A Missing Dimension in Measures of Vaccination Impacts
Publication Date
March 06, 2014
Journal
PLOS Pathogens
Authors
M. Gabriela M. Gomes, Marc Lipsitch, Andrew R. Wargo, Gael Kurath, et al
Volume
10
Issue
3
Pages
e1003849
DOI
https://dx.plos.org/10.1371/journal.ppat.1003849
Publisher URL
http://journals.plos.org/plospathogens/article?id=10.1371%2Fjournal.ppat.1003849
PubMed
http://www.ncbi.nlm.nih.gov/pubmed/24603721
PubMed Central
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3946326
Europe PMC
http://europepmc.org/abstract/MED/24603721
Web of Science
000337470300002
Scopus
84897455603
Mendeley
http://www.mendeley.com/research/missing-dimension-measures-vaccination-impacts
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Mendeley | Further Information

{"title"=>"A Missing Dimension in Measures of Vaccination Impacts", "type"=>"journal", "authors"=>[{"first_name"=>"M. Gabriela M.", "last_name"=>"Gomes", "scopus_author_id"=>"7201712379"}, {"first_name"=>"Marc", "last_name"=>"Lipsitch", "scopus_author_id"=>"7006236353"}, {"first_name"=>"Andrew R.", "last_name"=>"Wargo", "scopus_author_id"=>"22136678300"}, {"first_name"=>"Gael", "last_name"=>"Kurath", "scopus_author_id"=>"7004152460"}, {"first_name"=>"Carlota", "last_name"=>"Rebelo", "scopus_author_id"=>"6603873654"}, {"first_name"=>"Graham F.", "last_name"=>"Medley", "scopus_author_id"=>"7005195578"}, {"first_name"=>"Antonio", "last_name"=>"Coutinho", "scopus_author_id"=>"7102909682"}], "year"=>2014, "source"=>"PLoS Pathogens", "identifiers"=>{"issn"=>"15537374", "isbn"=>"1553-7374 (Electronic)\\r1553-7366 (Linking)", "pmid"=>"24603721", "scopus"=>"2-s2.0-84897455603", "doi"=>"10.1371/journal.ppat.1003849", "sgr"=>"84897455603", "pui"=>"372738297"}, "id"=>"c9d92010-d1f3-3b30-b63c-349698ee9add", "abstract"=>"Immunological protection, acquired from either natural infection or vaccina- tion, varies among hosts, reflecting under- lying biological variation and affecting population-level protection. Owing to the nature of resistance mechanisms, distribu- tions of susceptibility and protection en- tangle with pathogen dose in a way that can be decoupled by adequately repre- senting the dose dimension. Any infectious processes must depend in some fashion on dose, and empirical evidence exists for an effect of exposure dose on the probability of transmission to mumps-vaccinated hosts [1], the case-fatality ratio of measles [2], and the probability of infection and, given infection, of symptoms in cholera [3]. Extreme distributions of vaccine protec- tion have been termed leaky (partially protects all hosts) and all-or-nothing (to- tally protects a proportion of hosts) [4]. These distributions can be distinguished in vaccine field trials from the time depen- dence of infections [5]. Frailty mixing models have also been proposed to estimate the distribution of protection from time to event data [6,7], although the results are not comparable across regions unless there is explicit control for baseline transmission [8]. Distributions of host susceptibility and acquired protection can be estimated from dose-response data generated under controlled experimental conditions [9–11] and natural settings [12,13]. These distributions can guide research on mechanisms of protection, as well as enable model validity across the entire range of transmission intensities. We argue for a shift to a dose-dimension paradigm in infectious disease science and community health. Natural", "link"=>"http://www.mendeley.com/research/missing-dimension-measures-vaccination-impacts", "reader_count"=>44, "reader_count_by_academic_status"=>{"Professor > Associate Professor"=>2, "Librarian"=>1, "Researcher"=>11, "Student > Doctoral Student"=>5, "Student > Ph. D. Student"=>9, "Student > Postgraduate"=>3, "Student > Master"=>2, "Other"=>1, "Lecturer"=>3, "Professor"=>7}, "reader_count_by_user_role"=>{"Professor > Associate Professor"=>2, "Librarian"=>1, "Researcher"=>11, "Student > Doctoral Student"=>5, "Student > Ph. D. Student"=>9, "Student > Postgraduate"=>3, "Student > Master"=>2, "Other"=>1, "Lecturer"=>3, "Professor"=>7}, "reader_count_by_subject_area"=>{"Unspecified"=>6, "Environmental Science"=>3, "Biochemistry, Genetics and Molecular Biology"=>1, "Nursing and Health Professions"=>1, "Mathematics"=>4, "Agricultural and Biological Sciences"=>15, "Medicine and Dentistry"=>6, "Veterinary Science and Veterinary Medicine"=>1, "Computer Science"=>1, "Immunology and Microbiology"=>6}, "reader_count_by_subdiscipline"=>{"Medicine and Dentistry"=>{"Medicine and Dentistry"=>6}, "Immunology and Microbiology"=>{"Immunology and Microbiology"=>6}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>15}, "Computer Science"=>{"Computer Science"=>1}, "Nursing and Health Professions"=>{"Nursing and Health Professions"=>1}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>1}, "Mathematics"=>{"Mathematics"=>4}, "Unspecified"=>{"Unspecified"=>6}, "Environmental Science"=>{"Environmental Science"=>3}, "Veterinary Science and Veterinary Medicine"=>{"Veterinary Science and Veterinary Medicine"=>1}}, "reader_count_by_country"=>{"United States"=>2, "Brazil"=>1, "United Kingdom"=>3, "France"=>2, "Kenya"=>1, "Portugal"=>1, "Spain"=>1}, "group_count"=>3}

