Pub Date : 2018-12-14DOI: 10.21061/VIRAL-NETWORKS.PHILLIPS
Christopher J Phillips
There is a statistical paradox at the heart of twentieth-century medicine. In 1900 physicians largely ignored the tools of statistical analysis. Clinicians and laboratory researchers saw themselves as fundamentally opposed to the burgeoning field of academic statistics: they were interested in biomedical causation, statisticians were focused on numerical correlation; they were focused on exceptions and idiosyncrasies, statisticians were focused on norms and averages; they were determinists, statisticians were probabilists. There were essentially no statistical articles in medical journals, no statistical training required for the M.D., no well-known statistical interpretations of laboratory experiments. The American Medical Association lamented that questions about therapeutic efficacy were largely addressed by anecdotal accounts from influential physicians (and drug companies themselves).1 The burgeoning field of public health (sometimes under the title of “sanitation” or “hygiene”) drew on epidemiological measures of disease, and questions of inoculation and epidemic infection had long been resolved with statistical calculations.2 But these were seen as limited to large outbreaks where people could be treated as interchangeable; in the clinic, the opposite was true. Patients were unique and the aggregative methods of epidemiology irrelevant.3 By 2000 the situation was seemingly reversed. A statistically significant randomized clinical trial was the gold standard of therapeutic efficacy, and such proof was required by the Food and Drug Administration (FDA) prior to licensing drugs.4 Reformers now promoted “evidence-based” medicine (as if medicine had never before been based on evidence), an initiative which claimed best practices should be determined solely on the basis of statistically
{"title":"Networks of Statisticians and the Transformation of Medicine","authors":"Christopher J Phillips","doi":"10.21061/VIRAL-NETWORKS.PHILLIPS","DOIUrl":"https://doi.org/10.21061/VIRAL-NETWORKS.PHILLIPS","url":null,"abstract":"There is a statistical paradox at the heart of twentieth-century medicine. In 1900 physicians largely ignored the tools of statistical analysis. Clinicians and laboratory researchers saw themselves as fundamentally opposed to the burgeoning field of academic statistics: they were interested in biomedical causation, statisticians were focused on numerical correlation; they were focused on exceptions and idiosyncrasies, statisticians were focused on norms and averages; they were determinists, statisticians were probabilists. There were essentially no statistical articles in medical journals, no statistical training required for the M.D., no well-known statistical interpretations of laboratory experiments. The American Medical Association lamented that questions about therapeutic efficacy were largely addressed by anecdotal accounts from influential physicians (and drug companies themselves).1 The burgeoning field of public health (sometimes under the title of “sanitation” or “hygiene”) drew on epidemiological measures of disease, and questions of inoculation and epidemic infection had long been resolved with statistical calculations.2 But these were seen as limited to large outbreaks where people could be treated as interchangeable; in the clinic, the opposite was true. Patients were unique and the aggregative methods of epidemiology irrelevant.3 By 2000 the situation was seemingly reversed. A statistically significant randomized clinical trial was the gold standard of therapeutic efficacy, and such proof was required by the Food and Drug Administration (FDA) prior to licensing drugs.4 Reformers now promoted “evidence-based” medicine (as if medicine had never before been based on evidence), an initiative which claimed best practices should be determined solely on the basis of statistically","PeriodicalId":355263,"journal":{"name":"Viral Networks","volume":"25 4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126713870","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-12-14DOI: 10.21061/VIRAL-NETWORKS.PORTER
N. Porter
{"title":"Using Data and Network Analysis in Humanities Research: A Guide to Getting Started","authors":"N. Porter","doi":"10.21061/VIRAL-NETWORKS.PORTER","DOIUrl":"https://doi.org/10.21061/VIRAL-NETWORKS.PORTER","url":null,"abstract":"","PeriodicalId":355263,"journal":{"name":"Viral Networks","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130641184","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-12-14DOI: 10.21061/VIRAL-NETWORKS.GLOSSARY
N. Porter
