Hedging against Antiviral Resistance during the Next Influenza Pandemic Using Small Stockpiles of an Alternative Chemotherapy
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{"title"=>"Hedging against antiviral resistance during the next influenza pandemic using small stockpiles of an alternative chemotherapy", "type"=>"journal", "authors"=>[{"first_name"=>"Joseph T.", "last_name"=>"Wu", "scopus_author_id"=>"7409256423"}, {"first_name"=>"Gabriel M.", "last_name"=>"Leung", "scopus_author_id"=>"7007159841"}, {"first_name"=>"Marc", "last_name"=>"Lipsitch", "scopus_author_id"=>"7006236353"}, {"first_name"=>"Ben S.", "last_name"=>"Cooper", "scopus_author_id"=>"7401623515"}, {"first_name"=>"Steven", "last_name"=>"Riley", "scopus_author_id"=>"7102619416"}], "year"=>2009, "source"=>"PLoS Medicine", "identifiers"=>{"issn"=>"15491277", "pui"=>"354671553", "doi"=>"10.1371/journal.pmed.1000085", "sgr"=>"66349121943", "isbn"=>"1549-1676 (Electronic)", "pmid"=>"19440354", "scopus"=>"2-s2.0-66349121943"}, "id"=>"e8d1c528-8b5f-3999-af7e-84fadd97c588", "abstract"=>"Background\\n\\n\\nThe effectiveness of single-drug antiviral interventions to reduce\\nmorbidity and mortality during the next influenza pandemic will be\\nsubstantially weakened if transmissible strains emerge which are\\nresistant to the stockpiled antiviral drugs. We developed a mathematical\\nmodel to test the hypothesis that a small stockpile of a secondary\\nantiviral drug could be used to mitigate the adverse consequences\\nof the emergence of resistant strains.\\n\\n\\nMethods and Findings\\n\\n\\nWe used a multistrain stochastic transmission model of influenza to\\nshow that the spread of antiviral resistance can be significantly\\nreduced by deploying a small stockpile (1% population coverage) of\\na secondary drug during the early phase of local epidemics. We considered\\ntwo strategies for the use of the secondary stockpile: early combination\\nchemotherapy (ECC; individuals are treated with both drugs in combination\\nwhile both are available); and sequential multidrug chemotherapy\\n(SMC; individuals are treated only with the secondary drug until\\nit is exhausted, then treated with the primary drug). We investigated\\nall potentially important regions of unknown parameter space and\\nfound that both ECC and SMC reduced the cumulative attack rate (AR)\\nand the resistant attack rate (RAR) unless the probability of emergence\\nof resistance to the primary drug pA was so low (less than 1 in 10,000)\\nthat resistance was unlikely to be a problem or so high (more than\\n1 in 20) that resistance emerged as soon as primary drug monotherapy\\nbegan. For example, when the basic reproductive number was 1.8 and\\n40% of symptomatic individuals were treated with antivirals, AR and\\nRAR were 67% and 38% under monotherapy if pA = 0.01. If the probability\\nof resistance emergence for the secondary drug was also 0.01, then\\nSMC reduced AR and RAR to 57% and 2%. The effectiveness of ECC was\\nsimilar if combination chemotherapy reduced the probabilities of\\nresistance emergence by at least ten times. We extended our model\\nusing travel data between 105 large cities to investigate the robustness\\nof these resistance-limiting strategies at a global scale. We found\\nthat as long as populations that were the main source of resistant\\nstrains employed these strategies (SMC or ECC), then those same strategies\\nwere also effective for populations far from the source even when\\nsome intermediate populations failed to control resistance. In essence,\\nthrough the existence of many wild-type epidemics, the interconnectedness\\nof the global network dampened the international spread of resistant\\nstrains.