The Development of an Age-Structured Model for Trachoma Transmission Dynamics, Pathogenesis and Control
Publication Date
June 16, 2009
Journal
PLOS Neglected Tropical Diseases
Authors
Manoj Gambhir, Maria Gloria Basáñez, Matthew J. Burton, Anthony W. Solomon, et al
Volume
3
Issue
6
Pages
e462
DOI
https://dx.plos.org/10.1371/journal.pntd.0000462
Publisher URL
http://journals.plos.org/plosntds/article?id=10.1371%2Fjournal.pntd.0000462
PubMed
http://www.ncbi.nlm.nih.gov/pubmed/19529762
PubMed Central
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2691478
Europe PMC
http://europepmc.org/abstract/MED/19529762
Web of Science
000268401000013
Scopus
68349091596
Mendeley
http://www.mendeley.com/research/development-agestructured-model-trachoma-transmission-dynamics-pathogenesis-control
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Mendeley | Further Information

{"title"=>"The development of an age-structured model for trachoma transmission dynamics, pathogenesis and control", "type"=>"journal", "authors"=>[{"first_name"=>"Manoj", "last_name"=>"Gambhir", "scopus_author_id"=>"35279721500"}, {"first_name"=>"Maria Gloria", "last_name"=>"Basáñez", "scopus_author_id"=>"6701712698"}, {"first_name"=>"Matthew J.", "last_name"=>"Burton", "scopus_author_id"=>"7202920109"}, {"first_name"=>"Anthony W.", "last_name"=>"Solomon", "scopus_author_id"=>"7202208128"}, {"first_name"=>"Robin L.", "last_name"=>"Bailey", "scopus_author_id"=>"7402652011"}, {"first_name"=>"Martin J.", "last_name"=>"Holland", "scopus_author_id"=>"7201661621"}, {"first_name"=>"Isobel M.", "last_name"=>"Blake", "scopus_author_id"=>"26535580400"}, {"first_name"=>"Christl A.", "last_name"=>"Donnelly", "scopus_author_id"=>"35468127900"}, {"first_name"=>"Ibrahim", "last_name"=>"Jabr", "scopus_author_id"=>"34868179000"}, {"first_name"=>"David C.", "last_name"=>"Mabey", "scopus_author_id"=>"56400433900"}, {"first_name"=>"Nicholas C.", "last_name"=>"Grassly", "scopus_author_id"=>"6701838083"}], "year"=>2009, "source"=>"PLoS Neglected Tropical Diseases", "identifiers"=>{"issn"=>"1935-2735", "isbn"=>"1935-2735 (Electronic)", "pui"=>"355064762", "sgr"=>"68349091596", "doi"=>"10.1371/journal.pntd.0000462", "scopus"=>"2-s2.0-68349091596", "pmid"=>"19529762"}, "id"=>"579eea25-fc88-391b-925e-e7f5110795e4", "abstract"=>"BACKGROUND: Trachoma, the worldwide leading infectious cause of blindness, is due to repeated conjunctival infection with Chlamydia trachomatis. The effects of control interventions on population levels of infection and active disease can be promptly measured, but the effects on severe ocular sequelae require long-term monitoring. We present an age-structured mathematical model of trachoma transmission and disease to predict the impact of interventions on the prevalence of blinding trachoma. METHODOLOGY/PRINCIPAL FINDINGS: The model is based on the concept of multiple reinfections leading to progressive conjunctival scarring, trichiasis, corneal opacity and blindness. It also includes aspects of trachoma natural history, such as an increasing rate of recovery from infection and a decreasing chlamydial load with subsequent infections that depend upon a (presumed) acquired immunity that clears infection with age more rapidly. Parameters were estimated using maximum likelihood by fitting the model to pre-control infection prevalence data from hypo-, meso- and hyperendemic communities from The Gambia and Tanzania. The model reproduces key features of trachoma epidemiology: 1) the age-profile of infection prevalence, which increases to a peak at very young ages and declines at older ages; 2) a shift in this prevalence peak, toward younger ages in higher force of infection environments; 3) a raised overall profile of infection prevalence with higher force of infection; and 4) a rising profile, with age, of the prevalence of the ensuing severe sequelae (trachomatous scarring, trichiasis), as well as estimates of the