Actor In an affiliation network, the people or other entities tied by events Asymmetrical tie An edge or relationship in a directed network that is not reciprocated; for example Bob cites Jane but Jane does not cite Bob Affiliation network A network where the edges consisted of a shared characteristic, such as attending a class together, rather than a direct relationship, such as friendship, and the nodes are the actors and events; actors cannot be directly tied to other actors, nor events to other events Attribute A characteristic of a node or edge; can be used to select nodes and edges or as an analytic variable; can also be represented visually through size, color, etc. Bipartite network A network where ties occur only between (and not within) two distinct subgroups; affiliation networks are a type of bipartite networks Betweenness centrality A type of node centrality measuring the importance of each node in geodesic paths between other nodes Centralization A network statistic measuring how unevenly spread the edges in a network are; a network with high centralization has relatively few key nodes connecting a large number of other nodes Clique A subgroup of nodes where each node shares an edge with every other node; the most restrictive subgroup definition Closeness centrality A type of node centrality determined by the geodesic distance to all other nodes in a component; high closeness indicates that most other nodes can be reached in relatively few steps
{"title":"Glossary of Network Terminology","authors":"N. Porter","doi":"10.21061/VIRAL-NETWORKS.GLOSSARY","DOIUrl":"https://doi.org/10.21061/VIRAL-NETWORKS.GLOSSARY","url":null,"abstract":"Actor In an affiliation network, the people or other entities tied by events Asymmetrical tie An edge or relationship in a directed network that is not reciprocated; for example Bob cites Jane but Jane does not cite Bob Affiliation network A network where the edges consisted of a shared characteristic, such as attending a class together, rather than a direct relationship, such as friendship, and the nodes are the actors and events; actors cannot be directly tied to other actors, nor events to other events Attribute A characteristic of a node or edge; can be used to select nodes and edges or as an analytic variable; can also be represented visually through size, color, etc. Bipartite network A network where ties occur only between (and not within) two distinct subgroups; affiliation networks are a type of bipartite networks Betweenness centrality A type of node centrality measuring the importance of each node in geodesic paths between other nodes Centralization A network statistic measuring how unevenly spread the edges in a network are; a network with high centralization has relatively few key nodes connecting a large number of other nodes Clique A subgroup of nodes where each node shares an edge with every other node; the most restrictive subgroup definition Closeness centrality A type of node centrality determined by the geodesic distance to all other nodes in a component; high closeness indicates that most other nodes can be reached in relatively few steps","PeriodicalId":355263,"journal":{"name":"Viral Networks","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126791962","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-12-14DOI: 10.21061/viral-networks.runcie
S. Runcie
{"title":"Networks of the Unnamed and Medical Interventions in Colonial Cameroon","authors":"S. Runcie","doi":"10.21061/viral-networks.runcie","DOIUrl":"https://doi.org/10.21061/viral-networks.runcie","url":null,"abstract":"","PeriodicalId":355263,"journal":{"name":"Viral Networks","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125048849","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-12-14DOI: 10.21061/VIRAL-NETWORKS.ENGELMANN
Lukas Engelmann
The science of epidemiology has always had an intricate relationship to the history of diseases. The design of models of the dynamics that govern diseases in their relation to population is ultimately based on information and data gathered from past outbreaks. Epidemiology belongs to what Lorraine Daston has recently called “Sciences of the Archive.”1 Like astronomy, zoology, demography, or meteorology, the study of epidemics operates with objects of superhuman scale. The discipline deals with plagues that exceed historiographical periods and geographical regions; and, thus, it always requires elaborated practices of collecting, accounting, and archiving to establish its status as a discipline. Daston reminds us that despite this reliance of some “hard” sciences on the historical record, their conduct of history often differs from the perspective of humanists on the same historical event. Where exegesis, commentary, and interpretation of contexts and niches might characterize a history of diseases and epidemics, the epidemiological grasp on the historical record seeks to collect quantifiable data. But epidemiology wasn’t always a science of mathematical analysis, concerned with the production of formal expressions and the elaborate design of stochastic models. The epidemiology of the late nineteenth and early twentieth centuries is best described as a broad interdisciplinary project, suspended between isolated academics in medical schools and a growing group of governmental medical officers applying a mixture of methods, integrating
