the dynamics behind the spread of disease has become more complex, limiting our ability to predict and control epidemics. In this regard, patterns of disease transmission should be used to design specific public health strategies to enhance sustainable capacity while building activities to improve government responses to infectious diseases. Therefore, an analysis of disease dynamics based on the contact patterns can be used to build practical guidance while framing disease prevention and management strategies.\nThe interpersonal contact patterns of disease transmissions have often been discussed in a network context while modelling epidemics1–4. Most potential disease contact takes place in localized communities among individuals occupying a local geographic space around the diseased. If such contacts are repeated within a given period, certain patterns of links will arise. These link patterns can be represented as networks, which show the spread of an infection among individuals. Thus, certain disease dynamics represented in a contact network can be characterised by topologies.\nPrevious studies on super-spreaders have identified two major types of networks, small-world network5 and scale-free network6. In the small-world network, a small number of shortcuts are discovered either by randomly connecting the nodes or randomly rewiring the links. From the shortcuts, it can be inferred that the average node length between any two individuals is shortened, thereby making geographic distance a causal factor in epidemic outbreaks7. In the small-world network context, thus, it is important to control the super-spreading events to prevent completely new outbreaks8,9. In the scale-free network, on the other hand, the number of contacts per individual exhibits a power-law distribution of infection links. The variation in the connectivity distribution of the scale-free network is infinite, because it does not exhibit the threshold phenomenon. Hence, an outbreak can occur at any time10. It can be inferred from both networks that the average shortest path length and a small degree of separation are important factors in the epidemic network analysis11. Furthermore, the super-spreading characteristic of epidemics has been associated with the spatial proximity of neighbouring nodes in the network5,12. Localised transmission of the epidemic is facilitated by high clustering coefficients, because of the close spatial proximity in node connectivity and its influence on their relation. Thus, nodes with a high spatial proximity tend to intensify super-spreading events within clusters, making it easy for the disease to spread locally over the considered population or areas.\nIt is known that three factors can cause a disproportionately large number of secondary contacts during super-spreading events13: host factors (including physiological, behavioural, and immunological factors); viral factors (including virulence and co-infection factors); and environmental factors (including density, failure to recognise the disease, inter-hospital transfers, and airflow dynamics). Among these various factors, previous studies have focused specifically on the behaviour of the host and environmental factors in explaining the outbreak of SARS and MERS14,15. It has been established that certain behaviours of the hosts, such as doctor shopping (visiting multiple doctors and facilities), play a critical role in the spread of infectious disease, as multiple visits by the super-spreaders can lead to the contamination of several medical facilities. In addition to the behaviour of the infected individual, a high population density also correlates to a higher number of infections emanating from both the SARS and MERS hosts, because the probability of infection in such a setting tends to be high15. Given that the edges in the epidemic network represent physical proximity, a high level of clustering implies that infection occurs locally and spreads rapidly16.\nThe 2015 outbreak of the Middle East Respiratory Syndrome (MERS) in South Korea has been paid much attention as the outbreak was the first and biggest to occur outside Saudi Arabia, where the disease was identified in 2012. It has been known that the human-to-human transmission of MERS-coronavirus (CoV), which is a viral respiratory infection caused by a coronavirus, is relatively limited owing to its lower level of contagiousness. According to Centers for Disease Control and Prevention (CDC)17, MERS is thought to be transmitted through respiratory secretions. However, the particular way in which the virus spreads is not fully understood. During the MERS epidemic of 2015, 186 people across 16 healthcare facilities were infected, of whom 39 lost their lives. This biggest outbreak in a relatively short time began with an ‘index patient’, who had visited the Middle East and returned to Korea on May 4. The patient sought treatment for respiratory symptoms at several healthcare facilities and was later confirmed to have a MERS infection on May 20. By then, 31 people had come in contact with the patient, including family members, patients, visitors and hospital staff. In one instance of contact, a second patient was exposed to MERS while sharing an emergency room where the index patient sought care. The two patients became super-spreaders, assumed to generate many transmission events. Thus, they were likely to initiate infection among the susceptible population. Our expectation is that super-spreaders are more likely to hold certain structural advantages in facilitating continued transmission.\nThis study investigates the spread of infections over networks of contacts among individuals by exploring the 2015 MERS outbreak in Korea. We assume that the spread of a disease in a population depends on both the dynamics of the disease transmission and the structure of the contact networks over which they spread1,18–24. One perspective contends that the hosts who transmit the MERS infection are those who are highly central in the contact network. Thus, many neighbouring hosts form relational ties to others vulnerable to infection. Another perspective argues that if a host has already been infected and other hosts are not yet exposed, healthcare facilities play the pertinent role of delivering the infectious virus to other susceptible hosts. We analyze structural network properties of the epidemic transmission by examining both the relationship matrix of the infection tracing of infected individuals (from-whom-to-whom) and the bipartite transmission routes of infected individuals by healthcare facilities visited for treatment. In this study, we explore two research questions about the MERS outbreak in Korea: (a) How did the infectious disease become widespread through a network in a relatively short period of time?; and (b) How did a small fraction of individual hosts spread the MERS virus to a majority of the population?"}

