Early Characterization of the Severity and Transmissibility of Pandemic Influenza Using Clinical Episode Data from Multiple Populations
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{"title"=>"Early Characterization of the Severity and Transmissibility of Pandemic Influenza Using Clinical Episode Data from Multiple Populations", "type"=>"journal", "authors"=>[{"first_name"=>"Pete", "last_name"=>"Riley", "scopus_author_id"=>"7201512624"}, {"first_name"=>"Michal", "last_name"=>"Ben-Nun", "scopus_author_id"=>"57190975779"}, {"first_name"=>"Jon A.", "last_name"=>"Linker", "scopus_author_id"=>"6701596959"}, {"first_name"=>"Angelia A.", "last_name"=>"Cost", "scopus_author_id"=>"56532060400"}, {"first_name"=>"Jose L.", "last_name"=>"Sanchez", "scopus_author_id"=>"55853974400"}, {"first_name"=>"Dylan", "last_name"=>"George", "scopus_author_id"=>"35274703600"}, {"first_name"=>"David P.", "last_name"=>"Bacon", "scopus_author_id"=>"55989264100"}, {"first_name"=>"Steven", "last_name"=>"Riley", "scopus_author_id"=>"7102619416"}], "year"=>2015, "source"=>"PLoS Computational Biology", "identifiers"=>{"isbn"=>"1553-734x", "sgr"=>"84943516294", "pui"=>"606367022", "scopus"=>"2-s2.0-84943516294", "issn"=>"15537358", "doi"=>"10.1371/journal.pcbi.1004392", "pmid"=>"26402446"}, "id"=>"b14ee981-15b9-3fb8-a343-b42c7572a46a", "abstract"=>"The potential rapid availability of large-scale clinical episode data during the next influenza pandemic suggests an opportunity for increasing the speed with which novel respiratory pathogens can be characterized. Key intervention decisions will be determined by both the transmissibility of the novel strain (measured by the basic reproductive number R0) and its individual-level severity. The 2009 pandemic illustrated that estimating individual-level severity, as described by the proportion pC of infections that result in clinical cases, can remain uncertain for a prolonged period of time. Here, we use 50 distinct US military populations during 2009 as a retrospective cohort to test the hypothesis that real-time encounter data combined with disease dynamic models can be used to bridge this uncertainty gap. Effectively, we estimated the total number of infections in multiple early-affected communities using the model and divided that number by the known number of clinical cases. Joint estimates of severity and transmissibility clustered within a relatively small region of parameter space, with 40 of the 50 populations bounded by: pC, 0.0133-0.150 and R0, 1.09-2.16. These fits were obtained despite widely varying incidence profiles: some with spring waves, some with fall waves and some with both. To illustrate the benefit of specific pairing of rapidly available data and infectious disease models, we simulated a future moderate pandemic strain with pC approximately ×10 that of 2009; the results demonstrating that even before the peak had passed in the first affected population, R0 and pC could be well estimated. This study provides a clear reference in this two-dimensional space against which future novel respiratory pathogens can be rapidly assessed and compared with previous pandemics.", "link"=>"http://www.mendeley.com/research/early-characterization-severity-transmissibility-pandemic-influenza-using-clinical-episode-data-mult-1", "reader_count"=>17, "reader_count_by_academic_status"=>{"Professor > Associate Professor"=>1, "Researcher"=>10, "Student > Doctoral Student"=>2, "Student > Ph. D. Student"=>3, "Student > Master"=>1}, "reader_count_by_user_role"=>{"Professor > Associate Professor"=>1, "Researcher"=>10, "Student > Doctoral Student"=>2, "Student > Ph. D. Student"=>3, "Student > Master"=>1}, "reader_count_by_subject_area"=>{"Environmental Science"=>1, "Biochemistry, Genetics and Molecular Biology"=>1, "Mathematics"=>3, "Agricultural and Biological Sciences"=>6, "Medicine and Dentistry"=>4, "Computer Science"=>1, "Immunology and Microbiology"=>1}, "reader_count_by_subdiscipline"=>{"Medicine and Dentistry"=>{"Medicine and Dentistry"=>4}, "Immunology and Microbiology"=>{"Immunology and Microbiology"=>1}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>6}, "Computer Science"=>{"Computer Science"=>1}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>1}, "Mathematics"=>{"Mathematics"=>3}, "Environmental Science"=>{"Environmental Science"=>1}}, "reader_count_by_country"=>{"United States"=>1, "Australia"=>1}, "group_count"=>1}

