Estimating the Global Clinical Burden of Plasmodium falciparum Malaria in 2007
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{"title"=>"Estimating the global clinical burden of plasmodium falciparum malaria in 2007", "type"=>"journal", "authors"=>[{"first_name"=>"Simon I.", "last_name"=>"Hay", "scopus_author_id"=>"7101875313"}, {"first_name"=>"Emelda A.", "last_name"=>"Okiro", "scopus_author_id"=>"10640311700"}, {"first_name"=>"Peter W.", "last_name"=>"Gething", "scopus_author_id"=>"57193559189"}, {"first_name"=>"Anand P.", "last_name"=>"Patil", "scopus_author_id"=>"26425192800"}, {"first_name"=>"Andrew J.", "last_name"=>"Tatem", "scopus_author_id"=>"6603035928"}, {"first_name"=>"Carlos A.", "last_name"=>"Guerra", "scopus_author_id"=>"57128857000"}, {"first_name"=>"Robert W.", "last_name"=>"Snow", "scopus_author_id"=>"56771582900"}], "year"=>2010, "source"=>"PLoS Medicine", "identifiers"=>{"sgr"=>"77955027421", "doi"=>"10.1371/journal.pmed.1000290", "pui"=>"359258157", "pmid"=>"20563310", "scopus"=>"2-s2.0-77955027421", "issn"=>"15491277", "isbn"=>"1549-1676 (Electronic)\\r1549-1277 (Linking)"}, "id"=>"71a65993-d0c3-3b43-a202-48d4a2334bd1", "abstract"=>"BACKGROUND: The epidemiology of malaria makes surveillance-based methods of estimating its disease burden problematic. Cartographic approaches have provided alternative malaria burden estimates, but there remains widespread misunderstanding about their derivation and fidelity. The aims of this study are to present a new cartographic technique and its application for deriving global clinical burden estimates of Plasmodium falciparum malaria for 2007, and to compare these estimates and their likely precision with those derived under existing surveillance-based approaches.\\n\\nMETHODS AND FINDINGS: In seven of the 87 countries endemic for P. falciparum malaria, the health reporting infrastructure was deemed sufficiently rigorous for case reports to be used verbatim. In the remaining countries, the mapped extent of unstable and stable P. falciparum malaria transmission was first determined. Estimates of the plausible incidence range of clinical cases were then calculated within the spatial limits of unstable transmission. A modelled relationship between clinical incidence and prevalence was used, together with new maps of P. falciparum malaria endemicity, to estimate incidence in areas of stable transmission, and geostatistical joint simulation was used to quantify uncertainty in these estimates at national, regional, and global scales. Combining these estimates for all areas of transmission risk resulted in 451 million (95% credible interval 349-552 million) clinical cases of P. falciparum malaria in 2007. Almost all of this burden of morbidity occurred in areas of stable transmission. More than half of all estimated P. falciparum clinical cases and associated uncertainty occurred in India, Nigeria, the Democratic Republic of the Congo (DRC), and Myanmar (Burma), where 1.405 billion people are at risk. Recent surveillance-based methods of burden estimation were then reviewed and discrepancies in national estimates explored. When these cartographically derived national estimates were ranked according to their relative uncertainty and replaced by surveillance-based estimates in the least certain half, 98% of the global clinical burden continued to be estimated by cartographic techniques.