LitCovid-PD-UBERON

{"project":"LitCovid-PD-UBERON","denotations":[{"id":"T1","span":{"begin":1573,"end":1578},"obj":"Body_part"},{"id":"T2","span":{"begin":2065,"end":2070},"obj":"Body_part"},{"id":"T3","span":{"begin":2098,"end":2102},"obj":"Body_part"},{"id":"T4","span":{"begin":2250,"end":2255},"obj":"Body_part"}],"attributes":[{"id":"A1","pred":"uberon_id","subj":"T1","obj":"http://purl.obolibrary.org/obo/UBERON_0002542"},{"id":"A2","pred":"uberon_id","subj":"T2","obj":"http://purl.obolibrary.org/obo/UBERON_0002542"},{"id":"A3","pred":"uberon_id","subj":"T3","obj":"http://purl.obolibrary.org/obo/UBERON_0002398"},{"id":"A4","pred":"uberon_id","subj":"T4","obj":"http://purl.obolibrary.org/obo/UBERON_0002542"}],"text":"Introduction\nTransmission of infectious disease through contact among individuals increases the risk of outbreaks with epidemic potential. However, understanding how diseases spread over networks of contacts remains a challenge. In particular, outbreaks of potentially devastating infections, such as SARS (2003), Ebola (2014–2015), and Zika (2015–2016), have shown that the dynamics behind the spread of disease has become more complex, limiting our ability to predict and control epidemics. In this regard, patterns of disease transmission should be used to design specific public health strategies to enhance sustainable capacity while building activities to improve government responses to infectious diseases. Therefore, an analysis of disease dynamics based on the contact patterns can be used to build practical guidance while framing disease prevention and management strategies.\nThe interpersonal contact patterns of disease transmissions have often been discussed in a network context while modelling epidemics1–4. Most potential disease contact takes place in localized communities among individuals occupying a local geographic space around the diseased. If such contacts are repeated within a given period, certain patterns of links will arise. These link patterns can be represented as networks, which show the spread of an infection among individuals. Thus, certain disease dynamics represented in a contact network can be characterised by topologies.\nPrevious studies on super-spreaders have identified two major types of networks, small-world network5 and scale-free network6. In the small-world network, a small number of shortcuts are discovered either by randomly connecting the nodes or randomly rewiring the links. From the shortcuts, it can be inferred that the average node length between any two individuals is shortened, thereby making geographic distance a causal factor in epidemic outbreaks7. In the small-world network context, thus, it is important to control the super-spreading events to prevent completely new outbreaks8,9. In the scale-free network, on the other hand, the number of contacts per individual exhibits a power-law distribution of infection links. The variation in the connectivity distribution of the scale-free network is infinite, because it does not exhibit the threshold phenomenon. Hence, an outbreak can occur at any time10. It can be inferred from both networks that the average shortest path length and a small degree of separation are important factors in the epidemic network analysis11. Furthermore, the super-spreading characteristic of epidemics has been associated with the spatial proximity of neighbouring nodes in the network5,12. Localised transmission of the epidemic is facilitated by high clustering coefficients, because of the close spatial proximity in node connectivity and its influence on their relation. Thus, nodes with a high spatial proximity tend to intensify super-spreading events within clusters, making it easy for the disease to spread locally over the considered population or areas.\nIt is known that three factors can cause a disproportionately large number of secondary contacts during super-spreading events13: host factors (including physiological, behavioural, and immunological factors); viral factors (including virulence and co-infection factors); and environmental factors (including density, failure to recognise the disease, inter-hospital transfers, and airflow dynamics). Among these various factors, previous studies have focused specifically on the behaviour of the host and environmental factors in explaining the outbreak of SARS and MERS14,15. It has been established that certain behaviours of the hosts, such as doctor shopping (visiting multiple doctors and facilities), play a critical role in the spread of infectious disease, as multiple visits by the super-spreaders can lead to the contamination of several medical facilities. In addition to the behaviour of the infected individual, a high population density also correlates to a higher number of infections emanating from both the SARS and MERS hosts, because the probability of infection in such a setting tends to be high15. Given that the edges in the epidemic network represent physical proximity, a high level of clustering implies that infection occurs locally and spreads rapidly16.\nThe 2015 outbreak of the Middle East Respiratory Syndrome (MERS) in South Korea has been paid much attention as the outbreak was the first and biggest to occur outside Saudi Arabia, where the disease was identified in 2012. It has been known that the human-to-human transmission of MERS-coronavirus (CoV), which is a viral respiratory infection caused by a coronavirus, is relatively limited owing to its lower level of contagiousness. According to Centers for Disease Control and Prevention (CDC)17, MERS is thought to be transmitted through respiratory secretions. However, the particular way in which the virus spreads is not fully understood. During the MERS epidemic of 2015, 186 people across 16 healthcare facilities were infected, of whom 39 lost their lives. This biggest outbreak in a relatively short time began with an ‘index patient’, who had visited the Middle East and returned to Korea on May 4. The patient sought treatment for respiratory symptoms at several healthcare facilities and was later confirmed to have a MERS infection on May 20. By then, 31 people had come in contact with the patient, including family members, patients, visitors and hospital staff. In one instance of contact, a second patient was exposed to MERS while sharing an emergency room where the index patient sought care. The two patients became super-spreaders, assumed to generate many transmission events. Thus, they were likely to initiate infection among the susceptible population. Our expectation is that super-spreaders are more likely to hold certain structural advantages in facilitating continued transmission.\nThis study investigates the spread of infections over networks of contacts among individuals by exploring the 2015 MERS outbreak in Korea. We assume that the spread of a disease in a population depends on both the dynamics of the disease transmission and the structure of the contact networks over which they spread1,18–24. One perspective contends that the hosts who transmit the MERS infection are those who are highly central in the contact network. Thus, many neighbouring hosts form relational ties to others vulnerable to infection. Another perspective argues that if a host has already been infected and other hosts are not yet exposed, healthcare facilities play the pertinent role of delivering the infectious virus to other susceptible hosts. We analyze structural network properties of the epidemic transmission by examining both the relationship matrix of the infection tracing of infected individuals (from-whom-to-whom) and the bipartite transmission routes of infected individuals by healthcare facilities visited for treatment. In this study, we explore two research questions about the MERS outbreak in Korea: (a) How did the infectious disease become widespread through a network in a relatively short period of time?; and (b) How did a small fraction of individual hosts spread the MERS virus to a majority of the population?"}

