Estimation of the Basic Reproductive Number and Mean Serial Interval of a Novel Pathogen in a Small, Well-Observed Discrete Population
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{"title"=>"Estimation of the basic reproductive number and mean serial interval of a novel pathogen in a small, well-observed discrete population", "type"=>"journal", "authors"=>[{"first_name"=>"Kendra M.", "last_name"=>"Wu", "scopus_author_id"=>"36151664200"}, {"first_name"=>"Steven", "last_name"=>"Riley", "scopus_author_id"=>"7102619416"}], "year"=>2016, "source"=>"PLoS ONE", "identifiers"=>{"pmid"=>"26849644", "sgr"=>"84959378709", "pui"=>"608546509", "scopus"=>"2-s2.0-84959378709", "issn"=>"19326203", "doi"=>"10.1371/journal.pone.0148061"}, "id"=>"14db31cc-e72c-3b8c-8cf1-f4599fe9defc", "abstract"=>"Background. Accurately assessing the transmissibility and serial interval of a novel human pathogen is public health priority so that the timing and required strength of interventions may be determined. Recent theoretical work has focused on making best use of data from the initial exponential phase of growth of incidence in large populations. Methods. We measured generational transmissibility by the basic reproductive number R0 and the serial interval by its mean Tg. First, we constructed a simulation algorithm for case data arising from a small population of known size with R0 and Tg also known. We then developed an inferential model for the likelihood of these case data as a function of R0 and Tg. The model was designed to capture a) any signal of the serial interval distribution in the initial stochastic phase b) the growth rate of the exponential phase and c) the unique combination of R0 and Tg that generates a specific shape of peak incidence when the susceptible portion of a small population is depleted. Findings. Extensive repeat simulation and parameter estimation revealed no bias in univariate estimates of either R0 and Tg. We were also able to simultaneously estimate both R0 and Tg. However, accurate final estimates could be obtained only much later in the outbreak. In particular, estimates of Tg were considerably less accurate in the bivariate case until the peak of incidence had passed. Conclusions. The basic reproductive number and mean serial interval can be estimated simultaneously in real time during an outbreak of an emerging pathogen. Repeated application of these methods to small scale outbreaks at the start of an epidemic would permit accurate estimates of key parameters.", "link"=>"http://www.mendeley.com/research/estimation-basic-reproductive-number-mean-serial-interval-novel-pathogen-small-wellobserved-discrete", "reader_count"=>13, "reader_count_by_academic_status"=>{"Researcher"=>5, "Student > Ph. D. Student"=>4, "Student > Postgraduate"=>1, "Student > Master"=>3}, "reader_count_by_user_role"=>{"Researcher"=>5, "Student > Ph. D. Student"=>4, "Student > Postgraduate"=>1, "Student > Master"=>3}, "reader_count_by_subject_area"=>{"Engineering"=>1, "Unspecified"=>1, "Environmental Science"=>2, "Mathematics"=>3, "Agricultural and Biological Sciences"=>3, "Medicine and Dentistry"=>3}, "reader_count_by_subdiscipline"=>{"Engineering"=>{"Engineering"=>1}, "Medicine and Dentistry"=>{"Medicine and Dentistry"=>3}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>3}, "Mathematics"=>{"Mathematics"=>3}, "Unspecified"=>{"Unspecified"=>1}, "Environmental Science"=>{"Environmental Science"=>2}}, "reader_count_by_country"=>{"United States"=>1}, "group_count"=>1}

Scopus | Further Information

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/4219162"], "description"=>"<p>The following parameters were used: (a) <i>R</i><sub>0</sub> = 1.8, <i>T</i><sub><i>g</i></sub> = 2.5, <i>k</i> = 10; (b) <i>R</i><sub>0</sub> = 1.8, <i>T</i><sub><i>g</i></sub> = 2.5, <i>k</i> = 25; (c) <i>R</i><sub>0</sub> = 3.0, <i>T</i><sub><i>g</i></sub> = 6.25, <i>k</i> = 10; (d) <i>R</i><sub>0</sub> = 3.0, <i>T</i><sub><i>g</i></sub> = 6.25, <i>k</i> = 25. PI: prediction interval.