\\n\\n\\nConclusions\\n\\n\\nOur results indicate that the augmentation of existing stockpiles\\nof a single anti-influenza drug with smaller stockpiles of a second\\ndrug could be an effective and inexpensive epidemiological hedge\\nagainst antiviral resistance if either SMC or ECC were used. Choosing\\nbetween these strategies will require additional empirical studies.\\nSpecifically, the choice will depend on the safety of combination\\ntherapy and the synergistic effect of one antiviral in suppressing\\nthe emergence of resistance to the other antiviral when both are\\ntaken in combination.", "link"=>"http://www.mendeley.com/research/hedging-against-antiviral-resistance-during-next-influenza-pandemic-using-small-stockpiles-alternati", "reader_count"=>63, "reader_count_by_academic_status"=>{"Professor > Associate Professor"=>9, "Researcher"=>15, "Student > Doctoral Student"=>4, "Student > Ph. D. Student"=>13, "Student > Postgraduate"=>4, "Student > Master"=>4, "Other"=>2, "Student > Bachelor"=>5, "Lecturer > Senior Lecturer"=>2, "Professor"=>5}, "reader_count_by_user_role"=>{"Professor > Associate Professor"=>9, "Researcher"=>15, "Student > Doctoral Student"=>4, "Student > Ph. D. Student"=>13, "Student > Postgraduate"=>4, "Student > Master"=>4, "Other"=>2, "Student > Bachelor"=>5, "Lecturer > Senior Lecturer"=>2, "Professor"=>5}, "reader_count_by_subject_area"=>{"Unspecified"=>5, "Agricultural and Biological Sciences"=>17, "Business, Management and Accounting"=>1, "Chemical Engineering"=>1, "Computer Science"=>5, "Engineering"=>1, "Environmental Science"=>6, "Biochemistry, Genetics and Molecular Biology"=>1, "Mathematics"=>5, "Medicine and Dentistry"=>17, "Physics and Astronomy"=>2, "Social Sciences"=>1, "Immunology and Microbiology"=>1}, "reader_count_by_subdiscipline"=>{"Medicine and Dentistry"=>{"Medicine and Dentistry"=>17}, "Social Sciences"=>{"Social Sciences"=>1}, "Physics and Astronomy"=>{"Physics and Astronomy"=>2}, "Mathematics"=>{"Mathematics"=>5}, "Unspecified"=>{"Unspecified"=>5}, "Environmental Science"=>{"Environmental Science"=>6}, "Chemical Engineering"=>{"Chemical Engineering"=>1}, "Engineering"=>{"Engineering"=>1}, "Immunology and Microbiology"=>{"Immunology and Microbiology"=>1}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>17}, "Computer Science"=>{"Computer Science"=>5}, "Business, Management and Accounting"=>{"Business, Management and Accounting"=>1}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>1}}, "reader_count_by_country"=>{"Vietnam"=>1, "United States"=>3, "Japan"=>1, "Taiwan"=>1, "South Africa"=>1, "Mexico"=>1, "United Kingdom"=>1, "Australia"=>1}, "group_count"=>5}

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  • {"month"=>"10", "year"=>"2019", "pdf_views"=>"3", "xml_views"=>"1", "html_views"=>"15"}
  • {"month"=>"11", "year"=>"2019", "pdf_views"=>"8", "xml_views"=>"0", "html_views"=>"10"}
  • {"month"=>"12", "year"=>"2019", "pdf_views"=>"7", "xml_views"=>"0", "html_views"=>"11"}
  • {"month"=>"1", "year"=>"2020", "pdf_views"=>"9", "xml_views"=>"2", "html_views"=>"7"}
  • {"month"=>"2", "year"=>"2020", "pdf_views"=>"6", "xml_views"=>"0", "html_views"=>"6"}

Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/444352", "https://ndownloader.figshare.com/files/444487", "https://ndownloader.figshare.com/files/444533", "https://ndownloader.figshare.com/files/444582", "https://ndownloader.figshare.com/files/444609"], "description"=>"<div><h3>Background</h3><p>The effectiveness of single-drug antiviral interventions to reduce morbidity and mortality during the next influenza pandemic will be substantially weakened if transmissible strains emerge which are resistant to the stockpiled antiviral drugs. We developed a mathematical model to test the hypothesis that a small stockpile of a secondary antiviral drug could be used to mitigate the adverse consequences of the emergence of resistant strains.