Disease Interventions Can Interfere with One Another through Disease-Behaviour Interactions
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Mendeley | Further Information

{"title"=>"Disease Interventions Can Interfere with One Another through Disease-Behaviour Interactions", "type"=>"journal", "authors"=>[{"first_name"=>"Michael A.", "last_name"=>"Andrews", "scopus_author_id"=>"56727516400"}, {"first_name"=>"Chris T.", "last_name"=>"Bauch", "scopus_author_id"=>"35619116700"}], "year"=>2015, "source"=>"PLoS Computational Biology", "identifiers"=>{"sgr"=>"84953232093", "scopus"=>"2-s2.0-84953232093", "pui"=>"607497663", "pmid"=>"26047028", "issn"=>"15537358", "doi"=>"10.1371/journal.pcbi.1004291"}, "id"=>"9cafb9d2-2e8a-32a1-81f8-e54f3b3a43c0", "abstract"=>"Theoretical models of disease dynamics on networks can aid our understanding of how infectious diseases spread through a population. Models that incorporate decision-making mechanisms can furthermore capture how behaviour-driven aspects of transmission such as vaccination choices and the use of non-pharmaceutical interventions (NPIs) interact with disease dynamics. However, these two interventions are usually modelled separately. Here, we construct a simulation model of influenza transmission through a contact network, where individuals can choose whether to become vaccinated and/or practice NPIs. These decisions are based on previous experience with the disease, the current state of infection amongst one's contacts, and the personal and social impacts of the choices they make. We find that the interventions interfere with one another: because of negative feedback between intervention uptake and infection prevalence, it is difficult to simultaneously increase uptake of all interventions by changing utilities or perceived risks. However, on account of vaccine efficacy being higher than NPI efficacy, measures to expand NPI practice have only a small net impact on influenza incidence due to strongly mitigating feedback from vaccinating behaviour, whereas expanding vaccine uptake causes a significant net reduction in influenza incidence, despite the reduction of NPI practice in response. As a result, measures that support expansion of only vaccination (such as reducing vaccine cost), or measures that simultaneously support vaccination and NPIs (such as emphasizing harms of influenza infection, or satisfaction from preventing infection in others through both interventions) can significantly reduce influenza incidence, whereas measures that only support expansion of NPI practice (such as making hand sanitizers more available) have little net impact on influenza incidence. (However, measures that improve NPI efficacy may fare better.) We conclude that the impact of interference on programs relying on multiple interventions should be more carefully studied, for both influenza and other infectious diseases.", "link"=>"http://www.mendeley.com/research/disease-interventions-interfere-one-another-through-diseasebehaviour-interactions", "reader_count"=>20, "reader_count_by_academic_status"=>{"Researcher"=>7, "Student > Doctoral Student"=>1, "Student > Ph. D. Student"=>8, "Student > Postgraduate"=>2, "Student > Bachelor"=>1, "Lecturer"=>1}, "reader_count_by_user_role"=>{"Researcher"=>7, "Student > Doctoral Student"=>1, "Student > Ph. D. Student"=>8, "Student > Postgraduate"=>2, "Student > Bachelor"=>1, "Lecturer"=>1}, "reader_count_by_subject_area"=>{"Environmental Science"=>4, "Biochemistry, Genetics and Molecular Biology"=>2, "Mathematics"=>1, "Medicine and Dentistry"=>2, "Agricultural and Biological Sciences"=>5, "Veterinary Science and Veterinary Medicine"=>2, "Physics and Astronomy"=>1, "Psychology"=>1, "Computer Science"=>1, "Economics, Econometrics and Finance"=>1}, "reader_count_by_subdiscipline"=>{"Medicine and Dentistry"=>{"Medicine and Dentistry"=>2}, "Physics and Astronomy"=>{"Physics and Astronomy"=>1}, "Psychology"=>{"Psychology"=>1}, "Economics, Econometrics and Finance"=>{"Economics, Econometrics and Finance"=>1}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>5}, "Computer Science"=>{"Computer Science"=>1}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>2}, "Mathematics"=>{"Mathematics"=>1}, "Environmental Science"=>{"Environmental Science"=>4}, "Veterinary Science and Veterinary Medicine"=>{"Veterinary Science and Veterinary Medicine"=>2}}, "reader_count_by_country"=>{"United States"=>1, "Australia"=>1, "France"=>1}, "group_count"=>0}

Scopus | Further Information