\\n\\nCONCLUSIONS AND SIGNIFICANCE: Cartographic approaches to burden estimation provide a globally consistent measure of malaria morbidity of known fidelity, and they represent the only plausible method in those malaria-endemic countries with nonfunctional national surveillance. Unacceptable uncertainty in the clinical burden of malaria in only four countries confounds our ability to evaluate needs and monitor progress toward international targets for malaria control at the global scale. National prevalence surveys in each nation would reduce this uncertainty profoundly. Opportunities for further reducing uncertainty in clinical burden estimates by hybridizing alternative burden estimation procedures are also evaluated.", "link"=>"http://www.mendeley.com/research/estimating-global-clinical-burden-plasmodium-falciparum-malaria-2007-6", "reader_count"=>238, "reader_count_by_academic_status"=>{"Unspecified"=>1, "Professor > Associate Professor"=>10, "Researcher"=>40, "Student > Doctoral Student"=>16, "Student > Ph. D. Student"=>55, "Student > Postgraduate"=>14, "Other"=>8, "Student > Master"=>57, "Student > Bachelor"=>27, "Lecturer"=>5, "Professor"=>5}, "reader_count_by_user_role"=>{"Unspecified"=>1, "Professor > Associate Professor"=>10, "Researcher"=>40, "Student > Doctoral Student"=>16, "Student > Ph. D. Student"=>55, "Student > Postgraduate"=>14, "Other"=>8, "Student > Master"=>57, "Student > Bachelor"=>27, "Lecturer"=>5, "Professor"=>5}, "reader_count_by_subject_area"=>{"Unspecified"=>13, "Agricultural and Biological Sciences"=>104, "Chemistry"=>7, "Computer Science"=>2, "Engineering"=>1, "Environmental Science"=>12, "Nursing and Health Professions"=>4, "Biochemistry, Genetics and Molecular Biology"=>12, "Mathematics"=>5, "Medicine and Dentistry"=>60, "Pharmacology, Toxicology and Pharmaceutical Science"=>3, "Psychology"=>1, "Social Sciences"=>9, "Immunology and Microbiology"=>5}, "reader_count_by_subdiscipline"=>{"Medicine and Dentistry"=>{"Medicine and Dentistry"=>60}, "Social Sciences"=>{"Social Sciences"=>9}, "Psychology"=>{"Psychology"=>1}, "Mathematics"=>{"Mathematics"=>5}, "Unspecified"=>{"Unspecified"=>13}, "Environmental Science"=>{"Environmental Science"=>12}, "Pharmacology, Toxicology and Pharmaceutical Science"=>{"Pharmacology, Toxicology and Pharmaceutical Science"=>3}, "Engineering"=>{"Engineering"=>1}, "Chemistry"=>{"Chemistry"=>7}, "Immunology and Microbiology"=>{"Immunology and Microbiology"=>5}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>104}, "Computer Science"=>{"Computer Science"=>2}, "Nursing and Health Professions"=>{"Nursing and Health Professions"=>4}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>12}}, "reader_count_by_country"=>{"United States"=>4, "Tanzania"=>1, "United Kingdom"=>1, "Kenya"=>1, "Switzerland"=>1, "India"=>1, "Spain"=>1, "Malawi"=>1, "Brazil"=>2, "Italy"=>1, "Mexico"=>1, "France"=>1, "Bulgaria"=>1}, "group_count"=>9}

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  • {"files"=>["https://ndownloader.figshare.com/files/420627", "https://ndownloader.figshare.com/files/420711"], "description"=>"<div><h3>Background</h3><p>The epidemiology of malaria makes surveillance-based methods of estimating its disease burden problematic. Cartographic approaches have provided alternative malaria burden estimates, but there remains widespread misunderstanding about their derivation and fidelity. The aims of this study are to present a new cartographic technique and its application for deriving global clinical burden estimates of <em>Plasmodium falciparum</em> malaria for 2007, and to compare these estimates and their likely precision with those derived under existing surveillance-based approaches.</p><h3>Methods and Findings</h3><p>In seven of the 87 countries endemic for <em>P. falciparum</em> malaria, the health reporting infrastructure was deemed sufficiently rigorous for case reports to be used verbatim. In the remaining countries, the mapped extent of unstable and stable <em>P. falciparum</em> malaria transmission was first determined. Estimates of the plausible incidence range of clinical cases were then calculated within the spatial limits of unstable transmission. A modelled relationship between clinical incidence and prevalence was used, together with new maps of <em>P. falciparum</em> malaria endemicity, to estimate incidence in areas of stable transmission, and geostatistical joint simulation was used to quantify uncertainty in these estimates at national, regional, and global scales.