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{"title"=>"Using Routine Surveillance Data to Estimate the Epidemic Potential of Emerging Zoonoses: Application to the Emergence of US Swine Origin Influenza A H3N2v Virus", "type"=>"journal", "authors"=>[{"first_name"=>"Simon", "last_name"=>"Cauchemez", "scopus_author_id"=>"11940936200"}, {"first_name"=>"Scott", "last_name"=>"Epperson", "scopus_author_id"=>"23975632000"}, {"first_name"=>"Matthew", "last_name"=>"Biggerstaff", "scopus_author_id"=>"16314774100"}, {"first_name"=>"David", "last_name"=>"Swerdlow", "scopus_author_id"=>"7007016524"}, {"first_name"=>"Lyn", "last_name"=>"Finelli", "scopus_author_id"=>"7007050800"}, {"first_name"=>"Neil M.", "last_name"=>"Ferguson", "scopus_author_id"=>"7103246319"}], "year"=>2013, "source"=>"PLoS Medicine", "identifiers"=>{"issn"=>"15491277", "scopus"=>"2-s2.0-84875461152", "sgr"=>"84875461152", "pui"=>"368602098", "isbn"=>"1549-1676", "pmid"=>"23472057", "doi"=>"10.1371/journal.pmed.1001399"}, "id"=>"4f5d4055-d925-3f57-b648-456d9fd58ce9", "abstract"=>"BACKGROUND: Prior to emergence in human populations, zoonoses such as SARS cause occasional infections in human populations exposed to reservoir species. The risk of widespread epidemics in humans can be assessed by monitoring the reproduction number R (average number of persons infected by a human case). However, until now, estimating R required detailed outbreak investigations of human clusters, for which resources and expertise are not always available. Additionally, existing methods do not correct for important selection and under-ascertainment biases. Here, we present simple estimation methods that overcome many of these limitations.\\n\\nMETHODS AND FINDINGS: Our approach is based on a parsimonious mathematical model of disease transmission and only requires data collected through routine surveillance and standard case investigations. We apply it to assess the transmissibility of swine-origin influenza A H3N2v-M virus in the US, Nipah virus in Malaysia and Bangladesh, and also present a non-zoonotic example (cholera in the Dominican Republic). Estimation is based on two simple summary statistics, the proportion infected by the natural reservoir among detected cases (G) and among the subset of the first detected cases in each cluster (F). If detection of a case does not affect detection of other cases from the same cluster, we find that R can be estimated by 1-G; otherwise R can be estimated by 1-F when the case detection rate is low. In more general cases, bounds on R can still be derived.\\n\\nCONCLUSIONS: We have developed a simple approach with limited data requirements that enables robust assessment of the risks posed by emerging zoonoses. We illustrate this by deriving transmissibility estimates for the H3N2v-M virus, an important step in evaluating the possible pandemic threat posed by this virus. Please see later in the article for the Editors' Summary.", "link"=>"http://www.mendeley.com/research/using-routine-surveillance-data-estimate-epidemic-potential-emerging-zoonoses-application-emergence", "reader_count"=>92, "reader_count_by_academic_status"=>{"Professor > Associate Professor"=>6, "Researcher"=>30, "Student > Doctoral Student"=>3, "Student > Ph. D. Student"=>28, "Student > Postgraduate"=>1, "Student > Master"=>12, "Other"=>3, "Student > Bachelor"=>5, "Professor"=>4}, "reader_count_by_user_role"=>{"Professor > Associate Professor"=>6, "Researcher"=>30, "Student > Doctoral Student"=>3, "Student > Ph. D. Student"=>28, "Student > Postgraduate"=>1, "Student > Master"=>12, "Other"=>3, "Student > Bachelor"=>5, "Professor"=>4}, "reader_count_by_subject_area"=>{"Unspecified"=>3, "Agricultural and Biological Sciences"=>36, "Veterinary Science and Veterinary Medicine"=>4, "Computer Science"=>3, "Earth and Planetary Sciences"=>1, "Economics, Econometrics and Finance"=>1, "Environmental Science"=>5, "Biochemistry, Genetics and Molecular Biology"=>2, "Nursing and Health Professions"=>1, "Mathematics"=>6, "Medicine and Dentistry"=>26, "Physics and Astronomy"=>1, "Social Sciences"=>1, "Immunology and Microbiology"=>2}, "reader_count_by_subdiscipline"=>{"Medicine