LitCovid-PD-MONDO

LitCovid-PD-CLO

LitCovid-PD-GO-BP

{"project":"LitCovid-PD-GO-BP","denotations":[{"id":"T1","span":{"begin":3240,"end":3251},"obj":"http://purl.obolibrary.org/obo/GO_0007610"},{"id":"T2","span":{"begin":3306,"end":3315},"obj":"http://purl.obolibrary.org/obo/GO_0016032"},{"id":"T3","span":{"begin":3306,"end":3315},"obj":"http://purl.obolibrary.org/obo/GO_0009405"},{"id":"T4","span":{"begin":3551,"end":3560},"obj":"http://purl.obolibrary.org/obo/GO_0007610"},{"id":"T5","span":{"begin":3686,"end":3696},"obj":"http://purl.obolibrary.org/obo/GO_0007610"},{"id":"T6","span":{"begin":3959,"end":3968},"obj":"http://purl.obolibrary.org/obo/GO_0007610"},{"id":"T7","span":{"begin":4910,"end":4920},"obj":"http://purl.obolibrary.org/obo/GO_0046903"}],"text":"Introduction\nTransmission of infectious disease through contact among individuals increases the risk of outbreaks with epidemic potential. However, understanding how diseases spread over networks of contacts remains a challenge. In particular, outbreaks of potentially devastating infections, such as SARS (2003), Ebola (2014–2015), and Zika (2015–2016), have shown that the dynamics behind the spread of disease has become more complex, limiting our ability to predict and control epidemics. In this regard, patterns of disease transmission should be used to design specific public health strategies to enhance sustainable capacity while building activities to improve government responses to infectious diseases. Therefore, an analysis of disease dynamics based on the contact patterns can be used to build practical guidance while framing disease prevention and management strategies.\nThe interpersonal contact patterns of disease transmissions have often been discussed in a network context while modelling epidemics1–4. Most potential disease contact takes place in localized communities among individuals occupying a local geographic space around the diseased. If such contacts are repeated within a given period, certain patterns of links will arise. These link patterns can be represented as networks, which show the spread of an infection among individuals. Thus, certain disease dynamics represented in a contact network can be characterised by topologies.\nPrevious studies on super-spreaders have identified two major types of networks, small-world network5 and scale-free network6. In the small-world network, a small number of shortcuts are discovered either by randomly connecting the nodes or randomly rewiring the links. From the shortcuts, it can be inferred that the average node length between any two individuals is shortened, thereby making geographic distance a causal factor in epidemic outbreaks7. In the small-world network context, thus, it is important to control the super-spreading events to prevent completely new outbreaks8,9. In the scale-free network, on the other hand, the number of contacts per individual exhibits a power-law distribution of infection links. The variation in the connectivity distribution of the scale-free network is infinite, because it does not exhibit the threshold phenomenon. Hence, an outbreak can occur at any time10. It can be inferred from both networks that the average shortest path length and a small degree of separation are important factors in the epidemic network analysis11. Furthermore, the super-spreading characteristic of epidemics has been associated with the spatial proximity of neighbouring nodes in the network5,12. Localised transmission of the epidemic is facilitated by high clustering coefficients, because of the close spatial proximity in node connectivity and its influence on their relation. Thus, nodes with a high spatial proximity tend to intensify super-spreading events within clusters, making it easy for the disease to spread locally over the considered population or areas.