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/2097607"], "description"=>"<p>Univariate analysis for <i>E</i><sub><i>V</i></sub> and <i>E</i><sub><i>I</i></sub> determining the amount that vaccination and NPIs interfere with each other in each scenario. (a), (c) Average values across 30 seasons for change in vaccination coverage (blue) and change in incidence (green) between hypothetical scenarios without NPI usage and the baseline scenarios. (b), (d) Average values across 30 seasons for change in NPI usage amongst susceptible (black) and infectious (red) individuals and change in incidence (green) between hypothetical scenarios without vaccine usage and the baseline scenarios. Confidence intervals represent two standard deviations of the mean of the 100 parameter sets across 30 simulated seasons.</p>", "links"=>[], "tags"=>["impact", "vaccine uptake causes", "influenza incidence", "support expansion", "Disease dynamics", "intervention", "vaccination", "infection", "NPI efficacy", "NPI practice"], "article_id"=>1438209, "categories"=>["Biological Sciences"], "users"=>["Michael A. Andrews", "Chris T. Bauch"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1004291.g007", "stats"=>{"downloads"=>0, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Interference_between_vaccination_and_NPIs_/1438209", "title"=>"Interference between vaccination and NPIs.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-06-05 03:16:21"}
  • {"files"=>["https://ndownloader.figshare.com/files/2097608"], "description"=>"<p>(a), (b) Univariate sensitivity analysis for NPI efficacy, <i>ϵ</i><sub><i>NPI</i></sub>. Data shows average values across 30 seasons for vaccination coverage (blue), incidence (green), NPI usage amongst susceptible individuals (black), and NPI usage amongst infectious individuals (red). (c), (d) Determining the amount that vaccination and NPIs interfere with each other for various NPI efficacies. Average values across 30 seasons for change in vaccination coverage (blue), change in incidence (green), and change in NPI usage amongst susceptible (black) and infectious (red) individuals are shown. Confidence intervals represent two standard deviations of the mean of the 100 parameter sets across 30 simulated seasons.</p>", "links"=>[], "tags"=>["impact", "vaccine uptake causes", "influenza incidence", "support expansion", "Disease dynamics", "intervention", "vaccination", "infection", "NPI efficacy", "NPI practice"], "article_id"=>1438210, "categories"=>["Biological Sciences"], "users"=>["Michael A. Andrews", "Chris T. Bauch"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1004291.g008", "stats"=>{"downloads"=>0, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_effects_of_NPI_efficacy_/1438210", "title"=>"The effects of NPI efficacy.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-06-05 03:16:21"}
  • {"files"=>["https://ndownloader.figshare.com/files/2097610"], "description"=>"<p>(a), (b) Univariate sensitivity analysis for vaccine efficacy, <i>ϵ</i><sub><i>V</i></sub>. Data shows average values across 30 seasons for vaccination coverage (blue), incidence (green), NPI usage amongst susceptible individuals (black), and NPI usage amongst infectious individuals (red). (c), (d) Determining the amount that vaccination and NPIs interfere with each other for various vaccine efficacies. Average values across 30 seasons for change in vaccination coverage (blue), change in incidence (green), and change in NPI usage amongst susceptible (black) and infectious (red) individuals are shown. Confidence intervals represent two standard deviations of the mean of the 100 parameter sets across 30 simulated seasons.</p>", "links"=>[], "tags"=>["impact", "vaccine uptake causes", "influenza incidence", "support expansion", "Disease dynamics", "intervention", "vaccination", "infection", "NPI efficacy", "NPI practice"], "article_id"=>1438212, "categories"=>["Biological Sciences"], "users"=>["Michael A. Andrews", "Chris T. Bauch"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1004291.g009", "stats"=>{"downloads"=>0, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_effects_of_vaccine_efficacy_/1438212", "title"=>"The effects of vaccine efficacy.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-06-05 03:16:21"}
  • {"files"=>["https://ndownloader.figshare.com/files/2097611"], "description"=>"<p>Model parameters with baseline values and sources.</p>", "links"=>[], "tags"=>["impact", "vaccine uptake causes", "influenza incidence", "support expansion", "Disease dynamics", "intervention", "vaccination", "infection", "NPI efficacy", "NPI practice"], "article_id"=>1438213, "categories"=>["Biological Sciences"], "users"=>["Michael A. Andrews", "Chris T. Bauch"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1004291.t001", "stats"=>{"downloads"=>1, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Model_parameters_with_baseline_values_and_sources_/1438213", "title"=>"Model parameters with baseline values and sources.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2015-06-05 03:16:21"}