</p>", "links"=>[], "tags"=>["case data", "Tg", "Basic Reproductive Number", "R 0", "incidence", "outbreak", "interval", "number R 0", "phase"], "article_id"=>2575684, "categories"=>["Medicine", "Genetics", "Molecular Biology", "Neuroscience", "Biotechnology", "Evolutionary Biology", "Mathematical Sciences not elsewhere classified", "Developmental Biology", "Cancer", "Infectious Diseases", "Plant Biology", "Virology", "Computational Biology"], "users"=>["Kendra M. Wu", "Steven Riley"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0148061.t001", "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Univariate_and_bivariate_estimates_of_the_model_using_Poisson_distributed_serial_interval_different_i_k_i_values_and_number_of_simulation_iterations_/2575684", "title"=>"Univariate and bivariate estimates of the model using Poisson distributed serial interval, different <i>k</i> values, and number of simulation iterations.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2016-02-05 12:23:53"}
  • {"files"=>["https://ndownloader.figshare.com/files/4219153"], "description"=>"<p>Part <b>a</b> shows real-time univariate estimates of <i>R</i><sub>0</sub> in which the true value of <i>T</i><sub><i>g</i></sub> was assumed known. Part <b>b</b> shows real-time univariate estimates of <i>T</i><sub><i>g</i></sub> with <i>R</i><sub>0</sub> assumed known. The results of jointly estimating <i>R</i><sub>0</sub> and <i>T</i><sub><i>g</i></sub> are shown in parts <b>c</b> (<i>R</i><sub>0</sub>) and <b>d</b> (<i>T</i><sub><i>g</i></sub>). Scenario 2 is as per <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0148061#pone.0148061.g001\" target=\"_blank\">Fig 1B</a> with <i>R</i><sub>0</sub> = 3.0, <i>T</i><sub><i>g</i></sub> = 6.25. The epidemic was simulated in a population of 1,000 people all of whom were initially susceptible, other than 100 who were infectious on day 1. Grey lines trace the corresponding estimates of 8 arbitrary epidemics. Black lines represent the average of 100 estimates at that time point, red lines represent the median, and the green dotted lines are the 5% and 95% prediction intervals for 100 epidemics. The dark blue solid horizontal lines show the true parameter values.</p>", "links"=>[], "tags"=>["case data", "Tg", "Basic Reproductive Number", "R 0", "incidence", "outbreak", "interval", "number R 0", "phase"], "article_id"=>2575675, "categories"=>["Medicine", "Genetics", "Molecular Biology", "Neuroscience", "Biotechnology", "Evolutionary Biology", "Mathematical Sciences not elsewhere classified", "Developmental Biology", "Cancer", "Infectious Diseases", "Plant Biology", "Virology", "Computational Biology"], "users"=>["Kendra M. Wu", "Steven Riley"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0148061.g003", "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Univariate_and_bivariate_parameter_estimation_from_simulated_daily_data_for_Scenario_2_/2575675", "title"=>"Univariate and bivariate parameter estimation from simulated daily data for Scenario 2.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2016-02-05 12:23:53"}
  • {"files"=>["https://ndownloader.figshare.com/files/4219138"], "description"=>"<p>Part <b>A</b> shows Scenario 1 with <i>R</i><sub>0</sub> = 1.8, <i>T</i><sub><i>g</i></sub> = 2.5. Part <b>B</b> shows Scenario 2 with <i>R</i><sub>0</sub> = 3.0, <i>T</i><sub><i>g</i></sub> = 6.25. Offspring and the serial interval assumed to follow Poisson distribution. In both scenarios, 1,000 individuals were initially susceptible and 10 people were infectious on day 1. Grey lines trace 5 single example realisations. Black lines show average case incidence, and the green dotted lines are the 5% and 95% prediction intervals from 30 realisations.</p>", "links"=>[], "tags"=>["case data", "Tg", "Basic Reproductive Number", "R 0", "incidence", "outbreak", "interval", "number R 0", "phase"], "article_id"=>2575660, "categories"=>["Medicine", "Genetics", "Molecular Biology", "Neuroscience", "Biotechnology", "Evolutionary Biology", "Mathematical Sciences not elsewhere classified", "Developmental Biology", "Cancer", "Infectious Diseases", "Plant Biology", "Virology", "Computational Biology"], "users"=>["Kendra M. Wu", "Steven Riley"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0148061.g001", "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Simulated_epidemics_/2575660", "title"=>"Simulated epidemics.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2016-02-05 12:23:53"}