Scopus | Further Information

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/2287610"], "description"=>"<p><b>a</b> Estimates of <i>R</i><sub>0</sub> versus <i>p</i><sub><i>C</i></sub> for top-50 military installations. The ten installations with the largest number of ILI cases are colored red, installations 11 through 20 are colored blue, and the remaining 30 bases are colored cyan. The grey box denotes the 40 installations with the smallest area in <i>p</i><sub><i>C</i></sub>-<i>R</i><sub>0</sub> space. The histograms along the top and right show the distribution of <i>p</i><sub><i>C</i></sub> and <i>R</i><sub>0</sub> values, respectively. <b>b-e</b> Incidence rates for four military installations (red line), with model fits overlaid (blue line), illustrating: <b>b</b> a two-peak profile; <b>c</b> a single-peak profile; <b>d</b> an anomalously high and narrow profile; and <b>e</b> a complex profile. The green line shows the value of the basic reproduction number and the horizontal dashed grey line marks the critical value of 1.0. The inset in each panel shows the cumulative attack rate for the same time period.</p>", "links"=>[], "tags"=>["spring waves", "Clinical Episode Data", "pandemic strain", "uncertainty gap", "proportion pC", "Key intervention decisions", "2009 Pandemic", "Joint estimates", "disease models", "r 0", "future novel", "novel strain", "retrospective cohort", "encounter data", "influenza pandemic", "pathogen", "use 50", "infection", "incidence profiles", "pandemic influenza", "severity", "episode data", "parameter space", "50 populations", "transmissibility", "fall waves", "Multiple Populations"], "article_id"=>1555480, "categories"=>["Uncategorised"], "users"=>["Pete Riley", "Michal Ben-Nun", "Jon A. Linker", "Angelia A. Cost", "Jose L. Sanchez", "Dylan George", "David P. Bacon", "Steven Riley"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1004392.g002", "stats"=>{"downloads"=>7, "page_views"=>11, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Characterization_of_overall_severity_and_individual_base_fits_/1555480", "title"=>"Characterization of overall severity and individual base fits.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-09-24 03:32:57"}
  • {"files"=>["https://ndownloader.figshare.com/files/2287611"], "description"=>"<p><b>a-c</b> Three frames illustrating stochastic predictions for an outbreak based on parameters that give a typical two-peak profile (such as that of MPZ-92055), but increasing <i>p</i><sub><i>C</i></sub> by about an order of magnitude. The grey lines give individual simulation realizations while the green line shows the ensemble average. The data contributing to the realizations is marked in red. The insets show the evolution of computed <i>p</i><sub><i>C</i></sub>, <i>R</i><sub><i>A</i></sub>, and <i>R</i><sub><i>B</i></sub> as more simulated data are added to the predictions. The red line is our median estimate and the blue lines are the 95% confidence intervals. <b>d-f</b> Analogous frames for a typical single-peaked outbreak such as that of MPZ-23708, but again increasing <i>p</i><sub><i>c</i></sub> by about an order of magnitude.</p>", "links"=>[], "tags"=>["spring waves", "Clinical Episode Data", "pandemic strain", "uncertainty gap", "proportion pC", "Key intervention decisions", "2009 Pandemic", "Joint estimates", "disease models", "r 0", "future novel", "novel strain", "retrospective cohort", "encounter data", "influenza pandemic", "pathogen", "use 50", "infection", "incidence profiles", "pandemic influenza", "severity", "episode data", "parameter space", "50 populations", "transmissibility", "fall waves", "Multiple Populations"], "article_id"=>1555481, "categories"=>["Uncategorised"], "users"=>["Pete Riley", "Michal Ben-Nun", "Jon A. Linker", "Angelia A. Cost", "Jose L. Sanchez", "Dylan George", "David P. Bacon", "Steven Riley"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1004392.g003", "stats"=>{"downloads"=>4, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Characterizing_a_model_strain_of_pandemic_influenza_in_real_time_for_a_single_population_/1555481", "title"=>"Characterizing a model strain of pandemic influenza in real time for a single population.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-09-24 03:32:57"}
  • {"files"=>["https://ndownloader.figshare.com/files/2287608"], "description"=>"<p><b>a</b> Relationship between the total number of individuals infected and the basic reproductive number <i>R</i><sub>0</sub>. Arrows show the non-linear effect of a 20% reduction in transmission: at lower reproductive numbers, the same intervention is much more effective. <b>b</b> Severity pyramid for infectious disease. The strength of symptoms and ability to detect cases increases with each level in the pyramid. <b>c</b> Conceptual two-dimensional classification of pandemics in terms of basic reproductive number (<i>R</i><sub>0</sub>) and severity (<i>p</i><sub><i>C</i></sub>), illustrating the likely impact of interventions, depending on where the outbreak falls in this parameter space (see main text).