How to Make Epidemiological Training Infectious
Publication Date
April 03, 2012
Journal
PLOS Biology
Authors
Steve E. Bellan, Juliet R. C. Pulliam, James C. Scott, Jonathan Dushoff, et al
Volume
10
Issue
4
Pages
e1001295
DOI
https://dx.plos.org/10.1371/journal.pbio.1001295
Publisher URL
http://journals.plos.org/plosbiology/article?id=10.1371%2Fjournal.pbio.1001295
PubMed
http://www.ncbi.nlm.nih.gov/pubmed/22509129
PubMed Central
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3317897
Europe PMC
http://europepmc.org/abstract/MED/22509129
Web of Science
000303541800001
Scopus
84859994580
Mendeley
http://www.mendeley.com/research/make-epidemiological-training-infectious
Events
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Mendeley | Further Information

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Scopus | Further Information

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/658351"], "description"=>"<p>Epidemic time series for the outbreaks at the 2010 (A) and 2011 (B) outbreaks. The former and latter outbreaks differ by their different basic reproductive numbers (defined as the average number of people an infectious individual infects if the rest of the population is susceptible; <i>R</i><sub>0</sub> = 1.23 and 1.82, respectively), the initial number of infectious individuals in the population (2 and 4, respectively), and the number of individuals immune at the start of the outbreak (0 and 14, respectively). (C and D) demonstrate how the effective reproductive number (<i>R</i><sub>eff</sub>; average number of individuals each infected person infects) changes during the course of the outbreak as the number of susceptibles decreases and that the epidemic begins to burn out when <i>R</i><sub>eff</sub> decreases below 1 and infectious individuals no longer replace themselves with new infections. The script for production of and further detail on this figure are given in <a href=\"http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.1001295#pbio.1001295.s018\" target=\"_blank\">Text S3</a>.</p>", "links"=>[], "tags"=>["mathematical"], "article_id"=>328841, "categories"=>["Science Policy", "Infectious Diseases", "Mathematics", "Genetics", "Biotechnology", "Ecology"], "users"=>["Steve E. Bellan", "Juliet R. C. Pulliam", "James C. Scott", "Jonathan Dushoff"], "doi"=>"https://dx.doi.org/10.1371/journal.pbio.1001295.g001", "stats"=>{"downloads"=>1, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Muizenberg_Mathematical_Fever_epidemic_time_series_/328841", "title"=>"Muizenberg Mathematical Fever epidemic time series.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-04-03 02:27:21"}
  • {"files"=>["https://ndownloader.figshare.com/files/658562"], "description"=>"<p>Further explanation and R scripts are provided in <a href=\"http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.1001295#pbio.1001295.s016\" target=\"_blank\">Texts S1</a>, <a href=\"http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.1001295#pbio.1001295.s018\" target=\"_blank\">S3</a>, and S4.</p>", "links"=>[], "tags"=>["epidemiological", "methods"], "article_id"=>329052, "categories"=>["Science Policy", "Infectious Diseases", "Mathematics", "Genetics", "Biotechnology", "Ecology"], "users"=>["Steve E. Bellan", "Juliet R. C. Pulliam", "James C. Scott", "Jonathan Dushoff"], "doi"=>"https://dx.doi.org/10.1371/journal.pbio.1001295.t001", "stats"=>{"downloads"=>1, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Examples_of_epidemiological_methods_that_can_be_used_during_the_exercise_/329052", "title"=>"Examples of epidemiological methods that can be used during the exercise.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2012-04-03 02:30:52"}
  • {"files"=>["https://ndownloader.figshare.com/files/658474"], "description"=>"<p>(A) Five stochastic simulations of Muizenberg Mathematical Fever outbreaks using transmission parameters fit from the 2011 outbreak data but with only one infected individual initiating the outbreak (instead of four) but the same proportion initially immune (25%). In comparison to <a href=\"http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.1001295#pbio-1001295-g001\" target=\"_blank\">Figure 1A</a>, the outbreak would appear less likely to take off (with four simulated outbreaks burning out by Wednesday) if only one infected individual initiated the outbreak. (B) shows the distribution of outbreak size (total number infected before burnout) among simulated outbreaks in populations with different immune proportions at the outbreak initiation. The black line shows the median outbreak size among 1,000 stochastic simulations for each immune proportion and the gray regions show 10% quantiles of outbreak size. Only small levels of immunity are necessary to make outbreaks rare (with a fifth of the population immune 50% of simulated outbreaks never exceed more than two secondary cases), but just by chance larger outbreaks still occur. The script for production of and further detail on this figure are given in <a href=\"http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.1001295#pbio.1001295.s018\" target=\"_blank\">Text S3</a>.</p>", "links"=>[], "tags"=>["stochastic", "simulation", "vaccination"], "article_id"=>328965, "categories"=>["Science Policy", "Infectious Diseases", "Mathematics", "Genetics", "Biotechnology", "Ecology"], "users"=>["Steve E. Bellan", "Juliet R. C. Pulliam", "James C. Scott", "Jonathan Dushoff"], "doi"=>"https://dx.doi.org/10.1371/journal.pbio.1001295.g003", "stats"=>{"downloads"=>1, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Utility_of_stochastic_simulation_to_study_the_effect_of_vaccination_on_future_outbreaks_/328965", "title"=>"Utility of stochastic simulation to study the effect of vaccination on future outbreaks.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-04-03 02:29:25"}