number of infections that need to occur before these sequelae appear. CONCLUSIONS/SIGNIFICANCE: We present a framework that is sufficiently comprehensive to examine the outcomes of the A (antibiotic) component of the SAFE strategy on disease. The suitability of the model for representing population-level patterns of infection and disease sequelae is discussed in view of the individual processes leading to these patterns.", "link"=>"http://www.mendeley.com/research/development-agestructured-model-trachoma-transmission-dynamics-pathogenesis-control", "reader_count"=>50, "reader_count_by_academic_status"=>{"Unspecified"=>4, "Professor > Associate Professor"=>4, "Researcher"=>13, "Student > Doctoral Student"=>2, "Student > Ph. D. Student"=>4, "Student > Postgraduate"=>3, "Student > Master"=>9, "Other"=>5, "Lecturer > Senior Lecturer"=>3, "Professor"=>3}, "reader_count_by_user_role"=>{"Unspecified"=>4, "Professor > Associate Professor"=>4, "Researcher"=>13, "Student > Doctoral Student"=>2, "Student > Ph. D. Student"=>4, "Student > Postgraduate"=>3, "Student > Master"=>9, "Other"=>5, "Lecturer > Senior Lecturer"=>3, "Professor"=>3}, "reader_count_by_subject_area"=>{"Unspecified"=>7, "Agricultural and Biological Sciences"=>10, "Business, Management and Accounting"=>1, "Computer Science"=>1, "Engineering"=>1, "Biochemistry, Genetics and Molecular Biology"=>2, "Nursing and Health Professions"=>2, "Mathematics"=>1, "Medicine and Dentistry"=>18, "Neuroscience"=>1, "Psychology"=>1, "Social Sciences"=>3, "Immunology and Microbiology"=>2}, "reader_count_by_subdiscipline"=>{"Medicine and Dentistry"=>{"Medicine and Dentistry"=>18}, "Social Sciences"=>{"Social Sciences"=>3}, "Psychology"=>{"Psychology"=>1}, "Mathematics"=>{"Mathematics"=>1}, "Unspecified"=>{"Unspecified"=>7}, "Engineering"=>{"Engineering"=>1}, "Neuroscience"=>{"Neuroscience"=>1}, "Immunology and Microbiology"=>{"Immunology and Microbiology"=>2}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>10}, "Computer Science"=>{"Computer Science"=>1}, "Business, Management and Accounting"=>{"Business, Management and Accounting"=>1}, "Nursing and Health Professions"=>{"Nursing and Health Professions"=>2}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>2}}, "reader_count_by_country"=>{"United Kingdom"=>3, "Australia"=>1}, "group_count"=>4}

Scopus | Further Information

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/894448"], "description"=>"<p>Provenance of the data from three trachoma-endemic regions that are used in this paper for model fitting. Active disease is measured as trachoma follicular (TF) and/or trachoma inflammation (TI) on the World Health Organization simplified grading scheme <a href=\"http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0000462#pntd.0000462-Taylor3\" target=\"_blank\">[43]</a>.</p>", "links"=>[], "tags"=>["levels", "data-sets"], "article_id"=>564896, "categories"=>["Medicine"], "users"=>["Manoj Gambhir", "María-Gloria Basáñez", "Matthew J. Burton", "Anthony W. Solomon", "Robin L. Bailey", "Martin J. Holland", "Isobel M. Blake", "Christl A. Donnelly", "Ibrahim Jabr", "David C. Mabey", "Nicholas C. Grassly"], "doi"=>"https://dx.doi.org/10.1371/journal.pntd.0000462.t001", "stats"=>{"downloads"=>5, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Summary_of_the_infection_and_disease_levels_for_the_data_sets_used_in_this_study_/564896", "title"=>"Summary of the infection and disease levels for the data-sets used in this study.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2009-06-16 01:21:36"}