  • {"files"=>["https://ndownloader.figshare.com/files/2097612"], "description"=>"<p>Sampling ranges for parameters used to obtain 100 baseline sets.</p>", "links"=>[], "tags"=>["impact", "vaccine uptake causes", "influenza incidence", "support expansion", "Disease dynamics", "intervention", "vaccination", "infection", "NPI efficacy", "NPI practice"], "article_id"=>1438214, "categories"=>["Biological Sciences"], "users"=>["Michael A. Andrews", "Chris T. Bauch"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1004291.t002", "stats"=>{"downloads"=>1, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Sampling_ranges_for_parameters_used_to_obtain_100_baseline_sets_/1438214", "title"=>"Sampling ranges for parameters used to obtain 100 baseline sets.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2015-06-05 03:16:21"}
  • {"files"=>["https://ndownloader.figshare.com/files/2097613"], "description"=>"<p>Acceptance ranges for simulation averages across 30 seasons.</p>", "links"=>[], "tags"=>["impact", "vaccine uptake causes", "influenza incidence", "support expansion", "Disease dynamics", "intervention", "vaccination", "infection", "NPI efficacy", "NPI practice"], "article_id"=>1438215, "categories"=>["Biological Sciences"], "users"=>["Michael A. Andrews", "Chris T. Bauch"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1004291.t003", "stats"=>{"downloads"=>1, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Acceptance_ranges_for_simulation_averages_across_30_seasons_/1438215", "title"=>"Acceptance ranges for simulation averages across 30 seasons.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2015-06-05 03:16:21"}
  • {"files"=>["https://ndownloader.figshare.com/files/2097616", "https://ndownloader.figshare.com/files/2097617", "https://ndownloader.figshare.com/files/2097618", "https://ndownloader.figshare.com/files/2097619"], "description"=>"<div><p>Theoretical models of disease dynamics on networks can aid our understanding of how infectious diseases spread through a population. Models that incorporate decision-making mechanisms can furthermore capture how behaviour-driven aspects of transmission such as vaccination choices and the use of non-pharmaceutical interventions (NPIs) interact with disease dynamics. However, these two interventions are usually modelled separately. Here, we construct a simulation model of influenza transmission through a contact network, where individuals can choose whether to become vaccinated and/or practice NPIs. These decisions are based on previous experience with the disease, the current state of infection amongst one's contacts, and the personal and social impacts of the choices they make. We find that the interventions interfere with one another: because of negative feedback between intervention uptake and infection prevalence, it is difficult to simultaneously increase uptake of all interventions by changing utilities or perceived risks. However, on account of vaccine efficacy being higher than NPI efficacy, measures to expand NPI practice have only a small net impact on influenza incidence due to strongly mitigating feedback from vaccinating behaviour, whereas expanding vaccine uptake causes a significant net reduction in influenza incidence, despite the reduction of NPI practice in response. As a result, measures that support expansion of only vaccination (such as reducing vaccine cost), or measures that simultaneously support vaccination and NPIs (such as emphasizing harms of influenza infection, or satisfaction from preventing infection in others through both interventions) can significantly reduce influenza incidence, whereas measures that only support expansion of NPI practice (such as making hand sanitizers more available) have little net impact on influenza incidence. (However, measures that improve NPI efficacy may fare better.) We conclude that the impact of interference on programs relying on multiple interventions should be more carefully studied, for both influenza and other infectious diseases.</p></div>", "links"=>[], "tags"=>["impact", "vaccine uptake causes", "influenza incidence", "support expansion", "Disease dynamics", "intervention", "vaccination", "infection", "NPI efficacy", "NPI practice"], "article_id"=>1438218, "categories"=>["Biological Sciences"], "users"=>["Michael A. Andrews", "Chris T. Bauch"], "doi"=>["https://dx.doi.org/10.1371/journal.pcbi.1004291.s001", "https://dx.doi.org/10.1371/journal.pcbi.1004291.s002", "https://dx.doi.org/10.1371/journal.pcbi.1004291.s003", "https://dx.doi.org/10.1371/journal.pcbi.1004291.s004"], "stats"=>{"downloads"=>1, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Disease_Interventions_Can_Interfere_with_One_Another_through_Disease_Behaviour_Interactions_/1438218", "title"=>"Disease Interventions Can Interfere with One Another through Disease-Behaviour Interactions", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2015-06-05 03:16:21"}