  • {"files"=>["https://ndownloader.figshare.com/files/338141", "https://ndownloader.figshare.com/files/338167", "https://ndownloader.figshare.com/files/338185", "https://ndownloader.figshare.com/files/338206", "https://ndownloader.figshare.com/files/338222", "https://ndownloader.figshare.com/files/338240", "https://ndownloader.figshare.com/files/338264", "https://ndownloader.figshare.com/files/338280", "https://ndownloader.figshare.com/files/338449", "https://ndownloader.figshare.com/files/338576", "https://ndownloader.figshare.com/files/338704", "https://ndownloader.figshare.com/files/338863", "https://ndownloader.figshare.com/files/338965", "https://ndownloader.figshare.com/files/339162", "https://ndownloader.figshare.com/files/339351", "https://ndownloader.figshare.com/files/339450", "https://ndownloader.figshare.com/files/339626", "https://ndownloader.figshare.com/files/339695", "https://ndownloader.figshare.com/files/339715", "https://ndownloader.figshare.com/files/339738"], "description"=>"How to Make Epidemiological Training Infectious", "links"=>[], "tags"=>["epidemiological", "infectious"], "article_id"=>126838, "categories"=>["Cancer", "Science Policy", "Mathematics", "Genetics", "Biotechnology", "Ecology"], "users"=>["Steve E. Bellan", "Juliet R. C. Pulliam", "James C. Scott", "Jonathan Dushoff"], "doi"=>["https://dx.doi.org/10.1371/journal.pbio.1001295.s001", "https://dx.doi.org/10.1371/journal.pbio.1001295.s002", "https://dx.doi.org/10.1371/journal.pbio.1001295.s003", "https://dx.doi.org/10.1371/journal.pbio.1001295.s004", "https://dx.doi.org/10.1371/journal.pbio.1001295.s005", "https://dx.doi.org/10.1371/journal.pbio.1001295.s006", "https://dx.doi.org/10.1371/journal.pbio.1001295.s007", "https://dx.doi.org/10.1371/journal.pbio.1001295.s008", "https://dx.doi.org/10.1371/journal.pbio.1001295.s009", "https://dx.doi.org/10.1371/journal.pbio.1001295.s010", "https://dx.doi.org/10.1371/journal.pbio.1001295.s011", "https://dx.doi.org/10.1371/journal.pbio.1001295.s012", "https://dx.doi.org/10.1371/journal.pbio.1001295.s013", "https://dx.doi.org/10.1371/journal.pbio.1001295.s014", "https://dx.doi.org/10.1371/journal.pbio.1001295.s015", "https://dx.doi.org/10.1371/journal.pbio.1001295.s016", "https://dx.doi.org/10.1371/journal.pbio.1001295.s017", "https://dx.doi.org/10.1371/journal.pbio.1001295.s018", "https://dx.doi.org/10.1371/journal.pbio.1001295.s019", "https://dx.doi.org/10.1371/journal.pbio.1001295.s020"], "stats"=>{"downloads"=>40, "page_views"=>11, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/How_to_Make_Epidemiological_Training_Infectious/126838", "title"=>"How to Make Epidemiological Training Infectious", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2012-04-03 01:53:58"}
  • {"files"=>["https://ndownloader.figshare.com/files/658427"], "description"=>"<p>(A) Muizenberg Mathematical Fever 2011 outbreak data to illustrate how using a case definition with imperfect sensitivity (symptomatic disease) can cause nondifferential misclassification bias (the category of information bias where exposed and unexposed individuals are equally likely to be misclassified). Nondifferential misclassification biases the association between a risk factor and a disease outcome towards the null hypothesis of no association (odds ratio = 1). While attendance at the prior year's clinic was actually protective (black square and 95% CI), this bias was sufficient to cause the confidence interval for the odds ratio of this very protective variable to overlap (gray). (B) illustrates how a risk factor (arrival a day or more early to the clinic) that has no real association to a disease outcome can appear associated through confounding. Individuals who had attended the clinic in prior years were less likely to come to the clinic early and were also protected (i.e., A). Consequently, early attendance appeared associated with a higher risk of disease in a univariate analysis (gray) though the CI contains the null hypothesis of no association in a multivariate analysis that adjusts for prior attendance (black). The script for production of and further detail on this figure are given in <a href=\"http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.1001295#pbio.1001295.s018\" target=\"_blank\">Text S3</a>.</p>", "links"=>[], "tags"=>["confounding", "simulated", "outbreak"], "article_id"=>328911, "categories"=>["Science Policy", "Infectious Diseases", "Mathematics", "Genetics", "Biotechnology", "Ecology"], "users"=>["Steve E. Bellan", "Juliet R. C. Pulliam", "James C. Scott", "Jonathan Dushoff"], "doi"=>"https://dx.doi.org/10.1371/journal.pbio.1001295.g002", "stats"=>{"downloads"=>1, "page_views"=>12, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Demonstration_of_information_and_confounding_bias_with_simulated_outbreak_data_/328911", "title"=>"Demonstration of information and confounding bias with simulated outbreak data.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-04-03 02:28:31"}

PMC Usage Stats | Further Information

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Relative Metric

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