</p><p>Combining these estimates for all areas of transmission risk resulted in 451 million (95% credible interval 349–552 million) clinical cases of <em>P. falciparum</em> malaria in 2007. Almost all of this burden of morbidity occurred in areas of stable transmission. More than half of all estimated <em>P. falciparum</em> clinical cases and associated uncertainty occurred in India, Nigeria, the Democratic Republic of the Congo (DRC), and Myanmar (Burma), where 1.405 billion people are at risk.</p><p>Recent surveillance-based methods of burden estimation were then reviewed and discrepancies in national estimates explored. When these cartographically derived national estimates were ranked according to their relative uncertainty and replaced by surveillance-based estimates in the least certain half, 98% of the global clinical burden continued to be estimated by cartographic techniques.</p><h3>Conclusions and Significance</h3><p>Cartographic approaches to burden estimation provide a globally consistent measure of malaria morbidity of known fidelity, and they represent the only plausible method in those malaria-endemic countries with nonfunctional national surveillance. Unacceptable uncertainty in the clinical burden of malaria in only four countries confounds our ability to evaluate needs and monitor progress toward international targets for malaria control at the global scale. National prevalence surveys in each nation would reduce this uncertainty profoundly. Opportunities for further reducing uncertainty in clinical burden estimates by hybridizing alternative burden estimation procedures are also evaluated.</p><h3></h3><p><em>Please see later in the article for the Editors' Summary</em></p></div>", "links"=>[], "tags"=>["estimating", "malaria", "2007"], "article_id"=>143050, "categories"=>["Medicine", "Mathematics", "Biological Sciences"], "users"=>["Simon I. Hay", "Emelda A. Okiro", "Peter W. Gething", "Anand P. Patil", "Andrew J. Tatem", "Carlos A. Guerra", "Robert W. Snow"], "doi"=>["https://dx.doi.org/10.1371/journal.pmed.1000290.s001", "https://dx.doi.org/10.1371/journal.pmed.1000290.s002"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Estimating_the_Global_Clinical_Burden_of_Plasmodium_falciparum_Malaria_in_2007/143050", "title"=>"Estimating the Global Clinical Burden of <em>Plasmodium falciparum</em> Malaria in 2007", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2010-06-15 00:50:50"}
  • {"files"=>["https://ndownloader.figshare.com/files/844963"], "description"=>"<p>Blue boxes describe input data, orange boxes models and experimental procedures, dashed green rods intermediate output, and solid green rods the final output. The seven countries with reliable national reporting were Belize, Iran, Kyrgyzstan, Panama, Saudi Arabia, South Africa, and Tajikistan. The areas of unstable and stable transmission are defined as having less or more than one case per 10,000 PA, respectively <a href=\"http://www.plosmedicine.org/article/info:doi/10.1371/journal.pmed.1000290#pmed.1000290-Guerra1\" target=\"_blank\">[40]</a>,<a href=\"http://www.plosmedicine.org/article/info:doi/10.1371/journal.pmed.1000290#pmed.1000290-Hay3\" target=\"_blank\">[41]</a>.</p>", "links"=>[], "tags"=>["diagram"], "article_id"=>515419, "categories"=>["Medicine", "Mathematics", "Computational Biology"], "users"=>["Simon I. Hay", "Emelda A. Okiro", "Peter W. Gething", "Anand P. Patil", "Andrew J. Tatem", "Carlos A. Guerra", "Robert W. Snow"], "doi"=>["https://dx.doi.org/10.1371/journal.pmed.1000290.g001"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Schematic_diagram_showing_the_procedure_for_burden_estimation_/515419", "title"=>"Schematic diagram showing the procedure for burden estimation.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2010-06-15 01:30:19"}