{"title":"Mapping Early Epidemiology: Concepts of Causality in Reports of the Third Plague Pandemic, 1894–1950","authors":"Lukas Engelmann","doi":"10.21061/VIRAL-NETWORKS.ENGELMANN","DOIUrl":"https://doi.org/10.21061/VIRAL-NETWORKS.ENGELMANN","url":null,"abstract":"The science of epidemiology has always had an intricate relationship to the history of diseases. The design of models of the dynamics that govern diseases in their relation to population is ultimately based on information and data gathered from past outbreaks. Epidemiology belongs to what Lorraine Daston has recently called “Sciences of the Archive.”1 Like astronomy, zoology, demography, or meteorology, the study of epidemics operates with objects of superhuman scale. The discipline deals with plagues that exceed historiographical periods and geographical regions; and, thus, it always requires elaborated practices of collecting, accounting, and archiving to establish its status as a discipline. Daston reminds us that despite this reliance of some “hard” sciences on the historical record, their conduct of history often differs from the perspective of humanists on the same historical event. Where exegesis, commentary, and interpretation of contexts and niches might characterize a history of diseases and epidemics, the epidemiological grasp on the historical record seeks to collect quantifiable data. But epidemiology wasn’t always a science of mathematical analysis, concerned with the production of formal expressions and the elaborate design of stochastic models. The epidemiology of the late nineteenth and early twentieth centuries is best described as a broad interdisciplinary project, suspended between isolated academics in medical schools and a growing group of governmental medical officers applying a mixture of methods, integrating","PeriodicalId":355263,"journal":{"name":"Viral Networks","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127719239","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-12-14DOI: 10.21061/VIRAL-NETWORKS.COTTLE
Katherine Cottle
The recent transition from paper to electronic form as the standard means of communication has shifted not only the medium of epistolary expression, but also the networking potential of scholars and historians. Visualizations of networks can no longer rely solely on humanistic expectations of time, space, direction, and location with regards to communication, even when reading and studying text from pre-digital times. As personal print text becomes more and more indistinguishable from public digital communication, we find ourselves at a crossroads in finding appropriate venues for representing words that relate “a momentary experience which incorporates but stands outside orthodox conceptions of material and immaterial existence.”1 How do we, as current correspondents, scholars, and researchers, imbed standardized networking frameworks, such as traditional mapping, into current and future networking needs and applications? How can data-driven networks help to increase accessibility and knowledge of past figures and texts while simultaneously sustaining humanistic foundations, ethics, and aims? The Viral Networks workshop provided the time, physical and virtual space, guidance, and digital resources for me to explore these questions through networking applications of a recently discovered archive of personal correspondence, “The Esther Richards Letters, 1915–1932,” included within my forthcoming book, The Hidden Heart of Charm City: Baltimore Letters and Lives (AH/ Loyola University Maryland).
{"title":"Anatomical Reading of Correspondence: A Case Study of Epistolary Analysis Networks","authors":"Katherine Cottle","doi":"10.21061/VIRAL-NETWORKS.COTTLE","DOIUrl":"https://doi.org/10.21061/VIRAL-NETWORKS.COTTLE","url":null,"abstract":"The recent transition from paper to electronic form as the standard means of communication has shifted not only the medium of epistolary expression, but also the networking potential of scholars and historians. Visualizations of networks can no longer rely solely on humanistic expectations of time, space, direction, and location with regards to communication, even when reading and studying text from pre-digital times. As personal print text becomes more and more indistinguishable from public digital communication, we find