\nIt is known that three factors can cause a disproportionately large number of secondary contacts during super-spreading events13: host factors (including physiological, behavioural, and immunological factors); viral factors (including virulence and co-infection factors); and environmental factors (including density, failure to recognise the disease, inter-hospital transfers, and airflow dynamics). Among these various factors, previous studies have focused specifically on the behaviour of the host and environmental factors in explaining the outbreak of SARS and MERS14,15. It has been established that certain behaviours of the hosts, such as doctor shopping (visiting multiple doctors and facilities), play a critical role in the spread of infectious disease, as multiple visits by the super-spreaders can lead to the contamination of several medical facilities. In addition to the behaviour of the infected individual, a high population density also correlates to a higher number of infections emanating from both the SARS and MERS hosts, because the probability of infection in such a setting tends to be high15. Given that the edges in the epidemic network represent physical proximity, a high level of clustering implies that infection occurs locally and spreads rapidly16.\nThe 2015 outbreak of the Middle East Respiratory Syndrome (MERS) in South Korea has been paid much attention as the outbreak was the first and biggest to occur outside Saudi Arabia, where the disease was identified in 2012. It has been known that the human-to-human transmission of MERS-coronavirus (CoV), which is a viral respiratory infection caused by a coronavirus, is relatively limited owing to its lower level of contagiousness. According to Centers for Disease Control and Prevention (CDC)17, MERS is thought to be transmitted through respiratory secretions. However, the particular way in which the virus spreads is not fully understood. During the MERS epidemic of 2015, 186 people across 16 healthcare facilities were infected, of whom 39 lost their lives. This biggest outbreak in a relatively short time began with an ‘index patient’, who had visited the Middle East and returned to Korea on May 4. The patient sought treatment for respiratory symptoms at several healthcare facilities and was later confirmed to have a MERS infection on May 20. By then, 31 people had come in contact with the patient, including family members, patients, visitors and hospital staff. In one instance of contact, a second patient was exposed to MERS while sharing an emergency room where the index patient sought care. The two patients became super-spreaders, assumed to generate many transmission events. Thus, they were likely to initiate infection among the susceptible population. Our expectation is that super-spreaders are more likely to hold certain structural advantages in facilitating continued transmission.\nThis study investigates the spread of infections over networks of contacts among individuals by exploring the 2015 MERS outbreak in Korea. We assume that the spread of a disease in a population depends on both the dynamics of the disease transmission and the structure of the contact networks over which they spread1,18–24. One perspective contends that the hosts who transmit the MERS infection are those who are highly central in the contact network. Thus, many neighbouring hosts form relational ties to others vulnerable to infection. Another perspective argues that if a host has already been infected and other hosts are not yet exposed, healthcare facilities play the pertinent role of delivering the infectious virus to other susceptible hosts. We analyze structural network properties of the epidemic transmission by examining both the relationship matrix of the infection tracing of infected individuals (from-whom-to-whom) and the bipartite transmission routes of infected individuals by healthcare facilities visited for treatment. In this study, we explore two research questions about the MERS outbreak in Korea: (a) How did the infectious disease become widespread through a network in a relatively short period of time?; and (b) How did a small fraction of individual hosts spread the MERS virus to a majority of the population?"}