  • {"files"=>["https://ndownloader.figshare.com/files/4219147"], "description"=>"<p>Part <b>a</b> shows real-time univariate estimates of <i>R</i><sub>0</sub> in which the true value of <i>T</i><sub><i>g</i></sub> was assumed known. Part <b>b</b> shows real-time univariate estimates of <i>T</i><sub><i>g</i></sub> with <i>R</i><sub>0</sub> assumed known. The results of jointly estimating <i>R</i><sub>0</sub> and <i>T</i><sub><i>g</i></sub> are shown in parts <b>c</b> (<i>R</i><sub>0</sub>) and <b>d</b> (<i>T</i><sub><i>g</i></sub>). Scenario 1 is as per <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0148061#pone.0148061.g001\" target=\"_blank\">Fig 1A</a> with <i>R</i><sub>0</sub> = 1.8, <i>T</i><sub><i>g</i></sub> = 2.5. The epidemic was simulated in a population of 1,000 people all of whom were initially susceptible, other than 50 who were infectious on day 1. Grey lines trace the corresponding estimates of 8 arbitrary epidemics. Black lines represent the average of 100 estimates at that time point, red lines represent the median, and the green dotted lines are the 5% and 95% prediction intervals for 100 epidemics. The dark blue solid horizontal lines show the true parameter values.</p>", "links"=>[], "tags"=>["case data", "Tg", "Basic Reproductive Number", "R 0", "incidence", "outbreak", "interval", "number R 0", "phase"], "article_id"=>2575669, "categories"=>["Medicine", "Genetics", "Molecular Biology", "Neuroscience", "Biotechnology", "Evolutionary Biology", "Mathematical Sciences not elsewhere classified", "Developmental Biology", "Cancer", "Infectious Diseases", "Plant Biology", "Virology", "Computational Biology"], "users"=>["Kendra M. Wu", "Steven Riley"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0148061.g002", "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Univariate_and_bivariate_parameter_estimation_from_simulated_daily_data_for_Scenario_1_/2575669", "title"=>"Univariate and bivariate parameter estimation from simulated daily data for Scenario 1.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2016-02-05 12:23:53"}
  • {"files"=>["https://ndownloader.figshare.com/files/4219156"], "description"=>"<p>Sensitivity analysis using univariate estimations at single time point and assumed poission-distributed serial interval. <b>a</b> and <b>b</b> show the distribution of the marginals for estimation of <i>R</i><sub>0</sub> and <i>T</i><sub><i>g</i></sub> of 50 realisations respectively. All estimates were made at day 50. <b>c</b> shows the mean and confidence intervals for the marginals for estimates for <i>R</i><sub>0</sub> across a wide range of known values for <i>T</i><sub><i>g</i></sub>. Similarly, <b>d</b> shows the mean and confidence intervals for <i>T</i><sub><i>g</i></sub> across a wide range of value for <i>R</i><sub>0</sub>. Scenario 1 is as per <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0148061#pone.0148061.g001\" target=\"_blank\">Fig 1A</a> with <i>R</i><sub>0</sub> = 1.8, <i>T</i><sub><i>g</i></sub> = 2.5, and <i>k</i> = 10.</p>", "links"=>[], "tags"=>["case data", "Tg", "Basic Reproductive Number", "R 0", "incidence", "outbreak", "interval", "number R 0", "phase"], "article_id"=>2575678, "categories"=>["Medicine", "Genetics", "Molecular Biology", "Neuroscience", "Biotechnology", "Evolutionary Biology", "Mathematical Sciences not elsewhere classified", "Developmental Biology", "Cancer", "Infectious Diseases", "Plant Biology", "Virology", "Computational Biology"], "users"=>["Kendra M. Wu", "Steven Riley"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0148061.g004", "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Extended_assessment_of_univariate_inference_for_Scenario_1_/2575678", "title"=>"Extended assessment of univariate inference for Scenario 1.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2016-02-05 12:23:53"}

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Relative Metric

{"start_date"=>"2016-01-01T00:00:00Z", "end_date"=>"2016-12-31T00:00:00Z", "subject_areas"=>[]}
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