  • {"files"=>["https://ndownloader.figshare.com/files/845071"], "description"=>"<p>The land area was defined as no risk (light grey), unstable risk (medium grey areas, where <i>Pf</i>API <0.1‰ PA), and stable risk (where <i>Pf</i>API >0.1‰ PA) <a href=\"http://www.plosmedicine.org/article/info:doi/10.1371/journal.pmed.1000290#pmed.1000290-Guerra1\" target=\"_blank\">[40]</a> with endemicity (<i>Pf</i>PR in the 2- up to 10-year age group, <i>Pf</i>PR<sub>2–10</sub>) displayed as a continuum of yellow to red between 0% and 100%. The dashed lines separate the Americas, Africa+, and the CSE Asia region, respectively, from left to right. The seven countries with thick blue borders have very low <i>P. falciparum</i> burden and reliable national health information systems.</p>", "links"=>[], "tags"=>["limits", "endemicity"], "article_id"=>515527, "categories"=>["Medicine", "Mathematics", "Computational Biology"], "users"=>["Simon I. Hay", "Emelda A. Okiro", "Peter W. Gething", "Anand P. Patil", "Andrew J. Tatem", "Carlos A. Guerra", "Robert W. Snow"], "doi"=>["https://dx.doi.org/10.1371/journal.pmed.1000290.g002"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Global_limits_and_endemicity_of_P_falciparum_in_2007_/515527", "title"=>"Global limits and endemicity of <i>P. falciparum</i> in 2007.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2010-06-15 01:32:07"}
  • {"files"=>["https://ndownloader.figshare.com/files/845204"], "description"=>"<p>Human population density <a href=\"http://www.plosmedicine.org/article/info:doi/10.1371/journal.pmed.1000290#pmed.1000290-Balk1\" target=\"_blank\">[44]</a> in persons per km<sup>2</sup> is displayed on a logarithmic colour scale within the limits of <i>P. falciparum</i> transmission. No malaria risk is shown in light grey.</p>", "links"=>[], "tags"=>["infectious diseases/epidemiology and control of infectious diseases", "mathematics/statistics", "public health and epidemiology/infectious diseases"], "article_id"=>515658, "categories"=>["Medicine", "Mathematics", "Computational Biology"], "users"=>["Simon I. Hay", "Emelda A. Okiro", "Peter W. Gething", "Anand P. Patil", "Andrew J. Tatem", "Carlos A. Guerra", "Robert W. Snow"], "doi"=>["https://dx.doi.org/10.1371/journal.pmed.1000290.g003"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Global_human_population_density_in_2007_/515658", "title"=>"Global human population density in 2007.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2010-06-15 01:34:18"}
  • {"files"=>["https://ndownloader.figshare.com/files/845349"], "description"=>"<p>Bayesian geostatistical estimates (posterior means) of the number of all-age clinical cases per 5×5 km pixel displayed on a logarithmic colour scale between 0 and 10,000 cases, within the stable limits of <i>P. falciparum</i> transmission. Dark and light grey areas are as described in <a href=\"http://www.plosmedicine.org/article/info:doi/10.1371/journal.pmed.1000290#pmed-1000290-g002\" target=\"_blank\">Figure 2</a>.</p>", "links"=>[], "tags"=>["infectious diseases/epidemiology and control of infectious diseases", "mathematics/statistics", "public health and epidemiology/infectious diseases"], "article_id"=>515800, "categories"=>["Medicine", "Mathematics", "Computational Biology"], "users"=>["Simon I. Hay", "Emelda A. Okiro", "Peter W. Gething", "Anand P. Patil", "Andrew J. Tatem", "Carlos A. Guerra", "Robert W. Snow"], "doi"=>["https://dx.doi.org/10.1371/journal.pmed.1000290.g004"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Global_clinical_burden_of_P_falciparum_in_2007_/515800", "title"=>"Global clinical burden of <i>P. falciparum</i> in 2007.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2010-06-15 01:36:40"}