and Dentistry"=>{"Medicine and Dentistry"=>26}, "Social Sciences"=>{"Social Sciences"=>1}, "Physics and Astronomy"=>{"Physics and Astronomy"=>1}, "Mathematics"=>{"Mathematics"=>6}, "Unspecified"=>{"Unspecified"=>3}, "Environmental Science"=>{"Environmental Science"=>5}, "Earth and Planetary Sciences"=>{"Earth and Planetary Sciences"=>1}, "Economics, Econometrics and Finance"=>{"Economics, Econometrics and Finance"=>1}, "Immunology and Microbiology"=>{"Immunology and Microbiology"=>2}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>36}, "Computer Science"=>{"Computer Science"=>3}, "Nursing and Health Professions"=>{"Nursing and Health Professions"=>1}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>2}, "Veterinary Science and Veterinary Medicine"=>{"Veterinary Science and Veterinary Medicine"=>4}}, "reader_count_by_country"=>{"Canada"=>2, "Vietnam"=>1, "United States"=>7, "Serbia and Montenegro"=>1, "Denmark"=>1, "United Kingdom"=>2, "Australia"=>2, "France"=>1, "Spain"=>1}, "group_count"=>4}

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• http://doi.org/10.1371/journal.pcbi.1005257
• http://doi.org/10.15585/mmwr.mm6548a4
• http://doi.org/10.1093/cid/civ345
• http://doi.org/10.1017/S0950268818002881
• http://doi.org/10.1371/journal.pntd.0004957
• http://doi.org/10.1186/1741-7015-11-214
• http://doi.org/10.1126/science.1251449
• http://doi.org/10.1016/j.epidem.2014.09.002
• http://doi.org/10.1093/cid/cit298
• http://doi.org/10.1186/s12985-014-0194-z
• http://doi.org/10.1016/j.pt.2019.01.009
• http://doi.org/10.1093/cid/ciy541
• http://doi.org/10.1017/S0950268814000946
• http://doi.org/10.1098/rstb.2016.0164
• http://doi.org/10.1016/j.tim.2014.12.002
• http://doi.org/10.1038/s41426-018-0086-1
• http://doi.org/10.1038/emi.2014.5
• http://doi.org/10.1093/infdis/jit589
• http://doi.org/10.1016/j.jtbi.2019.01.033
• http://doi.org/10.1186/1742-4682-10-30
• http://doi.org/10.1186/s12879-017-2394-9
• http://doi.org/10.1016/S0140-6736(17)30129-0
• http://doi.org/10.1093/ve/vew001
• http://doi.org/10.1126/science.aaa4339
• http://doi.org/10.1371/journal.pone.0173996
• http://doi.org/10.1007/s11908-013-0341-5

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Figshare

• {"files"=>["https://ndownloader.figshare.com/files/978124"], "description"=>"<p>In this simulation study, 10,000 clusters are generated for <i>R</i> = 0.5 and <i>k</i> = 0.5; six case detection rates are considered (1%, 10%, 20%, 30%, 40%, 50%; with a number of detected clusters that is 207, 1,706, 2,922, 4,092, 5,118, 6,125, respectively). First, the formula 1−<i>G</i> gives point estimates for <i>R</i> (in the range 0.48–0.51 depending on the case detection rate). For given values of <i>R</i> and the overdispersion parameter <i>k</i>, it is possible to plot the relationship between <i>F</i> and the case detection rate. Black lines in the figure correspond to <i>R</i> = 0.5 (dashed line: <i>k</i> = 0.5; plain lines: <i>k</i> = 0.16 and <i>k</i> = 5). Colour triangles show estimates of the case detection rate obtained for each dataset when <i>k</i> is assumed to be known. When <i>k</i> is unknown, vertical colour plain lines give the range of values consistent with <i>k</i> in interval 0.16–5. Horizontal colour dashed lines indicate true case detection rate.</p>", "links"=>[], "tags"=>["detection", "outbreak"], "article_id"=>645086, "categories"=>["Microbiology", "Biological Sciences", "Infectious Diseases", "Biotechnology"], "users"=>["Simon Cauchemez", "Scott Epperson", "Matthew Biggerstaff", "David Swerdlow", "Lyn Finelli", "Neil M. Ferguson"], "doi"=>"https://dx.doi.org/10.1371/journal.pmed.1001399.g006", "stats"=>{"downloads"=>1, "page_views"=>27, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Estimating_R_and_the_case_detection_rate_when_both_summary_statistics_F_and_G_are_available_in_surveillance_scenario_1_i_e_detection_of_a_case_does_not_trigger_an_outbreak_investigation_/645086", "title"=>"Estimating <i>R</i> and the case detection rate when both summary statistics <i>F</i> and <i>G</i> are available in surveillance scenario 1 (i.e., detection of a case does not trigger an outbreak investigation).", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-03-06 13:37:00"}