LitCovid-sentences

LitCovid-PD-HP

{"project":"LitCovid-PD-HP","denotations":[{"id":"T1","span":{"begin":4678,"end":4699},"obj":"Phenotype"}],"attributes":[{"id":"A1","pred":"hp_id","subj":"T1","obj":"http://purl.obolibrary.org/obo/HP_0011947"}],"text":"Introduction\nTransmission of infectious disease through contact among individuals increases the risk of outbreaks with epidemic potential. However, understanding how diseases spread over networks of contacts remains a challenge. In particular, outbreaks of potentially devastating infections, such as SARS (2003), Ebola (2014–2015), and Zika (2015–2016), have shown that the dynamics behind the spread of disease has become more complex, limiting our ability to predict and control epidemics. In this regard, patterns of disease transmission should be used to design specific public health strategies to enhance sustainable capacity while building activities to improve government responses to infectious diseases. Therefore, an analysis of disease dynamics based on the contact patterns can be used to build practical guidance while framing disease prevention and management strategies.\nThe interpersonal contact patterns of disease transmissions have often been discussed in a network context while modelling epidemics1–4. Most potential disease contact takes place in localized communities among individuals occupying a local geographic space around the diseased. If such contacts are repeated within a given period, certain patterns of links will arise. These link patterns can be represented as networks, which show the spread of an infection among individuals. Thus, certain disease dynamics represented in a contact network can be characterised by topologies.\nPrevious studies on super-spreaders have identified two major types of networks, small-world network5 and scale-free network6. In the small-world network, a small number of shortcuts are discovered either by randomly connecting the nodes or randomly rewiring the links. From the shortcuts, it can be inferred that the average node length between any two individuals is shortened, thereby making geographic distance a causal factor in epidemic outbreaks7. In the small-world network context, thus, it is important to control the super-spreading events to prevent completely new outbreaks8,9. In the scale-free network, on the other hand, the number of contacts per individual exhibits a power-law distribution of infection links. The variation in the connectivity distribution of the scale-free network is infinite, because it does not exhibit the threshold phenomenon. Hence, an outbreak can occur at any time10. It can be inferred from both networks that the average shortest path length and a small degree of separation are important factors in the epidemic network analysis11. Furthermore, the super-spreading characteristic of epidemics has been associated with the spatial proximity of neighbouring nodes in the network5,12. Localised transmission of the epidemic is facilitated by high clustering coefficients, because of the close spatial proximity in node connectivity and its influence on their relation. Thus, nodes with a high spatial proximity tend to intensify super-spreading events within clusters, making it easy for the disease to spread locally over the considered population or areas.\nIt is known that three factors can cause a disproportionately large number of secondary contacts during super-spreading events13: host factors (including physiological, behavioural, and immunological factors); viral factors (including virulence and co-infection factors); and environmental factors (including density, failure to recognise the disease, inter-hospital transfers, and airflow dynamics). Among these various factors, previous studies have focused specifically on the behaviour of the host and environmental factors in explaining the outbreak of SARS and MERS14,15. It has been established that certain behaviours of the hosts, such as doctor shopping (visiting multiple doctors and facilities), play a critical role in the spread of infectious disease, as multiple visits by the super-spreaders can lead to the contamination of several medical facilities. In addition to the behaviour of the infected individual, a high population density also correlates to a higher number of infections emanating from both the SARS and MERS hosts, because the probability of infection in such a setting tends to be high15. Given that the edges in the epidemic network represent physical proximity, a high level of clustering implies that infection occurs locally and spreads rapidly16.\nThe 2015 outbreak of the Middle East Respiratory Syndrome (MERS) in South Korea has been paid much attention as the outbreak was the first and biggest to occur outside Saudi Arabia, where the disease was identified in 2012. It has been known that the human-to-human transmission of MERS-coronavirus (CoV), which is a viral respiratory infection caused by a coronavirus, is relatively limited owing to its lower level of contagiousness. According to Centers for Disease Control and Prevention (CDC)17, MERS is thought to be transmitted through respiratory secretions. However, the particular way in which the virus spreads is not fully understood. During the MERS epidemic of 2015, 186 people across 16 healthcare facilities were infected, of whom 39 lost their lives. This biggest outbreak in a relatively short time began with an ‘index patient’, who had visited the Middle East and returned to Korea on May 4. The patient sought treatment for respiratory symptoms at several healthcare facilities and was later confirmed to have a MERS infection on May 20. By then, 31 people had come in contact with the patient, including family members, patients, visitors and hospital staff. In one instance of contact, a second patient was exposed to MERS while sharing an emergency room where the index patient sought care. The two patients became super-spreaders, assumed to generate many transmission events. Thus, they were likely to initiate infection among the susceptible population. Our expectation is that super-spreaders are more likely to hold certain structural advantages in facilitating continued transmission.\nThis study investigates the spread of infections over networks of contacts among individuals by exploring the 2015 MERS outbreak in Korea. We assume that the spread of a disease in a population depends on both the dynamics of the disease transmission and the structure of the contact networks over which they spread1,18–24. One perspective contends that the hosts who transmit the MERS infection are those who are highly central in the contact network. Thus, many neighbouring hosts form relational ties to others vulnerable to infection. Another perspective argues that if a host has already been infected and other hosts are not yet exposed, healthcare facilities play the pertinent role of delivering the infectious virus to other susceptible hosts. We analyze structural network properties of the epidemic transmission by examining both the relationship matrix of the infection tracing of infected individuals (from-whom-to-whom) and the bipartite transmission routes of infected individuals by healthcare facilities visited for treatment. In this study, we explore two research questions about the MERS outbreak in Korea: (a) How did the infectious disease become widespread through a network in a relatively short period of time?; and (b) How did a small fraction of individual hosts spread the MERS virus to a majority of the population?"}