Scopus | Further Information

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/1411121"], "description"=>"<p>(A) Equilibrium prevalence of infection under a pathogen transmission model in which an intervention (vaccine or symbiont) reduces host susceptibility to a factor that is distributed as specified. The model is formally represented by the rates of change in the proportions of the population that are susceptible and infected: , , , and , where <i>S</i> and <i>I</i> are nonintervention, while and are intervention groups with susceptibility <i>x</i> distributed as (right panels). Colored lines assume total intervention coverage (), while the black line represents the scenario without intervention (). (B) Dose-response curves expected from an experiment in which groups of naive (black) and intervention (colored) hosts are challenged with a range of pathogen doses, under a model in which the intervention reduces susceptibility to a factor that is distributed as in panels on the right. Models for infected proportions in nonintervention and intervention groups are formalized in a dose-response manner by and , respectively, where <i>d</i> is the number of pathogens the host is challenged by and <i>p</i> is the probability of infection for each pathogen. Colored lines assume susceptibility factors distributed with mean 0.5 in all cases and variance 0 (red), 0.05 (orange), 0.1 (green), 0.2 (cyan), and 0.25 (blue). Red and blue at the extremes are discrete, while the intermediate cases are continuous beta distributions, with shape parameters <i>a</i> and <i>b</i> such that the mean is fixed,, and the variance, , spans the range, . Transmission models assume , and controlled infection models assume .</p>", "links"=>[], "tags"=>["epidemiology", "Infectious diseases", "heterogeneity"], "article_id"=>954709, "categories"=>["Medicine"], "users"=>["M. Gabriela M. Gomes", "Marc Lipsitch", "Andrew R. Wargo", "Gael Kurath", "Carlota Rebelo", "Graham F. Medley", "António Coutinho"], "doi"=>"https://dx.doi.org/10.1371/journal.ppat.1003849.g001", "stats"=>{"downloads"=>1, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Decreasing_infection_with_heterogeneity_in_host_protection_/954709", "title"=>"Decreasing infection with heterogeneity in host protection.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-03-06 04:45:43"}
  • {"files"=>["https://ndownloader.figshare.com/files/1411141", "https://ndownloader.figshare.com/files/1411142"], "description"=>"<p>A Missing Dimension in Measures of Vaccination Impacts</p>", "links"=>[], "tags"=>["epidemiology", "Infectious diseases", "measures", "vaccination"], "article_id"=>954729, "categories"=>["Medicine"], "users"=>["M. Gabriela M. Gomes", "Marc Lipsitch", "Andrew R. Wargo", "Gael Kurath", "Carlota Rebelo", "Graham F. Medley", "António Coutinho"], "doi"=>["https://dx.doi.org/10.1371/journal.ppat.1003849.s001", "https://dx.doi.org/10.1371/journal.ppat.1003849.s002"], "stats"=>{"downloads"=>1, "page_views"=>9, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_A_Missing_Dimension_in_Measures_of_Vaccination_Impacts_/954729", "title"=>"A Missing Dimension in Measures of Vaccination Impacts", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2014-03-06 04:45:43"}
  • {"files"=>["https://ndownloader.figshare.com/files/1411123"], "description"=>"<p>Beta distributions are classified as: polarized if ; symmetric if (gray dashed line), as in <a href=\"http://www.plospathogens.org/article/info:doi/10.1371/journal.ppat.1003849#ppat-1003849-g001\" target=\"_blank\">Figure 1</a>; homogeneous in the limit (red circle), as red in <a href=\"http://www.plospathogens.org/article/info:doi/10.1371/journal.ppat.1003849#ppat-1003849-g001\" target=\"_blank\">Figure 1</a>; all-or-nothing in the limit (blue circle), as blue in <a href=\"http://www.plospathogens.org/article/info:doi/10.1371/journal.ppat.1003849#ppat-1003849-g001\" target=\"_blank\">Figure 1</a>; and uniform if (gray square). The power to identify polarized distributions is analyzed in a neighborhood of the uniform distribution (<a href=\"http://www.plospathogens.org/article/info:doi/10.1371/journal.ppat.1003849#ppat.1003849.s001\" target=\"_blank\">Figure S1</a>).</p>", "links"=>[], "tags"=>["epidemiology", "Infectious diseases", "classification"], "article_id"=>954711, "categories"=>["Medicine"], "users"=>["M. Gabriela M. Gomes", "Marc Lipsitch", "Andrew R. Wargo", "Gael Kurath", "Carlota Rebelo", "Graham F. Medley", "António Coutinho"], "doi"=>"https://dx.doi.org/10.1371/journal.ppat.1003849.g002", "stats"=>{"downloads"=>1, "page_views"=>2, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Shape_classification_in_the_terms_of_parameters_a_and_b_/954711", "title"=>"Shape classification in the terms of parameters <i>a</i> and <i>b</i>.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-03-06 04:45:43"}

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