</p><h3>Methods and Findings</h3><p>We used a multistrain stochastic transmission model of influenza to show that the spread of antiviral resistance can be significantly reduced by deploying a small stockpile (1% population coverage) of a secondary drug during the early phase of local epidemics. We considered two strategies for the use of the secondary stockpile: early combination chemotherapy (ECC; individuals are treated with both drugs in combination while both are available); and sequential multidrug chemotherapy (SMC; individuals are treated only with the secondary drug until it is exhausted, then treated with the primary drug). We investigated all potentially important regions of unknown parameter space and found that both ECC and SMC reduced the cumulative attack rate (AR) and the resistant attack rate (RAR) unless the probability of emergence of resistance to the primary drug <em>p<sub>A</sub></em> was so low (less than 1 in 10,000) that resistance was unlikely to be a problem or so high (more than 1 in 20) that resistance emerged as soon as primary drug monotherapy began. For example, when the basic reproductive number was 1.8 and 40% of symptomatic individuals were treated with antivirals, AR and RAR were 67% and 38% under monotherapy if <em>p</em><sub>A</sub> = 0.01. If the probability of resistance emergence for the secondary drug was also 0.01, then SMC reduced AR and RAR to 57% and 2%. The effectiveness of ECC was similar if combination chemotherapy reduced the probabilities of resistance emergence by at least ten times. We extended our model using travel data between 105 large cities to investigate the robustness of these resistance-limiting strategies at a global scale. We found that as long as populations that were the main source of resistant strains employed these strategies (SMC or ECC), then those same strategies were also effective for populations far from the source even when some intermediate populations failed to control resistance. In essence, through the existence of many wild-type epidemics, the interconnectedness of the global network dampened the international spread of resistant strains.</p><h3>Conclusions</h3><p>Our results indicate that the augmentation of existing stockpiles of a single anti-influenza drug with smaller stockpiles of a second drug could be an effective and inexpensive epidemiological hedge against antiviral resistance if either SMC or ECC were used. Choosing between these strategies will require additional empirical studies. Specifically, the choice will depend on the safety of combination therapy and the synergistic effect of one antiviral in suppressing the emergence of resistance to the other antiviral when both are taken in combination.</p></div>", "links"=>[], "tags"=>["hedging", "antiviral", "influenza", "pandemic", "stockpiles", "chemotherapy"], "article_id"=>147590, "categories"=>["Biological Sciences", "Neuroscience"], "users"=>["Joseph T. Wu", "Gabriel M. Leung", "Marc Lipsitch", "Ben S. Cooper", "Steven Riley"], "doi"=>["https://dx.doi.org/10.1371/journal.pmed.1000085.s001", "https://dx.doi.org/10.1371/journal.pmed.1000085.s002", "https://dx.doi.org/10.1371/journal.pmed.1000085.s003", "https://dx.doi.org/10.1371/journal.pmed.1000085.s004", "https://dx.doi.org/10.1371/journal.pmed.1000085.s005"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Hedging_against_Antiviral_Resistance_during_the_Next_Influenza_Pandemic_Using_Small_Stockpiles_of_an_Alternative_Chemotherapy/147590", "title"=>"Hedging against Antiviral Resistance during the Next Influenza Pandemic Using Small Stockpiles of an Alternative Chemotherapy", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2009-05-19 02:06:30"}