2_test

{"project":"2_test","denotations":[{"id":"32152361-9623998-138518102","span":{"begin":1567,"end":1568},"obj":"9623998"},{"id":"32152361-10521342-138518103","span":{"begin":1591,"end":1592},"obj":"10521342"},{"id":"32152361-27433389-138518104","span":{"begin":2053,"end":2054},"obj":"27433389"},{"id":"32152361-15466494-138518105","span":{"begin":2055,"end":2056},"obj":"15466494"},{"id":"32152361-9623998-138518106","span":{"begin":2691,"end":2692},"obj":"9623998"},{"id":"32152361-21737332-138518107","span":{"begin":3197,"end":3199},"obj":"21737332"},{"id":"32152361-26088634-138518108","span":{"begin":3642,"end":3644},"obj":"26088634"},{"id":"32152361-26468744-138518109","span":{"begin":3645,"end":3647},"obj":"26468744"},{"id":"32152361-26468744-138518110","span":{"begin":4188,"end":4190},"obj":"26468744"},{"id":"32152361-26194875-138518111","span":{"begin":6287,"end":6289},"obj":"26194875"}],"text":"Introduction\nTransmission of infectious disease through contact among individuals increases the risk of outbreaks with epidemic potential. However, understanding how diseases spread over networks of contacts remains a challenge. In particular, outbreaks of potentially devastating infections, such as SARS (2003), Ebola (2014–2015), and Zika (2015–2016), have shown that the dynamics behind the spread of disease has become more complex, limiting our ability to predict and control epidemics. In this regard, patterns of disease transmission should be used to design specific public health strategies to enhance sustainable capacity while building activities to improve government responses to infectious diseases. Therefore, an analysis of disease dynamics based on the contact patterns can be used to build practical guidance while framing disease prevention and management strategies.\nThe interpersonal contact patterns of disease transmissions have often been discussed in a network context while modelling epidemics1–4. Most potential disease contact takes place in localized communities among individuals occupying a local geographic space around the diseased. If such contacts are repeated within a given period, certain patterns of links will arise. These link patterns can be represented as networks, which show the spread of an infection among individuals. Thus, certain disease dynamics represented in a contact network can be characterised by topologies.\nPrevious studies on super-spreaders have identified two major types of networks, small-world network5 and scale-free network6. In the small-world network, a small number of shortcuts are discovered either by randomly connecting the nodes or randomly rewiring the links. From the shortcuts, it can be inferred that the average node length between any two individuals is shortened, thereby making geographic distance a causal factor in epidemic outbreaks7. In the small-world network context, thus, it is important to control the super-spreading events to prevent completely new outbreaks8,9. In the scale-free network, on the other hand, the number of contacts per individual exhibits a power-law distribution of infection links. The variation in the connectivity distribution of the scale-free network is infinite, because it does not exhibit the threshold phenomenon. Hence, an outbreak can occur at any time10. It can be inferred from both networks that the average shortest path length and a small degree of separation are important factors in the epidemic network analysis11. Furthermore, the super-spreading characteristic of epidemics has been associated with the spatial proximity of neighbouring nodes in the network5,12. Localised transmission of the epidemic is facilitated by high clustering coefficients, because of the close spatial proximity in node connectivity and its influence on their relation. Thus, nodes with a high spatial proximity tend to intensify super-spreading events within clusters, making it easy for the disease to spread locally over the considered population or areas.