  • {"files"=>["https://ndownloader.figshare.com/files/845492"], "description"=>"<p>Bayesian geostatistical model-based prediction uncertainty (posterior standard deviations) on a logarithmic colour scale between 0 and 20,000 cases, within the stable limits of <i>P. falciparum</i> transmission. No model-based uncertainty metrics were produced for areas of unstable transmission. Dark and light grey areas are as described in <a href=\"http://www.plosmedicine.org/article/info:doi/10.1371/journal.pmed.1000290#pmed-1000290-g002\" target=\"_blank\">Figure 2</a>.</p>", "links"=>[], "tags"=>["infectious diseases/epidemiology and control of infectious diseases", "mathematics/statistics", "public health and epidemiology/infectious diseases"], "article_id"=>515946, "categories"=>["Medicine", "Mathematics", "Computational Biology"], "users"=>["Simon I. Hay", "Emelda A. Okiro", "Peter W. Gething", "Anand P. Patil", "Andrew J. Tatem", "Carlos A. Guerra", "Robert W. Snow"], "doi"=>["https://dx.doi.org/10.1371/journal.pmed.1000290.g005"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Uncertainty_in_the_global_clinical_burden_of_P_falciparum_in_2007_/515946", "title"=>"Uncertainty in the global clinical burden of <i>P. falciparum</i> in 2007.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2010-06-15 01:39:06"}
  • {"files"=>["https://ndownloader.figshare.com/files/845583"], "description"=>"<p>The relationship is plotted between malaria endemicity (<i>Pf</i>PR in the 2- up to 10-year age group, <i>Pf</i>PR<sub>2–10</sub>) and all-age incidence (clinical cases per thousand of the population PA) <a href=\"http://www.plosmedicine.org/article/info:doi/10.1371/journal.pmed.1000290#pmed.1000290-Patil1\" target=\"_blank\">[42]</a>. Please see reference <a href=\"http://www.plosmedicine.org/article/info:doi/10.1371/journal.pmed.1000290#pmed.1000290-Patil1\" target=\"_blank\">[42]</a> for a full description of the data, methods, and techniques used to define this relationship. The light grey, medium grey and dark grey regions define the 95%, 50%, and 25% credible intervals, respectively. The solid black line is the median and the data are shown as red dots.</p>", "links"=>[], "tags"=>["posterior", "prevalence-incidence"], "article_id"=>516036, "categories"=>["Medicine", "Mathematics", "Computational Biology"], "users"=>["Simon I. Hay", "Emelda A. Okiro", "Peter W. Gething", "Anand P. Patil", "Andrew J. Tatem", "Carlos A. Guerra", "Robert W. Snow"], "doi"=>["https://dx.doi.org/10.1371/journal.pmed.1000290.g006"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_posterior_distribution_of_the_prevalence_incidence_relationship_see_Methods_in_the_Africa_region_/516036", "title"=>"The posterior distribution of the prevalence-incidence relationship (, see Methods) in the Africa+ region.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2010-06-15 01:40:36"}
  • {"files"=>["https://ndownloader.figshare.com/files/845636"], "description"=>"<p>The techniques and colours used are identical to <a href=\"http://www.plosmedicine.org/article/info:doi/10.1371/journal.pmed.1000290#pmed-1000290-g006\" target=\"_blank\">Figure 6</a>.</p>", "links"=>[], "tags"=>["posterior", "prevalence-incidence", "cse", "asia"], "article_id"=>516090, "categories"=>["Medicine", "Mathematics", "Computational Biology"], "users"=>["Simon I. Hay", "Emelda A. Okiro", "Peter W. Gething", "Anand P. Patil", "Andrew J. Tatem", "Carlos A. Guerra", "Robert W. Snow"], "doi"=>["https://dx.doi.org/10.1371/journal.pmed.1000290.g007"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_posterior_distribution_of_the_prevalence_incidence_relationship_see_Methods_in_the_combined_CSE_Asia_region_and_the_Americas_/516090", "title"=>"The posterior distribution of the prevalence-incidence relationship (, see Methods) in the combined CSE Asia region and the Americas.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2010-06-15 01:41:30"}