• {"files"=>["https://ndownloader.figshare.com/files/978105"], "description"=>"<p>(A) True distribution of cluster sizes (red) and distribution for clusters that are detected (blue), for <i>R</i> = 0.7. (B) Asymptotic estimate of the reproduction number as a function of the true reproduction number, derived from distribution of cluster sizes of detected clusters. The detection rate is set to ρ = 1% and the overdispersion parameter of the offspring distribution <i>k</i> = 0.5.</p>", "links"=>[], "tags"=>["estimation", "reproduction"], "article_id"=>645067, "categories"=>["Microbiology", "Biological Sciences", "Infectious Diseases", "Biotechnology"], "users"=>["Simon Cauchemez", "Scott Epperson", "Matthew Biggerstaff", "David Swerdlow", "Lyn Finelli", "Neil M. Ferguson"], "doi"=>"https://dx.doi.org/10.1371/journal.pmed.1001399.g001", "stats"=>{"downloads"=>0, "page_views"=>2, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Selection_bias_and_the_estimation_of_the_reproduction_number_R_/645067", "title"=>"Selection bias and the estimation of the reproduction number <i>R</i>.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-03-06 13:32:10"}
• {"files"=>["https://ndownloader.figshare.com/files/1006212"], "description"=>"<p>We consider three scenarios for the case-to-case variation in infectiousness: high (i.e., most transmission events are caused by a small proportion of cases like for SARS; k  =  0.16), medium (k  =  0.5), and low (k  =  5) <a href=\"http://www.plosmedicine.org/article/info:doi/10.1371/journal.pmed.1001399#pmed.1001399-CDC7\" target=\"_blank\">[12]</a>.</p>a<p><i>n</i> is the number of clusters. <i>F</i> is the proportion of first detected cases in each cluster that were infected by the reservoir. The number of first detected cases that were infected by the reservoir was 20 for all strains, three for H3N2v-M variant, and 17 for variants other than H3N2v-M.</p>", "links"=>[], "tags"=>["h3n2v-m", "variants", "scenarios", "detection", "overdispersion", "offspring"], "article_id"=>666833, "categories"=>["Microbiology", "Biological Sciences", "Infectious Diseases", "Biotechnology"], "users"=>["Simon Cauchemez", "Scott Epperson", "Matthew Biggerstaff", "David Swerdlow", "Lyn Finelli", "Neil M. Ferguson"], "doi"=>"https://dx.doi.org/10.1371/journal.pmed.1001399.t001", "stats"=>{"downloads"=>1, "page_views"=>12, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Estimate_95_CI_of_R_for_all_strains_the_H3N2v_M_variant_and_variants_other_than_H3N2v_M_for_different_scenarios_of_detection_and_overdispersion_in_the_offspring_distribution_/666833", "title"=>"Estimate (95% CI) of <i>R</i> for all strains, the H3N2v-M variant, and variants other than H3N2v-M, for different scenarios of detection and overdispersion in the offspring distribution.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-03-05 01:53:53"}
• {"files"=>["https://ndownloader.figshare.com/files/978123"], "description"=>"<p>1−<i>F</i> always acts as a lower bound for <i>R</i>. Furthermore, an upper bound for <i>R</i> can be obtained if it is possible to specify an upper bound for the case detection rate and a lower bound for the overdispersion parameter <i>k</i> (see <a href=\"http://www.plosmedicine.org/article/info:doi/10.1371/journal.pmed.1001399#pmed.1001399.s009\" target=\"_blank\">Text S1</a>). The figure shows lower and upper bound for R as a function of . We specify which corresponds to the SARS scenario with superspreading events.</p>", "links"=>[], "tags"=>["detection", "overdispersion", "parameter", "estimates", "reproduction"], "article_id"=>645085, "categories"=>["Microbiology", "Biological Sciences", "Infectious Diseases", "Biotechnology"], "users"=>["Simon Cauchemez", "Scott Epperson", "Matthew Biggerstaff", "David Swerdlow", "Lyn Finelli", "Neil M. Ferguson"], "doi"=>"https://dx.doi.org/10.1371/journal.pmed.1001399.g005", "stats"=>{"downloads"=>0, "page_views"=>24, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Impact_of_uncertainty_on_the_case_detection_rate_and_the_overdispersion_parameter_on_estimates_of_the_reproduction_number_R_/645085", "title"=>"Impact of uncertainty on the case detection rate and the overdispersion parameter on estimates of the reproduction number R.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-03-06 13:36:47"}