  • {"files"=>["https://ndownloader.figshare.com/files/2097591"], "description"=>"<p>(a) Diagram representing the vaccination choice problem. (b) Diagram representing the infectious NPI choice problem. (c) Diagram representing the susceptible NPI choice problem.</p>", "links"=>[], "tags"=>["impact", "vaccine uptake causes", "influenza incidence", "support expansion", "Disease dynamics", "intervention", "vaccination", "infection", "NPI efficacy", "NPI practice"], "article_id"=>1438195, "categories"=>["Biological Sciences"], "users"=>["Michael A. Andrews", "Chris T. Bauch"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1004291.g001", "stats"=>{"downloads"=>4, "page_views"=>10, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Vaccination_and_NPI_decisions_/1438195", "title"=>"Vaccination and NPI decisions.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-06-05 03:16:21"}
  • {"files"=>["https://ndownloader.figshare.com/files/2097593"], "description"=>"<p>Example time series of vaccination (blue) and incidence (green) over a season. When a vaccine becomes available in early October, uptake increases in anticipation of the upcoming influenza season. Confidence intervals represent two standard deviations of outputs for the 100 parameter sets (see Model Calibration section).</p>", "links"=>[], "tags"=>["impact", "vaccine uptake causes", "influenza incidence", "support expansion", "Disease dynamics", "intervention", "vaccination", "infection", "NPI efficacy", "NPI practice"], "article_id"=>1438197, "categories"=>["Biological Sciences"], "users"=>["Michael A. Andrews", "Chris T. Bauch"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1004291.g002", "stats"=>{"downloads"=>0, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Seasonal_time_series_/1438197", "title"=>"Seasonal time series.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-06-05 03:16:21"}
  • {"files"=>["https://ndownloader.figshare.com/files/2097595"], "description"=>"<p>Example of a baseline scenario of our model where vaccination becomes available in season 10, causing a change in the vaccine coverage each season (blue), the seasonal infection incidence (green), the probability that a susceptible individual practices NPIs given that they encounter one or more infectious individuals on a given day (black), and the probability that an infectious individual practices NPIs while ill (red). Confidence intervals represent two standard deviations of outputs for the 100 parameter sets.</p>", "links"=>[], "tags"=>["impact", "vaccine uptake causes", "influenza incidence", "support expansion", "Disease dynamics", "intervention", "vaccination", "infection", "NPI efficacy", "NPI practice"], "article_id"=>1438199, "categories"=>["Biological Sciences"], "users"=>["Michael A. Andrews", "Chris T. Bauch"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1004291.g003", "stats"=>{"downloads"=>0, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Impact_of_vaccine_introduction_/1438199", "title"=>"Impact of vaccine introduction.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-06-05 03:16:21"}
  • {"files"=>["https://ndownloader.figshare.com/files/2097599"], "description"=>"<p>Univariate sensitivity analysis for parameters <i>E</i><sub><i>S</i></sub>, <i>E</i><sub><i>H</i></sub>, and <i>E</i><sub><i>D</i></sub>. The numbers on the horizontal axes correspond to multiples of the baseline values for <i>E</i><sub><i>S</i></sub>, <i>E</i><sub><i>H</i></sub>, and <i>E</i><sub><i>D</i></sub>, hence 1.0 corresponds to the baseline value of the each parameter. (a), (c), (e) Average values across 30 seasons for vaccination coverage (blue) and incidence (green). (b), (d), (f) Average values across 30 seasons for NPI usage amongst susceptible individuals (black) and infectious individuals (red). Confidence intervals represent two standard deviations of the mean of the 100 parameter sets across 30 simulated seasons.</p>", "links"=>[], "tags"=>["impact", "vaccine uptake causes", "influenza incidence", "support expansion", "Disease dynamics", "intervention", "vaccination", "infection", "NPI efficacy", "NPI practice"], "article_id"=>1438203, "categories"=>["Biological Sciences"], "users"=>["Michael A. Andrews", "Chris T. Bauch"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1004291.g004", "stats"=>{"downloads"=>0, "page_views"=>1, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_effects_of_social_parameters_on_interventions_/1438203", "title"=>"The effects of social parameters on interventions.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-06-05 03:16:21"}