  • {"files"=>["https://ndownloader.figshare.com/files/442961"], "description"=>"<div><h3>Background</h3><p>Trachoma, the worldwide leading infectious cause of blindness, is due to repeated conjunctival infection with <em>Chlamydia trachomatis</em>. The effects of control interventions on population levels of infection and active disease can be promptly measured, but the effects on severe ocular sequelae require long-term monitoring. We present an age-structured mathematical model of trachoma transmission and disease to predict the impact of interventions on the prevalence of blinding trachoma.</p><h3>Methodology/Principal Findings</h3><p>The model is based on the concept of multiple reinfections leading to progressive conjunctival scarring, trichiasis, corneal opacity and blindness. It also includes aspects of trachoma natural history, such as an increasing rate of recovery from infection and a decreasing chlamydial load with subsequent infections that depend upon a (presumed) acquired immunity that clears infection with age more rapidly. Parameters were estimated using maximum likelihood by fitting the model to pre-control infection prevalence data from hypo-, meso- and hyperendemic communities from The Gambia and Tanzania. The model reproduces key features of trachoma epidemiology: 1) the age-profile of infection prevalence, which increases to a peak at very young ages and declines at older ages; 2) a shift in this prevalence peak, toward younger ages in higher force of infection environments; 3) a raised overall profile of infection prevalence with higher force of infection; and 4) a rising profile, with age, of the prevalence of the ensuing severe sequelae (trachomatous scarring, trichiasis), as well as estimates of the number of infections that need to occur before these sequelae appear.</p><h3>Conclusions/Significance</h3><p>We present a framework that is sufficiently comprehensive to examine the outcomes of the A (antibiotic) component of the SAFE strategy on disease. The suitability of the model for representing population-level patterns of infection and disease sequelae is discussed in view of the individual processes leading to these patterns.</p></div>", "links"=>[], "tags"=>["age-structured", "trachoma", "pathogenesis"], "article_id"=>147299, "categories"=>["Medicine"], "users"=>["Manoj Gambhir", "María-Gloria Basáñez", "Matthew J. Burton", "Anthony W. Solomon", "Robin L. Bailey", "Martin J. Holland", "Isobel M. Blake", "Christl A. Donnelly", "Ibrahim Jabr", "David C. Mabey", "Nicholas C. Grassly"], "doi"=>"https://dx.doi.org/10.1371/journal.pntd.0000462", "stats"=>{"downloads"=>2, "page_views"=>13, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/The_Development_of_an_Age_Structured_Model_for_Trachoma_Transmission_Dynamics_Pathogenesis_and_Control/147299", "title"=>"The Development of an Age-Structured Model for Trachoma Transmission Dynamics, Pathogenesis and Control", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2009-06-16 02:01:39"}
  • {"files"=>["https://ndownloader.figshare.com/files/894340"], "description"=>"<p>Prevalence curves for trachomatous scarring (solid line) and trachomatous trichiasis (dotted line) are shown along with the data from <a href=\"http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0000462#pntd.0000462-Munoz1\" target=\"_blank\">[28]</a> (circles (TS) and squares (TT) and 95%CI error bars), which were collected from the same district as the data of West <i>et al. </i><a href=\"http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0000462#pntd.0000462-West1\" target=\"_blank\">[25]</a> during the mid-1990s, prior to drug treatment interventions. An assumption made here is that the incidence of the sequelae did not change over the decade prior to the first mass drug administration.</p>", "links"=>[], "tags"=>["age-dependent", "prevalence", "sequelae", "hyperendemic"], "article_id"=>564788, "categories"=>["Medicine"], "users"=>["Manoj Gambhir", "María-Gloria Basáñez", "Matthew J. Burton", "Anthony W. Solomon", "Robin L. Bailey", "Martin J. Holland", "Isobel M. Blake", "Christl A. Donnelly", "Ibrahim Jabr", "David C. Mabey", "Nicholas C. Grassly"], "doi"=>"https://dx.doi.org/10.1371/journal.pntd.0000462.g005", "stats"=>{"downloads"=>0, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_age_dependent_prevalence_of_the_disease_sequelae_in_a_hyperendemic_setting_/564788", "title"=>"The age-dependent prevalence of the disease sequelae in a hyperendemic setting.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2009-06-16 01:19:48"}