  • {"files"=>["https://ndownloader.figshare.com/files/897048"], "description"=>"<p>Outcome variables were calculated using 10,000 simulations of 365 d in a population of 6.8 million individuals. See main text for baseline natural history parameters. (A) The baseline monotherapy scenario was associated with substantial stochasticity for a wide range of values of <i>p<sub>A</sub></i> (dashed lines, 95% prediction intervals; colored diamonds indicate scenarios for emergence rates used in [C]). (B) Two stochastic realizations of the single-population epidemic with <i>p<sub>A</sub></i> = 0.01 (brown shaded area corresponds to resistance incidence; blue shaded area corresponds to wild-type incidence; upper graph, resistance emerged early; lower graph, resistance did not emerge early). (C) AR and RAR as functions of the cumulative number of wild-type infections at the time resistance emerged (dashed lines, 95% prediction intervals; colors correspond to the value of <i>p<sub>A</sub></i> as per diamonds in [A]; in the absence of interventions AR = 73% and RAR = 0%; in the absence of resistance, AR = 56% and RAR = 0%). (D) Efficacy of ECC (antiviral synergies of <i>s</i> = 0 and <i>s</i> = 1) and SMC in reducing the attack rate (shaded areas, 95% prediction intervals; purple shading indicates overlap between red and blue shades). Note that the results for ECC1 correspond to the thin gray area that forms the lower border of the green area.</p>", "links"=>[], "tags"=>["emergence", "mitigation"], "article_id"=>567503, "categories"=>["Computational Biology", "Neuroscience"], "users"=>["Joseph T. Wu", "Gabriel M. Leung", "Marc Lipsitch", "Ben S. Cooper", "Steven Riley"], "doi"=>["https://dx.doi.org/10.1371/journal.pmed.1000085.g001"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Dynamics_of_resistance_emergence_and_mitigation_in_a_single_population_/567503", "title"=>"Dynamics of resistance emergence and mitigation in a single population.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2009-05-19 02:05:03"}
  • {"files"=>["https://ndownloader.figshare.com/files/897141"], "description"=>"<p>We used Latin-hypercube sampling to generate 1,000 combinations of the following parameters: basic reproductive number <i>R</i><sub>0</sub>, linear scale on interval (1,3); generation time <i>T<sub>G</sub></i>, linear, (2,4) d; proportion of infections in which the infector was not symptomatic <i>θ</i>, linear, (0,0.3); proportion of <i>θ</i> in which the infector was never symptomatic, linear, (0,1); probability of emergence of resistance to the primary antiviral <i>p<sub>A</sub></i>, log, (10<sup>−5</sup>, 1); probability of emergence of resistance to the secondary antiviral <i>p<sub>B</sub></i>, log, (10<sup>−5</sup>, 1); synergistic effects of combination therapy in reducing the rates of emergence of resistance <i>s</i>, linear, (0,1) (see the left column of Figure E in <a href=\"http://www.plosmedicine.org/article/info:doi/10.1371/journal.pmed.1000085#pmed.1000085.s001\" target=\"_blank\">Text S1</a> for 1−<i>s</i> on log scale, (10<sup>−7</sup>, 1)). A probability of treatment of <i>p<sub>T</sub></i> = 0.4 was used throughout (see the right column of Figure E in <a href=\"http://www.plosmedicine.org/article/info:doi/10.1371/journal.pmed.1000085#pmed.1000085.s001\" target=\"_blank\">Text S1</a> for <i>p<sub>T</sub></i> = 1). For each parameter combination, we estimated the mean attack rate (from 2,500 realizations) for: monotherapy with resistance, AR(MONO); monotherapy without resistance, AR(MONO/R), i.e., <i>p<sub>A</sub></i> = <i>p<sub>B</sub></i> = 0; early combination chemotherapy, AR(ECC); and sequential multidrug chemotherapy, AR(SMC). (A) <i>p<sub>A</sub></i> and <i>R</i><sub>0</sub> determined the usefulness of a hedge against the emergence of resistance. Main chart, frequency of parameter combinations versus the increase in monotherapy AR due to resistance, AR(MONO)−AR(MONO/R); inset charts, parameter subsets for AR(MONO)−AR(MONO/R)<1% (left) and >1% (right, Set H), points are colored as per the <i>x</i>-axis values in main chart. (B) If a hedge was useful (i.e., for those parameter combinations in Set H), ECC failed if <i>p<sub>A</sub></i> or <i>p<sub>B</sub></i> or both were large and synergy