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Mendeley | Further Information

{"title"=>"Antiviral resistance and the control of pandemic influenza", "type"=>"journal", "authors"=>[{"first_name"=>"Marc", "last_name"=>"Lipsitch", "scopus_author_id"=>"7006236353"}, {"first_name"=>"Ted", "last_name"=>"Cohen", "scopus_author_id"=>"7202415780"}, {"first_name"=>"Megan", "last_name"=>"Murray", "scopus_author_id"=>"35479179200"}, {"first_name"=>"Bruce R.", "last_name"=>"Levin", "scopus_author_id"=>"35237661200"}], "year"=>2007, "source"=>"PLoS Medicine", "identifiers"=>{"sgr"=>"33846668526", "doi"=>"10.1371/journal.pmed.0040015", "issn"=>"15491277", "pui"=>"46191826", "isbn"=>"1549-1676 (Electronic)", "pmid"=>"17253900", "scopus"=>"2-s2.0-33846668526"}, "id"=>"12919784-7be6-39ae-bb2d-5fad50aa4afa", "abstract"=>"BACKGROUND: The response to the next influenza pandemic will likely include extensive use of antiviral drugs (mainly oseltamivir), combined with other transmission-reducing measures. Animal and in vitro studies suggest that some strains of influenza may become resistant to oseltamivir while maintaining infectiousness (fitness). Use of antiviral agents on the scale anticipated for the control of pandemic influenza will create an unprecedented selective pressure for the emergence and spread of these strains. Nonetheless, antiviral resistance has received little attention when evaluating these plans. METHODS AND FINDINGS: We designed and analyzed a deterministic compartmental model of the transmission of oseltamivir-sensitive and -resistant influenza infections during a pandemic. The model predicts that even if antiviral treatment or prophylaxis leads to the emergence of a transmissible resistant strain in as few as 1 in 50,000 treated persons and 1 in 500,000 prophylaxed persons, widespread use of antivirals may strongly promote the spread of resistant strains at the population level, leading to a prevalence of tens of percent by the end of a pandemic. On the other hand, even in circumstances in which a resistant strain spreads widely, the use of antivirals may significantly delay and/or reduce the total size of the pandemic. If resistant strains carry some fitness cost, then, despite widespread emergence of resistance, antivirals could slow pandemic spread by months or more, and buy time for vaccine development; this delay would be prolonged by nondrug control measures (e.g., social distancing) that reduce transmission, or use of a stockpiled suboptimal vaccine. Surprisingly, the model suggests that such nondrug control measures would increase the proportion of the epidemic caused by resistant strains. CONCLUSIONS: The benefits of antiviral drug use to control an influenza pandemic may be reduced, although not completely offset, by drug resistance in the virus. Therefore, the risk of resistance should be considered in pandemic planning and monitored closely during a pandemic.", "link"=>"http://www.mendeley.com/research/antiviral-resistance-control-pandemic-influenza", "reader_count"=>124, "reader_count_by_academic_status"=>{"Unspecified"=>2, "Professor > Associate Professor"=>16, "Librarian"=>1, "Student > Doctoral Student"=>5, "Researcher"=>34, "Student > Ph. D. Student"=>19, "Student > Postgraduate"=>8, "Student > Master"=>13, "Other"=>9, "Student > Bachelor"=>5, "Lecturer"=>1, "Lecturer > Senior Lecturer"=>2, "Professor"=>9}, "reader_count_by_user_role"=>{"Unspecified"=>2, "Professor > Associate Professor"=>16, "Librarian"=>1, "Student > Doctoral Student"=>5, "Researcher"=>34, "Student > Ph. D. 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Figshare