  • {"files"=>["https://ndownloader.figshare.com/files/894140"], "description"=>"<p>A schematic representation of the way in which the model determines the presence of disease sequelae: trachomatous scarring (TS) and trachomatous trichiasis (TT) (TS and TT—but not corneal opacity, CO—are considered to be caused exclusively by reinfection with <i>C. trachomatis</i>). The simplest scheme is used: thresholds exist, along the ladder of infection, beyond which each of the sequelae are assumed to be present. Beyond the threshold corresponding to a specific sequela, that sequela is assumed to be observed, e.g. when the threshold for TT has been reached, both TT and TS are observed.</p>", "links"=>[], "tags"=>["sequelae"], "article_id"=>564593, "categories"=>["Medicine"], "users"=>["Manoj Gambhir", "María-Gloria Basáñez", "Matthew J. Burton", "Anthony W. Solomon", "Robin L. Bailey", "Martin J. Holland", "Isobel M. Blake", "Christl A. Donnelly", "Ibrahim Jabr", "David C. Mabey", "Nicholas C. Grassly"], "doi"=>"https://dx.doi.org/10.1371/journal.pntd.0000462.g002", "stats"=>{"downloads"=>0, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Incorporating_disease_sequelae_into_the_model_/564593", "title"=>"Incorporating disease sequelae into the model.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2009-06-16 01:16:33"}
  • {"files"=>["https://ndownloader.figshare.com/files/894076"], "description"=>"<p>A compartmental diagram illustrating the model described in the text. Each susceptible and infected compartment is connected to the compartment above so that the population passes up a ‘ladder’ of infection. The subscript <i>i</i> corresponds to the number of prior infections experienced.</p>", "links"=>[], "tags"=>["diagram"], "article_id"=>564526, "categories"=>["Medicine"], "users"=>["Manoj Gambhir", "María-Gloria Basáñez", "Matthew J. Burton", "Anthony W. Solomon", "Robin L. Bailey", "Martin J. Holland", "Isobel M. Blake", "Christl A. Donnelly", "Ibrahim Jabr", "David C. Mabey", "Nicholas C. Grassly"], "doi"=>"https://dx.doi.org/10.1371/journal.pntd.0000462.g001", "stats"=>{"downloads"=>2, "page_views"=>10, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Compartmental_diagram_of_the_model_/564526", "title"=>"Compartmental diagram of the model.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2009-06-16 01:15:26"}
  • {"files"=>["https://ndownloader.figshare.com/files/894201"], "description"=>"<p>Age-profiles generated by the maximum likelihood parameter estimates to the hyperendemic data set of West <i>et al... </i><a href=\"http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0000462#pntd.0000462-West1\" target=\"_blank\">[25]</a> and—for the recovery rate—Bailey <i>et al... </i><a href=\"http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0000462#pntd.0000462-Bailey1\" target=\"_blank\">[7]</a>, re-analyzed by Grassly <i>et al... </i><a href=\"http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0000462#pntd.0000462-Grassly1\" target=\"_blank\">[3]</a> (data displayed as square data points with 95% confidence intervals, and model fits shown as solid lines): A) the infectivity, which is proportional to the bacterial load, measured by quantitative PCR; B) duration of infection.</p>", "links"=>[], "tags"=>["fits", "hyperendemic"], "article_id"=>564650, "categories"=>["Medicine"], "users"=>["Manoj Gambhir", "María-Gloria Basáñez", "Matthew J. Burton", "Anthony W. Solomon", "Robin L. Bailey", "Martin J. Holland", "Isobel M. Blake", "Christl A. Donnelly", "Ibrahim Jabr", "David C. Mabey", "Nicholas C. Grassly"], "doi"=>"https://dx.doi.org/10.1371/journal.pntd.0000462.g003", "stats"=>{"downloads"=>0, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Model_fits_to_hyperendemic_data_/564650", "title"=>"Model fits to hyperendemic data.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2009-06-16 01:17:30"}
