The Impact of Model Building on the Transmission Dynamics under Vaccination: Observable (Symptom-Based) versus Unobservable (Contagiousness-Dependent) Approaches
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{"title"=>"The Impact of Model Building on the Transmission Dynamics under Vaccination: Observable (Symptom-Based) versus Unobservable (Contagiousness-Dependent) Approaches", "type"=>"journal", "authors"=>[{"first_name"=>"Keisuke", "last_name"=>"Ejima", "scopus_author_id"=>"55252715300"}, {"first_name"=>"Kazuyuki", "last_name"=>"Aihara", "scopus_author_id"=>"7103203284"}, {"first_name"=>"Hiroshi", "last_name"=>"Nishiura", "scopus_author_id"=>"7005501836"}], "year"=>2013, "source"=>"PLoS ONE", "identifiers"=>{"scopus"=>"2-s2.0-84876091845", "doi"=>"10.1371/journal.pone.0062062", "sgr"=>"84876091845", "isbn"=>"1932-6203", "pmid"=>"23593507", "issn"=>"19326203", "pui"=>"368721460"}, "id"=>"b09c112f-2d45-37d7-a645-e24be6d6001b", "abstract"=>"BACKGROUND: The way we formulate a mathematical model of an infectious disease to capture symptomatic and asymptomatic transmission can greatly influence the likely effectiveness of vaccination in the presence of vaccine effect for preventing clinical illness. The present study aims to assess the impact of model building strategy on the epidemic threshold under vaccination.\\n\\nMETHODOLOGY/PRINCIPAL FINDINGS: We consider two different types of mathematical models, one based on observable variables including symptom onset and recovery from clinical illness (hereafter, the \"observable model\") and the other based on unobservable information of infection event and infectiousness (the \"unobservable model\"). By imposing a number of modifying assumptions to the observable model, we let it mimic the unobservable model, identifying that the two models are fully consistent only when the incubation period is identical to the latent period and when there is no pre-symptomatic transmission. We also computed the reproduction numbers with and without vaccination, demonstrating that the data generating process of vaccine-induced reduction in symptomatic illness is consistent with the observable model only and examining how the effective reproduction number is differently calculated by two models.\\n\\nCONCLUSIONS: To explicitly incorporate the vaccine effect in reducing the risk of symptomatic illness into the model, it is fruitful to employ a model that directly accounts for disease progression. More modeling studies based on observable epidemiological information are called for.", "link"=>"http://www.mendeley.com/research/impact-model-building-transmission-dynamics-under-vaccination-observable-symptombased-versus-unobser", "reader_count"=>13, "reader_count_by_academic_status"=>{"Unspecified"=>1, "Professor > Associate Professor"=>2, "Researcher"=>3, "Student > Doctoral Student"=>1, "Student > Ph. D. Student"=>3, "Student > Master"=>2, "Professor"=>1}, "reader_count_by_user_role"=>{"Unspecified"=>1, "Professor > Associate Professor"=>2, "Researcher"=>3, "Student > Doctoral Student"=>1, "Student > Ph. D. Student"=>3, "Student > Master"=>2, "Professor"=>1}, "reader_count_by_subject_area"=>{"Unspecified"=>1, "Mathematics"=>5, "Agricultural and Biological Sciences"=>1, "Medicine and Dentistry"=>4, "Physics and Astronomy"=>1, "Social Sciences"=>1}, "reader_count_by_subdiscipline"=>{"Medicine and Dentistry"=>{"Medicine and Dentistry"=>4}, "Social Sciences"=>{"Social Sciences"=>1}, "Physics and Astronomy"=>{"Physics and Astronomy"=>1}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>1}, "Mathematics"=>{"Mathematics"=>5}, "Unspecified"=>{"Unspecified"=>1}}, "reader_count_by_country"=>{"United States"=>1}, "group_count"=>1}