  • {"files"=>["https://ndownloader.figshare.com/files/845694"], "description"=>"<p>Please see reference <a href=\"http://www.plosmedicine.org/article/info:doi/10.1371/journal.pmed.1000290#pmed.1000290-Patil1\" target=\"_blank\">[42]</a> for a full description of the data, methods, and techniques used to define this relationship. The light grey, medium grey, and dark grey regions define the 95%, 50%, and 25% credible intervals, respectively. The solid black line is the median and the data are shown as red dots. Note that the data points were collected using different surveillance intervals over different time periods, and therefore should not be expected to follow the distribution predicted by the model exactly. The observed incidences are included in the figure as a visual aid only.</p>", "links"=>[], "tags"=>["predictive", "incidence", "observed", "two-year"], "article_id"=>516146, "categories"=>["Medicine", "Mathematics", "Computational Biology"], "users"=>["Simon I. Hay", "Emelda A. Okiro", "Peter W. Gething", "Anand P. Patil", "Andrew J. Tatem", "Carlos A. Guerra", "Robert W. Snow"], "doi"=>["https://dx.doi.org/10.1371/journal.pmed.1000290.g008"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_predictive_distribution_of_the_incidence_that_would_actually_be_observed_by_weekly_surveillance_over_a_two_year_period_in_the_Africa_region_/516146", "title"=>"The predictive distribution of the incidence that would actually be observed by weekly surveillance over a two-year period in the Africa+ region.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2010-06-15 01:42:26"}
  • {"files"=>["https://ndownloader.figshare.com/files/845747"], "description"=>"<p>The techniques and colours used are identical to <a href=\"http://www.plosmedicine.org/article/info:doi/10.1371/journal.pmed.1000290#pmed-1000290-g008\" target=\"_blank\">Figure 8</a>.</p>", "links"=>[], "tags"=>["predictive", "incidence", "observed", "two-year", "cse", "asia"], "article_id"=>516198, "categories"=>["Medicine", "Mathematics", "Computational Biology"], "users"=>["Simon I. Hay", "Emelda A. Okiro", "Peter W. Gething", "Anand P. Patil", "Andrew J. Tatem", "Carlos A. Guerra", "Robert W. Snow"], "doi"=>["https://dx.doi.org/10.1371/journal.pmed.1000290.g009"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_predictive_distribution_of_the_incidence_that_would_actually_be_observed_by_weekly_surveillance_over_a_two_year_period_in_the_combined_CSE_Asia_region_and_the_Americas_/516198", "title"=>"The predictive distribution of the incidence that would actually be observed by weekly surveillance over a two-year period in the combined CSE Asia region and the Americas.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2010-06-15 01:43:18"}
  • {"files"=>["https://ndownloader.figshare.com/files/845785"], "description"=>"<p>The pie chart shows the fraction of the 451 million cases of total clinical burden in each of the World Health Organization regions (<a href=\"http://www.plosmedicine.org/article/info:doi/10.1371/journal.pmed.1000290#pmed.1000290.s002\" target=\"_blank\">Protocol S2</a>). In the pie the regions are ordered counterclockwise starting at the top, from highest to lowest burden. The plotted area representing the EURO region is too thin to be visible. The thumbnail map shows the country composition of the WHO regions for all 87 <i>P. falciparum</i> endemic countries.</p>", "links"=>[], "tags"=>["cases"], "article_id"=>516249, "categories"=>["Medicine", "Mathematics", "Computational Biology"], "users"=>["Simon I. Hay", "Emelda A. Okiro", "Peter W. Gething", "Anand P. Patil", "Andrew J. Tatem", "Carlos A. Guerra", "Robert W. Snow"], "doi"=>["https://dx.doi.org/10.1371/journal.pmed.1000290.g010"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Pie_chart_of_P_falciparum_clinical_cases_in_2007_/516249", "title"=>"Pie chart of <i>P. falciparum</i> clinical cases in 2007.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2010-06-15 01:44:09"}