was not high. Main chart, frequency of parameter combinations versus the marginal benefit of ECC over monotherapy, AR(MONO)−AR(ECC); inset, distribution of parameter combinations in the <i>p<sub>B</sub></i>-<i>p<sub>A</sub></i> plane for which AR(MONO)−AR(ECC)<1%. Note: The colors here are not related to those in (A) (C) SMC performed better than ECC except when combination therapy results in very low probability of resistance emergence (e.g., very high synergy), yet drug B monotherapy has a high risk of emergence, rendering drug B monotherapy unsuitable and combination therapy highly effective. The proportion of scenarios for which ECC outperformed SMC (i.e. AR(ECC)−AR(SMC)<0%) was 22%. In a subset of such scenarios, ECC, but not SMC, had a high probability of achieving containment. Main chart, frequency of parameter combinations versus AR(ECC)−AR(SMC). Containment here was defined as an attack rate of <3%. Inset charts, distribution of parameter combinations for which AR(ECC)−AR(SMC)<0%. Inset left, <i>q<sub>c</sub></i>(SMC) and <i>q<sub>c</sub></i>(ECC) were the proportion of realizations with attack rate <3% under SMC and ECC; inset right, <i>q<sub>c</sub></i>(SMC)≪<i>q<sub>c</sub></i>(ECC) when <i>p<sub>B</sub></i> was high and (1−<i>s</i>)(<i>p<sub>A</sub></i>+<i>p<sub>B</sub></i>) (the probability of resistance emergence in a wild-type case under combination chemotherapy) was low.</p>", "links"=>[], "tags"=>["public health and epidemiology/epidemiology", "public health and epidemiology/infectious diseases"], "article_id"=>567599, "categories"=>["Computational Biology", "Neuroscience"], "users"=>["Joseph T. Wu", "Gabriel M. Leung", "Marc Lipsitch", "Ben S. Cooper", "Steven Riley"], "doi"=>["https://dx.doi.org/10.1371/journal.pmed.1000085.g002"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Sensitivity_analysis_/567599", "title"=>"Sensitivity analysis.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2009-05-19 02:06:39"}
  • {"files"=>["https://ndownloader.figshare.com/files/897231"], "description"=>"<p>Hong Kong (HK) is the source of infection in the network with 30 wild-type seeds on day 0. Twenty-eight cities implement large-scale antiviral intervention: Hong Kong, London, New York, Geneva, and 24 other cities (randomly chosen for each stochastic realization). Cities that implemented SMC had a drug B stockpile coverage of 1%. In this 4-by-4 chart panel, each row corresponds to a city (Hong Kong, London, New York or Geneva) and each column (A–D) corresponds to a different scenario. Each panel is the 2-D histogram (1,000 realizations) of attack rate (<i>y</i>-axes, 55%–75%) and resistant attack rate (<i>x</i>-axes, 0%–75%) for a given city and scenario. A bin size of 1% is used on both axes. The color for each bin indicates the frequency for that bin within that chart (see legend), i.e., same color in different panels does not indicate same frequency. The mean AR and RAR are shown at the bottom-right of each chart. The number <i>T<sub>s</sub></i> at the bottom of each chart is a measure of the time at which the epidemic had clearly taken off in each city: the mean time at which 1% of the population were infected. The numbers <i>A</i> and <i>B</i> at the upper-left indicate the mean amount of drugs A and B used (in terms of stockpile coverage, which means the number of treatment courses per capita). Four scenarios are shown: (A) HK and all 27 cities implemented monotherapy. (B) HK and all 27 cities implemented SMC. (C) HK, New York. Geneva and 11 other randomly chosen cities implemented SMC; London and 13 other randomly chosen cities implemented monotherapy. (D) Same as (C) except that HK did not implement SMC.</p>", "links"=>[], "tags"=>["105"], "article_id"=>567692, "categories"=>["Computational Biology", "Neuroscience"], "users"=>["Joseph T. Wu", "Gabriel M. Leung", "Marc Lipsitch", "Ben S. Cooper", "Steven Riley"], "doi"=>["https://dx.doi.org/10.1371/journal.pmed.1000085.g003"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_SMC_in_a_global_network_of_105_cities_/567692", "title"=>"SMC in a global network of 105 cities.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2009-05-19 02:08:12"}