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/1022752"], "description"=>"<p>The impact of varying the proportion of pre-symptomatic transmissions among all asymptomatic transmissions (the horizontal axis; denoted by <i>g</i> in the main text) on the basic reproduction number, <i>R</i><sub>0</sub>. Only the value of <i>g</i> (and the corresponding <i>α</i>) in the model is varied. All other parameters are fixed (see <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0062062#pone-0062062-t001\" target=\"_blank\">Table? 1</a>). Shaded area represents the plausible parameter region of the proportion of pre-symptomatic transmissions among the total asymptomatic transmissions, <i>g</i>, for a specific disease.</p>", "links"=>[], "tags"=>["reproduction", "pre-symptomatic"], "article_id"=>681089, "categories"=>["Information And Computing Sciences", "Mathematics", "Biological Sciences", "Infectious Diseases", "Biotechnology"], "users"=>["Keisuke Ejima", "Kazuyuki Aihara", "Hiroshi Nishiura"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0062062.g002", "stats"=>{"downloads"=>0, "page_views"=>1, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_basic_reproduction_number_and_the_pre_symptomatic_transmission_/681089", "title"=>"The basic reproduction number and the pre-symptomatic transmission.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-04-12 00:18:09"}
  • {"files"=>["https://ndownloader.figshare.com/files/1022753"], "description"=>"<p>Effective reproduction numbers for the observable model and the unobservable model are compared as a function of vaccine-induced reduction in symptomatic illness. To permit comparison, in the absence of vaccination practice, the epidemic threshold values of the two models were assumed as identical. Vaccination coverage is fixed at 50%. The solid line shows the reproduction number of the unobservable model under vaccination. The dashed line shows the reproduction number of the observable model under vaccination. Except the vaccine-induced reduction in symptomatic illness, all parameters were fixed (see <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0062062#pone-0062062-t001\" target=\"_blank\">Table? 1</a>). For the unobservable model, relative infectiousness of asymptomatic individuals (compared to symptomatic individuals), <i>m</i> (or <i>w</i>), was arbitrarily fixed at 0.5 for three diseases other than varicella to which we assigned 0.7 (these particular values were arbitrarily chosen to visually demonstrate the difference between two models).</p>", "links"=>[], "tags"=>["reproduction", "vaccination"], "article_id"=>681090, "categories"=>["Information And Computing Sciences", "Mathematics", "Biological Sciences", "Infectious Diseases", "Biotechnology"], "users"=>["Keisuke Ejima", "Kazuyuki Aihara", "Hiroshi Nishiura"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0062062.g003", "stats"=>{"downloads"=>1, "page_views"=>2, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_effective_reproduction_number_under_vaccination_practice_/681090", "title"=>"The effective reproduction number under vaccination practice.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-04-12 00:18:10"}
  • {"files"=>["https://ndownloader.figshare.com/files/1022754"], "description"=>"†<p>assumed.</p>", "links"=>[], "tags"=>["observable", "unobservable", "transmitted", "infectious"], "article_id"=>681091, "categories"=>["Information And Computing Sciences", "Mathematics", "Biological Sciences", "Infectious Diseases", "Biotechnology"], "users"=>["Keisuke Ejima", "Kazuyuki Aihara", "Hiroshi Nishiura"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0062062.t001", "stats"=>{"downloads"=>11, "page_views"=>2, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Parameter_values_for_observable_and_unobservable_models_of_directly_transmitted_infectious_diseases_/681091", "title"=>"Parameter values for observable and unobservable models of directly transmitted infectious diseases.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-04-12 00:18:11"}
  • {"files"=>["https://ndownloader.figshare.com/files/1022750"], "description"=>"<p>A. The compartment of an observable model. The model describes the transitions depending on illness onset and recovery from clinical symptoms. Once infected, all infected individuals experience asymptomatic period, <i>J</i><sub>A</sub>, some of which fully recover from infection without symptoms, and the remaining develop symptoms, <i>J</i><sub>S</sub>. B. The compartment of an unobservable model. The model describes the transitions depending on acquirement or disappearance of infectiousness. Upon infection, infected individuals experience the latent period (i.e. Exposed compartment (<i>E</i>)) after which each acquires infectiousness and is classified as either symptomatic (<i>I</i><sub>S</sub>) or asymptomatic (<i>I</i><sub>A</sub>) one. C. The compartment of the special case of the observable model. The model describes the transitions based on symptoms, but partially accounts for infectiousness too. To let it be similar to model B, we decomposed asymptomatic individuals, <i>J</i><sub>A</sub> of the observable model (panel A) into pre-symptomatic individuals, <i>H</i><sub>S</sub> and fully asymptomatic individuals with or without infectivity. <i>U</i> represents recovered individuals.</p>", "links"=>[], "tags"=>["observable", "unobservable"], "article_id"=>681087, "categories"=>["Information And Computing Sciences", "Mathematics", "Biological Sciences", "Infectious Diseases", "Biotechnology"], "users"=>["Keisuke Ejima", "Kazuyuki Aihara", "Hiroshi Nishiura"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0062062.g001", "stats"=>{"downloads"=>1, "page_views"=>25, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Compartments_of_observable_and_unobservable_models_/681087", "title"=>"Compartments of observable and unobservable models.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-04-12 00:18:07"}

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Relative Metric

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