• {"files"=>["https://ndownloader.figshare.com/files/954036"], "description"=>"<div><p>More-effective nondrug interventions reduce the attack rate while increasing the role of the resistant strain; intermediate levels of antiviral use minimize the attack rate. For combinations of interventions leading to effective reproductive numbers near unity, the epidemic may take an unrealistically long time (three or more years). Prophy, prophylaxed.</p>\n <p>(A) Total attack rate.</p>\n <p>(B) Attack rate of the resistant strain.</p>\n <p>(C) Proportion of all cases attributable to the resistant strain.</p></div>", "links"=>[], "tags"=>["varying", "reproductive", "numbers", "antiviral", "resistant", "cases", "attributable"], "article_id"=>624329, "categories"=>["Infectious Diseases", "Medicine", "Virology", "Biotechnology"], "users"=>["Marc Lipsitch", "Ted Cohen", "Megan Murray", "Bruce R Levin"], "doi"=>"https://dx.doi.org/10.1371/journal.pmed.0040015.g003", "stats"=>{"downloads"=>2, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Effect_of_Varying_Effective_Reproductive_Numbers_R_E_and_of_Antiviral_Use_on_Total_Attack_Rate_Attack_Rate_of_the_Resistant_Strain_and_Proportion_of_All_Cases_Attributable_to_the_Resistant_Strain_/624329", "title"=>"Effect of Varying Effective Reproductive Numbers <i>(R_E)</i> and of Antiviral Use on Total Attack Rate, Attack Rate of the Resistant Strain, and Proportion of All Cases Attributable to the Resistant Strain", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-21 12:56:09"}
• {"files"=>["https://ndownloader.figshare.com/files/953915"], "description"=>"<div><p>(A and B) Either treatment of 30% of infected hosts (A) or prophylaxis of 30% of contacts (B) reduces transmission of a sensitive strain (green) and leads to selection of a resistant strain (red), with somewhat more resistance due to prophylaxis than treatment, although the risk that resistance will emerge is assumed to be 0.2% for treatment and 0.02% for prophylaxis. Proportions of the entire population infected with either strain are read on the left <i>y</i>-axis. Black curves (right <i>y</i>-axis) indicate the proportion of the population uninfected.</p>\n <p>(C and D) Treatment of 30% and prophylaxis of 30% (C) selects most strongly for resistance, because treatment generates resistant strains early on, and these strains are amplified by prophylaxis. (D) shows the impact of neither treatment nor prophylaxis.</p>\n <p>(E–G) These contrasting effects are apparent in following the cumulative proportion of resistant infections (black lines), which rises earlier with treatment (E), but persists and reaches higher levels with prophylaxis (F). The orange curves show the proportion of all new resistant infections attributable to de novo resistance as opposed to transmission of a resistant strain; the contribution of de-novo resistance is highest early, but rapidly declines. Graph (G) shows the impact of both treatment and prophylaxis.</p></div>", "links"=>[], "tags"=>["resistant"], "article_id"=>624215, "categories"=>["Infectious Diseases", "Medicine", "Virology", "Biotechnology"], "users"=>["Marc Lipsitch", "Ted Cohen", "Megan Murray", "Bruce R Levin"], "doi"=>"https://dx.doi.org/10.1371/journal.pmed.0040015.g002", "stats"=>{"downloads"=>0, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Impact_of_Treatment_Prophylaxis_Both_or_Neither_on_the_Dynamics_of_Resistant_and_Sensitive_Infections_/624215", "title"=>"Impact of Treatment, Prophylaxis, Both, or Neither on the Dynamics of Resistant and Sensitive Infections", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-21 12:55:26"}