  • {"files"=>["https://ndownloader.figshare.com/files/845859"], "description"=>"<p>The regional groupings are illustrated in <a href=\"http://www.plosmedicine.org/article/info:doi/10.1371/journal.pmed.1000290#pmed-1000290-g001\" target=\"_blank\">Figure 1</a>.</p>a<p>Case numbers from countries with reliable reporting and areas of unstable risk are presented directly whilst those from areas of stable risk are presented in millions of cases, rounded to the nearest 10,000, reflecting the larger numbers and lower precision associated with these model-based estimates.</p>b<p>Presumed to be all <i>P. falciparum</i>, although autochthonous case reports did not specify.</p>c<p>Excluding countries with reliable case data.</p>", "links"=>[], "tags"=>["attacks", "globally"], "article_id"=>516312, "categories"=>["Medicine", "Mathematics", "Computational Biology"], "users"=>["Simon I. Hay", "Emelda A. Okiro", "Peter W. Gething", "Anand P. Patil", "Andrew J. Tatem", "Carlos A. Guerra", "Robert W. Snow"], "doi"=>["https://dx.doi.org/10.1371/journal.pmed.1000290.t001"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Numbers_of_Plasmodium_falciparum_clinical_attacks_by_region_globally_in_2007_/516312", "title"=>"Numbers of <i>Plasmodium falciparum</i> clinical attacks by region globally in 2007.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2010-06-15 01:45:12"}

PMC Usage Stats | Further Information

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  • {"unique-ip"=>"22", "full-text"=>"24", "pdf"=>"6", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"12", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"2"}
  • {"unique-ip"=>"20", "full-text"=>"20", "pdf"=>"4", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"3"}
  • {"unique-ip"=>"35", "full-text"=>"47", "pdf"=>"4", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"1", "supp-data"=>"1", "cited-by"=>"1", "year"=>"2019", "month"=>"4"}
  • {"unique-ip"=>"27", "full-text"=>"32", "pdf"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"5"}
  • {"unique-ip"=>"17", "full-text"=>"23", "pdf"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"2", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"8"}
  • {"unique-ip"=>"16", "full-text"=>"13", "pdf"=>"4", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"9", "supp-data"=>"1", "cited-by"=>"0", "year"=>"2019", "month"=>"9"}

Relative Metric

{"start_date"=>"2010-01-01T00:00:00Z", "end_date"=>"2010-12-31T00:00:00Z", "subject_areas"=>[{"subject_area"=>"/Biology and life sciences/Parasitology", "average_usage"=>[277, 573, 714, 825, 931, 1032, 1126, 1207, 1278, 1351, 1422, 1495, 1558, 1626, 1696, 1747, 1811, 1888, 1954, 2005, 2095, 2149, 2214, 2277, 2318, 2389, 2436, 2493, 2567, 2666, 2727, 2784, 2871, 2936, 2984, 3058, 3116, 3178, 3223, 3295, 3360, 3423, 3504, 3579, 3645, 3716, 3757, 3826, 3873]}, {"subject_area"=>"/Medicine and health sciences/Public and occupational health", "average_usage"=>[315, 602, 752, 899, 1003, 1120, 1229, 1321, 1410, 1477, 1543, 1645, 1738, 1801, 1853, 1914, 1966, 2044, 2104, 2165, 2242, 2326, 2378, 2467, 2572, 2616, 2727, 2781, 2839, 2923, 2969, 3067, 3150, 3201, 3263, 3329, 3400, 3474, 3532, 3595, 3640, 3674, 3724, 3776, 3803, 3835, 3910, 4039, 4091]}, {"subject_area"=>"/People and places", "average_usage"=>[300, 565, 713, 834, 949, 1067, 1154, 1249, 1325, 1400, 1475, 1554, 1619, 1696, 1757, 1821, 1905, 1968, 2034, 2088, 2147, 2218, 2289, 2365, 2434, 2491, 2560, 2618, 2682, 2777, 2842, 2910, 2977, 3035, 3107, 3178, 3250, 3313, 3383, 3445, 3512, 3569, 3631, 3683, 3748, 3818, 3882, 3942]}]}
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