  • {"files"=>["https://ndownloader.figshare.com/files/2097601"], "description"=>"<p>Univariate sensitivity analysis for <i>E</i><sub><i>V</i></sub> and <i>E</i><sub><i>I</i></sub>. The numbers on the horizontal axes correspond to multiples of the baseline values for <i>E</i><sub><i>V</i></sub> and <i>E</i><sub><i>I</i></sub>, hence 1.0 corresponds to the baseline value of the each parameter. (a), (c) Average values across 30 seasons for vaccination coverage (blue) and incidence (green). (b), (d) Average values across 30 seasons for NPI usage amongst susceptible individuals (black) and infectious individuals (red). Confidence intervals represent two standard deviations of the mean of the 100 parameter sets across 30 simulated seasons.</p>", "links"=>[], "tags"=>["impact", "vaccine uptake causes", "influenza incidence", "support expansion", "Disease dynamics", "intervention", "vaccination", "infection", "NPI efficacy", "NPI practice"], "article_id"=>1438205, "categories"=>["Biological Sciences"], "users"=>["Michael A. Andrews", "Chris T. Bauch"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1004291.g005", "stats"=>{"downloads"=>1, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_effects_of_infection_and_vaccination_costs_on_interventions_/1438205", "title"=>"The effects of infection and vaccination costs on interventions.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-06-05 03:16:21"}
  • {"files"=>["https://ndownloader.figshare.com/files/2097605"], "description"=>"<p>Univariate analysis for social parameters <i>E</i><sub><i>S</i></sub>, <i>E</i><sub><i>H</i></sub>, and <i>E</i><sub><i>D</i></sub> determining the amount that vaccination and NPIs interfere with each other in each scenario. (a), (c), (e) Average values across 30 seasons for change in vaccination coverage (blue) and change in incidence (green) between hypothetical scenarios without NPI usage and the baseline scenarios. (b), (d), (f) Average values across 30 seasons for change in NPI usage amongst susceptible (black) and infectious (red) individuals and change in incidence (green) between hypothetical scenarios without vaccine usage and the baseline scenarios. Confidence intervals represent two standard deviations of the mean of the 100 parameter sets across 30 simulated seasons.</p>", "links"=>[], "tags"=>["impact", "vaccine uptake causes", "influenza incidence", "support expansion", "Disease dynamics", "intervention", "vaccination", "infection", "NPI efficacy", "NPI practice"], "article_id"=>1438207, "categories"=>["Biological Sciences"], "users"=>["Michael A. Andrews", "Chris T. Bauch"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1004291.g006", "stats"=>{"downloads"=>3, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Interference_between_vaccination_and_NPIs_/1438207", "title"=>"Interference between vaccination and NPIs.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-06-05 03:16:21"}

PMC Usage Stats | Further Information

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  • {"unique-ip"=>"15", "full-text"=>"15", "pdf"=>"2", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"2", "year"=>"2020", "month"=>"2"}
  • {"unique-ip"=>"22", "full-text"=>"21", "pdf"=>"3", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2020", "month"=>"3"}
  • {"unique-ip"=>"31", "full-text"=>"35", "pdf"=>"4", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2020", "month"=>"4"}
  • {"unique-ip"=>"18", "full-text"=>"18", "pdf"=>"2", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2020", "month"=>"5"}
  • {"unique-ip"=>"7", "full-text"=>"6", "pdf"=>"3", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2020", "month"=>"6"}
  • {"unique-ip"=>"3", "full-text"=>"1", "pdf"=>"2", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2020", "month"=>"7"}
  • {"unique-ip"=>"2", "full-text"=>"2", "pdf"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2020", "month"=>"8"}
  • {"unique-ip"=>"11", "full-text"=>"16", "pdf"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2020", "month"=>"9"}

Relative Metric

{"start_date"=>"2015-01-01T00:00:00Z", "end_date"=>"2015-12-31T00:00:00Z", "subject_areas"=>[]}
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