</p>", "links"=>[], "tags"=>["spring waves", "Clinical Episode Data", "pandemic strain", "uncertainty gap", "proportion pC", "Key intervention decisions", "2009 Pandemic", "Joint estimates", "disease models", "r 0", "future novel", "novel strain", "retrospective cohort", "encounter data", "influenza pandemic", "pathogen", "use 50", "infection", "incidence profiles", "pandemic influenza", "severity", "episode data", "parameter space", "50 populations", "transmissibility", "fall waves", "Multiple Populations"], "article_id"=>1555478, "categories"=>["Uncategorised"], "users"=>["Pete Riley", "Michal Ben-Nun", "Jon A. Linker", "Angelia A. Cost", "Jose L. Sanchez", "Dylan George", "David P. Bacon", "Steven Riley"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1004392.g001", "stats"=>{"downloads"=>3, "page_views"=>25, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Transmissibility_and_severity_of_pandemic_influenza_/1555478", "title"=>"Transmissibility and severity of pandemic influenza.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-09-24 03:32:57"}
  • {"files"=>["https://ndownloader.figshare.com/files/2287625", "https://ndownloader.figshare.com/files/2287626", "https://ndownloader.figshare.com/files/2287627", "https://ndownloader.figshare.com/files/2287628", "https://ndownloader.figshare.com/files/2287629", "https://ndownloader.figshare.com/files/2287630", "https://ndownloader.figshare.com/files/2287631", "https://ndownloader.figshare.com/files/2287632"], "description"=>"<div><p>The potential rapid availability of large-scale clinical episode data during the next influenza pandemic suggests an opportunity for increasing the speed with which novel respiratory pathogens can be characterized. Key intervention decisions will be determined by both the transmissibility of the novel strain (measured by the basic reproductive number <i>R</i><sub>0</sub>) and its individual-level severity. The 2009 pandemic illustrated that estimating individual-level severity, as described by the proportion <i>p</i><sub><i>C</i></sub> of infections that result in clinical cases, can remain uncertain for a prolonged period of time. Here, we use 50 distinct US military populations during 2009 as a retrospective cohort to test the hypothesis that real-time encounter data combined with disease dynamic models can be used to bridge this uncertainty gap. Effectively, we estimated the total number of infections in multiple early-affected communities using the model and divided that number by the known number of clinical cases. Joint estimates of severity and transmissibility clustered within a relatively small region of parameter space, with 40 of the 50 populations bounded by: <i>p</i><sub><i>C</i></sub>, 0.0133–0.150 and <i>R</i><sub>0</sub>, 1.09–2.16. These fits were obtained despite widely varying incidence profiles: some with spring waves, some with fall waves and some with both. To illustrate the benefit of specific pairing of rapidly available data and infectious disease models, we simulated a future moderate pandemic strain with <i>p</i><sub><i>C</i></sub> approximately ×10 that of 2009; the results demonstrating that even before the peak had passed in the first affected population, <i>R</i><sub>0</sub> and <i>p</i><sub><i>C</i></sub> could be well estimated. This study provides a clear reference in this two-dimensional space against which future novel respiratory pathogens can be rapidly assessed and compared with previous pandemics.</p></div>", "links"=>[], "tags"=>["spring waves", "Clinical Episode Data", "pandemic strain", "uncertainty gap", "proportion pC", "Key intervention decisions", "2009 Pandemic", "Joint estimates", "disease models", "r 0", "future novel", "novel strain", "retrospective cohort", "encounter data", "influenza pandemic", "pathogen", "use 50", "infection", "incidence profiles", "pandemic influenza", "severity", "episode data", "parameter space", "50 populations", "transmissibility", "fall waves", "Multiple Populations"], "article_id"=>1555495, "categories"=>["Uncategorised"], "users"=>["Pete Riley", "Michal Ben-Nun", "Jon A. Linker", "Angelia A. Cost", "Jose L. Sanchez", "Dylan George", "David P. Bacon", "Steven Riley"], "doi"=>["https://dx.doi.org/10.1371/journal.pcbi.1004392.s001", "https://dx.doi.org/10.1371/journal.pcbi.1004392.s002", "https://dx.doi.org/10.1371/journal.pcbi.1004392.s003", "https://dx.doi.org/10.1371/journal.pcbi.1004392.s004", "https://dx.doi.org/10.1371/journal.pcbi.1004392.s005", "https://dx.doi.org/10.1371/journal.pcbi.1004392.s006", "https://dx.doi.org/10.1371/journal.pcbi.1004392.s007", "https://dx.doi.org/10.1371/journal.pcbi.1004392.s008"], "stats"=>{"downloads"=>13, "page_views"=>13, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Early_Characterization_of_the_Severity_and_Transmissibility_of_Pandemic_Influenza_Using_Clinical_Episode_Data_from_Multiple_Populations_/1555495", "title"=>"Early Characterization of the Severity and Transmissibility of Pandemic Influenza Using Clinical Episode Data from Multiple Populations", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2015-09-24 03:32:57"}

PMC Usage Stats | Further Information

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Relative Metric

{"start_date"=>"2015-01-01T00:00:00Z", "end_date"=>"2015-12-31T00:00:00Z", "subject_areas"=>[]}
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