ourselves at a crossroads in finding appropriate venues for representing words that relate “a momentary experience which incorporates but stands outside orthodox conceptions of material and immaterial existence.”1 How do we, as current correspondents, scholars, and researchers, imbed standardized networking frameworks, such as traditional mapping, into current and future networking needs and applications? How can data-driven networks help to increase accessibility and knowledge of past figures and texts while simultaneously sustaining humanistic foundations, ethics, and aims? The Viral Networks workshop provided the time, physical and virtual space, guidance, and digital resources for me to explore these questions through networking applications of a recently discovered archive of personal correspondence, “The Esther Richards Letters, 1915–1932,” included within my forthcoming book, The Hidden Heart of Charm City: Baltimore Letters and Lives (AH/ Loyola University Maryland).","PeriodicalId":355263,"journal":{"name":"Viral Networks","volume":"90 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131997079","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-12-14DOI: 10.21061/VIRAL-NETWORKS.DIMEO-RUIS
Michelle DiMeo, A. Ruis
Network models, in particular social network models, have improved our understanding of a variety of historical phenomena, including correspondence communities, trade networks, citation patterns, dissemination of news, and so on. In many cases, social network analysis has been used to show relationships among people—who corresponded with, traded with, cited, or otherwise interacted with whom? But what if we extended our scope to consider the networks of knowledge created by these individuals? Instead of asking merely “Who was in this network and how were they connected?”, we could ask, “How did information move through this network?” Such questions more closely model the qualitative questions that historians concerned with discourse and concepts have traditionally asked and usually try to answer without computational approaches; however, as access to historical data is expanding rapidly due to digitization efforts, it will be useful, if not necessary, to collaborate with machines on our analyses. To do so, we need to think about mixed-methods approaches that integrate the strengths of humans and computers, and network analysis is one methodological approach that could prove helpful in answering the kinds of qualitative research questions often asked by social, cultural, and intellectual historians.1 In this chapter we reflect on the use of epistemic network analysis (ENA) as a tool for modeling conceptual networks. Because there are a number of resources that explain ENA in great detail as a
{"title":"Thinking about Sources as Data: Reflections on Epistemic Network Analysis as a Technique for Historical Research","authors":"Michelle DiMeo, A. Ruis","doi":"10.21061/VIRAL-NETWORKS.DIMEO-RUIS","DOIUrl":"https://doi.org/10.21061/VIRAL-NETWORKS.DIMEO-RUIS","url":null,"abstract":"Network models, in particular social network models, have improved our understanding of a variety of historical phenomena, including correspondence communities, trade networks, citation patterns, dissemination of news, and so on. In many cases, social network analysis has been used to show relationships among people—who corresponded with, traded with, cited, or otherwise interacted with whom? But what if we extended our scope to consider the networks of knowledge created by these individuals? Instead of asking merely “Who was in this network and how were they connected?”, we could ask, “How did information move through this network?” Such questions more closely model the qualitative questions that historians concerned with discourse and concepts have traditionally asked and usually try to answer without computational approaches; however, as access to historical data is expanding rapidly due to digitization efforts, it will be useful, if not necessary, to collaborate with machines on our analyses. To do so, we need to think about mixed-methods approaches that integrate the strengths of humans and computers, and network analysis is one methodological approach that could prove helpful in answering the kinds of qualitative research questions often asked by social, cultural, and intellectual historians.1 In this chapter we reflect on the use of epistemic network analysis (ENA) as a tool for modeling conceptual networks. Because there are a number of resources that explain ENA in great detail as a","PeriodicalId":355263,"journal":{"name":"Viral Networks","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124500096","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-12-14DOI: 10.21061/VIRAL-NETWORKS.EWING-RANDALL