• {"files"=>["https://ndownloader.figshare.com/files/978108"], "description"=>"<p>The number of secondary cases caused by individual human cases is modelled with a Negative binomial distribution with parameters <i>R</i> and <i>k</i>, where <i>k</i> is the overdispersion parameter <a href=\"http://www.plosmedicine.org/article/info:doi/10.1371/journal.pmed.1001399#pmed.1001399-LloydSmith2\" target=\"_blank\">[12]</a>. We consider three scenarios for the case-to-case variation in infectiousness: high (i.e., most transmission events are caused by a small proportion of cases like for SARS; <i>k</i> = 0.16), medium (<i>k</i> = 0.5), and low (<i>k</i> = 5) <a href=\"http://www.plosmedicine.org/article/info:doi/10.1371/journal.pmed.1001399#pmed.1001399-LloydSmith2\" target=\"_blank\">[12]</a>.</p>", "links"=>[], "tags"=>["infected", "reproduction"], "article_id"=>645070, "categories"=>["Microbiology", "Biological Sciences", "Infectious Diseases", "Biotechnology"], "users"=>["Simon Cauchemez", "Scott Epperson", "Matthew Biggerstaff", "David Swerdlow", "Lyn Finelli", "Neil M. Ferguson"], "doi"=>"https://dx.doi.org/10.1371/journal.pmed.1001399.g002", "stats"=>{"downloads"=>1, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Probability_F_that_the_first_detected_case_in_a_cluster_was_infected_by_the_reservoir_as_a_function_of_the_reproduction_number_R_/645070", "title"=>"Probability <i>F</i> that the first detected case in a cluster was infected by the reservoir, as a function of the reproduction number <i>R</i>.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-03-06 13:32:34"}
• {"files"=>["https://ndownloader.figshare.com/files/977750", "https://ndownloader.figshare.com/files/977757", "https://ndownloader.figshare.com/files/977758", "https://ndownloader.figshare.com/files/977761", "https://ndownloader.figshare.com/files/977764", "https://ndownloader.figshare.com/files/977768", "https://ndownloader.figshare.com/files/977775", "https://ndownloader.figshare.com/files/977779", "https://ndownloader.figshare.com/files/977785"], "description"=>"<div><p>Background</p><p>Prior to emergence in human populations, zoonoses such as SARS cause occasional infections in human populations exposed to reservoir species. The risk of widespread epidemics in humans can be assessed by monitoring the reproduction number <i>R</i> (average number of persons infected by a human case). However, until now, estimating <i>R</i> required detailed outbreak investigations of human clusters, for which resources and expertise are not always available. Additionally, existing methods do not correct for important selection and under-ascertainment biases. Here, we present simple estimation methods that overcome many of these limitations.</p> <p>Methods and Findings</p><p>Our approach is based on a parsimonious mathematical model of disease transmission and only requires data collected through routine surveillance and standard case investigations. We apply it to assess the transmissibility of swine-origin influenza A H3N2v-M virus in the US, Nipah virus in Malaysia and Bangladesh, and also present a non-zoonotic example (cholera in the Dominican Republic). Estimation is based on two simple summary statistics, the proportion infected by the natural reservoir among detected cases (<i>G</i>) and among the subset of the first detected cases in each cluster (<i>F</i>). If detection of a case does not affect detection of other cases from the same cluster, we find that <i>R</i> can be estimated by 1−<i>G</i>; otherwise <i>R</i> can be estimated by 1−<i>F</i> when the case detection rate is low. In more general cases, bounds on <i>R</i> can still be derived.</p> <p>Conclusions</p><p>We have developed a simple approach with limited data requirements that enables robust assessment of the risks posed by emerging zoonoses. We illustrate this by deriving transmissibility estimates for the H3N2v-M virus, an important step in evaluating the possible pandemic threat posed by this virus.