  • {"files"=>["https://ndownloader.figshare.com/files/897324"], "description"=>"<p>Some of the data needed can be collected before the pandemic strikes, e.g., whether the side effects of combination chemotherapy are tolerable. Other data needed can be collected in real time after the pandemic virus has been observed, e.g., drug sensitivity of the pandemic virus and whether combination chemotherapy shows high synergy in reducing emergence of resistance for the pandemic strain.</p>", "links"=>[], "tags"=>["determining", "optimal", "antiviral", "hedging", "influenza"], "article_id"=>567775, "categories"=>["Computational Biology", "Neuroscience"], "users"=>["Joseph T. Wu", "Gabriel M. Leung", "Marc Lipsitch", "Ben S. Cooper", "Steven Riley"], "doi"=>["https://dx.doi.org/10.1371/journal.pmed.1000085.g004"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_A_decision_flow_chart_for_determining_the_optimal_use_of_a_secondary_antiviral_for_hedging_against_the_threat_of_antiviral_resistance_against_the_primary_drug_during_an_influenza_pandemic_/567775", "title"=>"A decision flow chart for determining the optimal use of a secondary antiviral for hedging against the threat of antiviral resistance against the primary drug during an influenza pandemic.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2009-05-19 02:09:35"}
  • {"files"=>["https://ndownloader.figshare.com/files/897425"], "description"=>"<p>Note: ARs and RARs are shown as means followed by the 95% prediction intervals. Three probabilities of resistance emergence for drug B are shown here: <i>p<sub>B</sub></i> = 0.01, 0.05, and 0.3. Under SMC and ECC1 (ECC with perfect synergy), AR and RAR are insensitive to the value of <i>p<sub>B</sub></i> in this range (see Table S2), hence only one set of outcomes is shown.</p>", "links"=>[], "tags"=>["ars", "rars", "smc", "ecc", "synergy"], "article_id"=>567877, "categories"=>["Computational Biology", "Neuroscience"], "users"=>["Joseph T. Wu", "Gabriel M. Leung", "Marc Lipsitch", "Ben S. Cooper", "Steven Riley"], "doi"=>["https://dx.doi.org/10.1371/journal.pmed.1000085.t001"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Example_ARs_and_RARs_under_SMC_and_ECC_with_synergy_of_0_ECC0_or_1_ECC1_/567877", "title"=>"Example ARs and RARs under SMC and ECC with synergy of 0 (ECC0) or 1 (ECC1).", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2009-05-19 02:11:17"}

PMC Usage Stats | Further Information

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  • {"unique-ip"=>"3", "full-text"=>"5", "pdf"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2018", "month"=>"12"}
  • {"unique-ip"=>"1", "full-text"=>"0", "pdf"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"2"}
  • {"unique-ip"=>"1", "full-text"=>"0", "pdf"=>"1", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"3"}
  • {"unique-ip"=>"2", "full-text"=>"2", "pdf"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"4"}
  • {"unique-ip"=>"10", "full-text"=>"11", "pdf"=>"2", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"5"}
  • {"unique-ip"=>"2", "full-text"=>"2", "pdf"=>"1", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"8"}
  • {"unique-ip"=>"3", "full-text"=>"2", "pdf"=>"2", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"9"}
  • {"unique-ip"=>"6", "full-text"=>"6", "pdf"=>"2", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"10"}
  • {"unique-ip"=>"6", "full-text"=>"2", "pdf"=>"4", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"1", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"12"}

Relative Metric

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