E. Ewing, Katherine Randall
{"title":"Introduction: Connecting Digital Humanities and Medical History through Viral Networks","authors":"E. Ewing, Katherine Randall","doi":"10.21061/VIRAL-NETWORKS.EWING-RANDALL","DOIUrl":"https://doi.org/10.21061/VIRAL-NETWORKS.EWING-RANDALL","url":null,"abstract":"","PeriodicalId":355263,"journal":{"name":"Viral Networks","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133388352","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-12-14DOI: 10.21061/VIRAL-NETWORKS.SMITH
Kylie Smith
Racism in American psychiatry can be traced back to the intellectual justifications for slavery, and the early linkage of the black psyche with criminality.1 The idea that the African American was inherently psychologically inferior, less complex, more childlike, or just inherently “bad,” gave rise to centuries of neglect, abuse, and misdiagnosis of black people with mental illness, as well as justifying a system of separate and unequal treatment.2 In Alabama, this system legally ended on February 11, 1969 when the Honorable Judge Frank M. Johnson, Chief Judge of the US District Court in the Middle District of Alabama, handed down his decision in what he called “a rather straightforward problem” in the case of Marable v. Alabama Mental Health Board. In this decision, Johnson laid out in plain detail the many ways in which the State of Alabama and the Alabama Mental Health Board were in breach of Title VI of the Civil Rights Act of 1964, and declared racial segregation in the state’s mental hospitals unconstitutional. Judge Johnson gave the Alabama Mental Health Board 12 months to desegregate its inpatient facilities entirely, or it would continue to have its federal mental health funding withheld and would not be eligible for any further such funds.3 In the context of the powerful Civil Rights Movement in Alabama, mental hospitals became sites of contested ideas about the nature of African American psychology and a challenge to the racist nature of American psychiatry itself. This chapter is part of a much broader project called “Jim Crow in the Asylum: Psychiatry and Civil Rights in the American South,”
美国精神病学中的种族主义可以追溯到奴隶制的理性辩护，以及黑人精神与犯罪的早期联系认为非裔美国人在心理上天生低人一等、不那么复杂、更孩子气或天生就是“坏”的观点，导致了几个世纪以来对黑人精神疾病患者的忽视、虐待和误诊，也为隔离和不平等待遇制度提供了理由在阿拉巴马州，这一制度于1969年2月11日在法律上结束，当时阿拉巴马州中区美国地方法院首席法官弗兰克·m·约翰逊(Frank M. Johnson)法官在马拉布尔诉阿拉巴马州精神健康委员会一案中宣布了他所谓的“相当直截了当的问题”的判决。在这项判决中，约翰逊详细地阐述了阿拉巴马州和阿拉巴马州精神健康委员会违反1964年民权法案第六章的许多方面，并宣布该州精神病院的种族隔离违宪。约翰逊法官给了阿拉巴马精神健康委员会12个月的时间，要求其完全取消住院设施的种族隔离，否则将继续扣留其联邦精神健康资金，并且不再有资格获得任何此类资金在阿拉巴马州声势浩大的民权运动的背景下，精神病院成为关于非裔美国人心理本质的争议之地，也是对美国精神病学本身种族主义本质的挑战。这一章是一个更广泛的项目的一部分，名为“收容所中的吉姆·克劳:美国南方的精神病学和民权”，
{"title":"\"A Rather Straightforward Problem\": Unravelling Networks of Segregation in Alabama’s Psychiatric Hospitals, 1966–1972","authors":"Kylie Smith","doi":"10.21061/VIRAL-NETWORKS.SMITH","DOIUrl":"https://doi.org/10.21061/VIRAL-NETWORKS.SMITH","url":null,"abstract":"Racism in American psychiatry can be traced back to the intellectual justifications for slavery, and the early linkage of the black psyche with criminality.1 The idea that the African American was inherently psychologically inferior, less complex, more childlike, or just inherently “bad,” gave rise to centuries of neglect, abuse, and misdiagnosis of black people with mental illness, as well as justifying a system of separate and unequal treatment.2 In Alabama, this system legally ended on February 11, 1969 when the Honorable Judge Frank M. Johnson, Chief Judge of the US District Court in the Middle District of Alabama, handed down his decision in what he called “a rather straightforward problem” in the case of Marable v. Alabama Mental Health Board. In this decision, Johnson laid out in plain detail the many ways in which the State of Alabama and the Alabama Mental Health Board were in breach of Title VI of the Civil Rights Act of 1964, and declared racial segregation in the state’s mental hospitals unconstitutional. Judge Johnson gave the Alabama Mental Health Board 12 months to desegregate its inpatient facilities entirely, or it would continue to have its federal mental health funding withheld and would not be eligible for any further such funds.3 In the context of the powerful Civil Rights Movement in Alabama, mental hospitals became sites of contested ideas about the nature of African American psychology and a challenge to the racist nature of American psychiatry itself. This chapter is part of a much broader project called “Jim Crow in the Asylum: Psychiatry and Civil Rights in the American South,”","PeriodicalId":355263,"journal":{"name":"Viral Networks","volume":"394 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124495507","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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