</p> <p> <i>Please see later in the article for the Editors' Summary</i></p> </div>", "links"=>[], "tags"=>["emergence", "swine", "influenza", "h3n2v", "virus"], "article_id"=>644843, "categories"=>["Microbiology", "Biological Sciences", "Infectious Diseases", "Biotechnology"], "users"=>["Simon Cauchemez", "Scott Epperson", "Matthew Biggerstaff", "David Swerdlow", "Lyn Finelli", "Neil M. Ferguson"], "doi"=>["https://dx.doi.org/10.1371/journal.pmed.1001399.s001", "https://dx.doi.org/10.1371/journal.pmed.1001399.s002", "https://dx.doi.org/10.1371/journal.pmed.1001399.s003", "https://dx.doi.org/10.1371/journal.pmed.1001399.s004", "https://dx.doi.org/10.1371/journal.pmed.1001399.s005", "https://dx.doi.org/10.1371/journal.pmed.1001399.s006", "https://dx.doi.org/10.1371/journal.pmed.1001399.s007", "https://dx.doi.org/10.1371/journal.pmed.1001399.s008", "https://dx.doi.org/10.1371/journal.pmed.1001399.s009"], "stats"=>{"downloads"=>79, "page_views"=>32, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Using_Routine_Surveillance_Data_to_Estimate_the_Epidemic_Potential_of_Emerging_Zoonoses_Application_to_the_Emergence_of_US_Swine_Origin_Influenza_A_H3N2v_Virus__/644843", "title"=>"Using Routine Surveillance Data to Estimate the Epidemic Potential of Emerging Zoonoses: Application to the Emergence of US Swine Origin Influenza A H3N2v Virus", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2013-03-06 09:20:12"}
• {"files"=>["https://ndownloader.figshare.com/files/978118"], "description"=>"<p><b>(A)</b> Absolute bias, standard deviation and root mean square error (RMSE) of estimator 1−<i>F</i> as a function of the case detection rate of the surveillance system, in a scenario with reproduction number <i>R</i> = 0.5, overdispersion parameter <i>k</i> = 0.5, and where <i>n</i> = 10,000 clusters occur in the country. Optimum trade-off between bias and precision is obtained when RMSE is minimum. (B) Optimum case detection rate as a function of the reproduction number <i>R</i>, for different values of overdispersion parameter <i>k</i> and of the number <i>n</i> of clusters. (C) Bias at the optimum case detection rate.</p>", "links"=>[], "tags"=>["precision", "estimator", "reproduction"], "article_id"=>645080, "categories"=>["Microbiology", "Biological Sciences", "Infectious Diseases", "Biotechnology"], "users"=>["Simon Cauchemez", "Scott Epperson", "Matthew Biggerstaff", "David Swerdlow", "Lyn Finelli", "Neil M. Ferguson"], "doi"=>"https://dx.doi.org/10.1371/journal.pmed.1001399.g004", "stats"=>{"downloads"=>1, "page_views"=>33, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Trade_off_between_bias_and_precision_for_the_estimator_1_F_of_the_reproduction_number_R_/645080", "title"=>"Trade-off between bias and precision for the estimator 1−<i>F</i> of the reproduction number <i>R</i>.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-03-06 13:34:26"}
• {"files"=>["https://ndownloader.figshare.com/files/978112"], "description"=>"<p>The values of the overdispersion parameter <i>k</i> and case detection rate ρ under which we can reject the hypothesis that <i>R</i>≥1 are indicated with a black dot, and parameter values for which the assumption cannot be rejected are shown in red.</p>", "links"=>[], "tags"=>["public health and epidemiology", "Infectious diseases", "microbiology", "Computational biology"], "article_id"=>645074, "categories"=>["Microbiology", "Biological Sciences", "Infectious Diseases", "Biotechnology"], "users"=>["Simon Cauchemez", "Scott Epperson", "Matthew Biggerstaff", "David Swerdlow", "Lyn Finelli", "Neil M. Ferguson"], "doi"=>"https://dx.doi.org/10.1371/journal.pmed.1001399.g003", "stats"=>{"downloads"=>0, "page_views"=>23, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Is_R_8805_1_for_H3N2v_M_/645074", "title"=>"Is <i>R≥1</i> for H3N2v-M?", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-03-06 13:33:09"}

PMC Usage Stats | Further Information

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Relative Metric

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F1000Prime