\nIt is known that three factors can cause a disproportionately large number of secondary contacts during super-spreading events13: host factors (including physiological, behavioural, and immunological factors); viral factors (including virulence and co-infection factors); and environmental factors (including density, failure to recognise the disease, inter-hospital transfers, and airflow dynamics). Among these various factors, previous studies have focused specifically on the behaviour of the host and environmental factors in explaining the outbreak of SARS and MERS14,15. It has been established that certain behaviours of the hosts, such as doctor shopping (visiting multiple doctors and facilities), play a critical role in the spread of infectious disease, as multiple visits by the super-spreaders can lead to the contamination of several medical facilities. In addition to the behaviour of the infected individual, a high population density also correlates to a higher number of infections emanating from both the SARS and MERS hosts, because the probability of infection in such a setting tends to be high15. Given that the edges in the epidemic network represent physical proximity, a high level of clustering implies that infection occurs locally and spreads rapidly16.\nThe 2015 outbreak of the Middle East Respiratory Syndrome (MERS) in South Korea has been paid much attention as the outbreak was the first and biggest to occur outside Saudi Arabia, where the disease was identified in 2012. It has been known that the human-to-human transmission of MERS-coronavirus (CoV), which is a viral respiratory infection caused by a coronavirus, is relatively limited owing to its lower level of contagiousness. According to Centers for Disease Control and Prevention (CDC)17, MERS is thought to be transmitted through respiratory secretions. However, the particular way in which the virus spreads is not fully understood. During the MERS epidemic of 2015, 186 people across 16 healthcare facilities were infected, of whom 39 lost their lives. This biggest outbreak in a relatively short time began with an ‘index patient’, who had visited the Middle East and returned to Korea on May 4. The patient sought treatment for respiratory symptoms at several healthcare facilities and was later confirmed to have a MERS infection on May 20. By then, 31 people had come in contact with the patient, including family members, patients, visitors and hospital staff. In one instance of contact, a second patient was exposed to MERS while sharing an emergency room where the index patient sought care. The two patients became super-spreaders, assumed to generate many transmission events. Thus, they were likely to initiate infection among the susceptible population. Our expectation is that super-spreaders are more likely to hold certain structural advantages in facilitating continued transmission.\nThis study investigates the spread of infections over networks of contacts among individuals by exploring the 2015 MERS outbreak in Korea. We assume that the spread of a disease in a population depends on both the dynamics of the disease transmission and the structure of the contact networks over which they spread1,18–24. One perspective contends that the hosts who transmit the MERS infection are those who are highly central in the contact network. Thus, many neighbouring hosts form relational ties to others vulnerable to infection. Another perspective argues that if a host has already been infected and other hosts are not yet exposed, healthcare facilities play the pertinent role of delivering the infectious virus to other susceptible hosts. We analyze structural network properties of the epidemic transmission by examining both the relationship matrix of the infection tracing of infected individuals (from-whom-to-whom) and the bipartite transmission routes of infected individuals by healthcare facilities visited for treatment. In this study, we explore two research questions about the MERS outbreak in Korea: (a) How did the infectious disease become widespread through a network in a relatively short period of time?; and (b) How did a small fraction of individual hosts spread the MERS virus to a majority of the population?"}

PMC:7062829 / 1429-8743 JSONTXT

Annnotations TAB JSON ListView MergeView

PMC:7062829 / 1429-8743 JSON TXT