  • {"files"=>["https://ndownloader.figshare.com/files/894405"], "description"=>"<p>Parameter definitions and estimates, with 95% confidence intervals, for the model obtained through maximum likelihood fitting using a function combining infection prevalence, bacterial load, and recovery rate data from a hyperendemic setting in Tanzania. The transmission parameters for the meso- and hypoendemic settings were estimated by maximum likelihood by fitting to prevalence data only. The parameter symbols refer to the model definition detailed in <a href=\"http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0000462#pntd.0000462.s001\" target=\"_blank\">Text S1</a>.</p>", "links"=>[], "tags"=>["parameter", "definitions"], "article_id"=>564851, "categories"=>["Medicine"], "users"=>["Manoj Gambhir", "María-Gloria Basáñez", "Matthew J. Burton", "Anthony W. Solomon", "Robin L. Bailey", "Martin J. Holland", "Isobel M. Blake", "Christl A. Donnelly", "Ibrahim Jabr", "David C. Mabey", "Nicholas C. Grassly"], "doi"=>"https://dx.doi.org/10.1371/journal.pntd.0000462.t002", "stats"=>{"downloads"=>1, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Model_parameter_definitions_and_estimates_/564851", "title"=>"Model parameter definitions and estimates.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2009-06-16 01:20:51"}
  • {"files"=>["https://ndownloader.figshare.com/files/894272"], "description"=>"<p>Age-profiles of the prevalence of infection generated by fitting the model (solid lines) to the data (squares and 95% confidence interval error bars) from (A) Kongwa, Tanzania <a href=\"http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0000462#pntd.0000462-West1\" target=\"_blank\">[25]</a> (B) Rombo, Tanzania <a href=\"http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0000462#pntd.0000462-Solomon2\" target=\"_blank\">[24]</a> and (C) Upper Saloum, The Gambia <a href=\"http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0000462#pntd.0000462-Burton1\" target=\"_blank\">[22]</a>. The dotted line in (A) corresponds to a model-fit to the data from Kongwa, Tanzania assuming a lower number of true infection positives, namely those that were determined to harbour chlamydial loads as measured by quantitative PCR (see main text and <a href=\"http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0000462#pntd.0000462.s001\" target=\"_blank\">Text S1</a>). Note the different scale on the y-axis of (A) in comparison to (B) and (C).</p>", "links"=>[], "tags"=>["fits", "endemic"], "article_id"=>564722, "categories"=>["Medicine"], "users"=>["Manoj Gambhir", "María-Gloria Basáñez", "Matthew J. Burton", "Anthony W. Solomon", "Robin L. Bailey", "Martin J. Holland", "Isobel M. Blake", "Christl A. Donnelly", "Ibrahim Jabr", "David C. Mabey", "Nicholas C. Grassly"], "doi"=>"https://dx.doi.org/10.1371/journal.pntd.0000462.g004", "stats"=>{"downloads"=>1, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Model_fits_to_data_from_three_endemic_settings_/564722", "title"=>"Model fits to data from three endemic settings.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2009-06-16 01:18:42"}

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  • {"unique-ip"=>"12", "full-text"=>"10", "pdf"=>"1", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"1", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"12"}
  • {"unique-ip"=>"14", "full-text"=>"19", "pdf"=>"3", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2020", "month"=>"2"}
  • {"unique-ip"=>"25", "full-text"=>"34", "pdf"=>"5", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"3", "cited-by"=>"0", "year"=>"2020", "month"=>"3"}
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Relative Metric

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