• {"files"=>["https://ndownloader.figshare.com/files/954113"], "description"=>"<div><p>Mean incidence time (“Inc. Time”) is the mean time of infection of all infected persons over the course of the epidemic. Rows represent different frequencies with which resistance emerges during prophylaxis (“Prophy”) (<i>c_p</i> = 0, 2 × 10⁻⁶, and 2 × 10⁻⁴) and treatment (<i>c_t</i> = 0, 2 × 10⁻⁵, and 2 × 10⁻³); columns represent different fitness costs of resistance (0, 20%, and 40%). Since the resistant strain is not present at the start of the simulations, it does not appear when <i>c_p</i> = <i>c_t</i> = 0, hence its fitness cost is irrelevant.</p>\n <p>Graph (A) represents the case with no resistance. Graphs (B–D) represent the case when <i>c_p</i> = 2 × 10⁻⁶ and <i>c_t</i> = 2 × 10⁻⁵ at a fitness cost of 0, 0.2, and 0.4, respectively. Graphs (E–G) represent the case when <i>c_p</i> = 2 × 10⁻⁴ and <i>c_t</i> = 2 × 10⁻³ at a fitness cost of 0, 0.2, and 0.4, respectively.</p></div>", "links"=>[], "tags"=>["varying", "reproductive", "numbers", "antiviral", "incidence"], "article_id"=>624411, "categories"=>["Infectious Diseases", "Medicine", "Virology", "Biotechnology"], "users"=>["Marc Lipsitch", "Ted Cohen", "Megan Murray", "Bruce R Levin"], "doi"=>"https://dx.doi.org/10.1371/journal.pmed.0040015.g004", "stats"=>{"downloads"=>1, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Effect_of_Varying_Effective_Reproductive_Numbers_R_E_and_of_Antiviral_Use_on_Total_Attack_Rate_Solid_Curves_and_Mean_Incidence_Time_Dashed_Curves_/624411", "title"=>"Effect of Varying Effective Reproductive Numbers <i>(R_E)</i> and of Antiviral Use on Total Attack Rate (Solid Curves) and Mean Incidence Time (Dashed Curves)", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-21 12:56:40"}
• {"files"=>["https://ndownloader.figshare.com/files/953828"], "description"=>"<p>Naive <i>(X)</i> individuals become infected with sensitive <i>(S)</i> or resistant <i>(R)</i> strain at rates proportional to prevalence of each strain, <i>Y_S</i> or <i>Y_R.</i> Individuals infected with the sensitive strain may be either treated <i>(T)</i> or untreated <i>(U)</i>. Individuals are removed <i>(Z)</i> by death or recovery. See <a href=\"http://www.plosmedicine.org/article/info:doi/10.1371/journal.pmed.0040015#s2\" target=\"_blank\">Methods</a> for corresponding equations. This model was used for analytical calculations, whereas an age-structured version (<a href=\"http://www.plosmedicine.org/article/info:doi/10.1371/journal.pmed.0040015#s2\" target=\"_blank\">Methods</a>) was used with parameters shown in <a href=\"http://www.plosmedicine.org/article/info:doi/10.1371/journal.pmed.0040015#pmed-0040015-t001\" target=\"_blank\">Table 1</a> for numerical solutions.</p>", "links"=>[], "tags"=>["Infectious diseases", "Non-clinical medicine", "public health and epidemiology", "Virology"], "article_id"=>624134, "categories"=>["Infectious Diseases", "Medicine", "Virology", "Biotechnology"], "users"=>["Marc Lipsitch", "Ted Cohen", "Megan Murray", "Bruce R Levin"], "doi"=>"https://dx.doi.org/10.1371/journal.pmed.0040015.g001", "stats"=>{"downloads"=>3, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Model_Structure_/624134", "title"=>"Model Structure", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-21 12:54:47"}
• {"files"=>["https://ndownloader.figshare.com/files/469121", "https://ndownloader.figshare.com/files/469253"], "description"=>"<div><h3>Background</h3><p>The response to the next influenza pandemic will likely include extensive use of antiviral drugs (mainly oseltamivir), combined with other transmission-reducing measures. Animal and in vitro studies suggest that some strains of influenza may become resistant to oseltamivir while maintaining infectiousness (fitness). Use of antiviral agents on the scale anticipated for the control of pandemic influenza will create an unprecedented selective pressure for the emergence and spread of these strains. Nonetheless, antiviral resistance has received little attention when evaluating these plans.</p> <h3>Methods and Findings</h3><p>We designed and analyzed a deterministic compartmental model of the transmission of oseltamivir-sensitive and -resistant influenza infections during a pandemic. The model predicts that even if antiviral treatment or prophylaxis leads to the emergence of a transmissible resistant strain in as few as 1 in 50,000 treated persons and 1 in 500,000 prophylaxed persons, widespread use of antivirals may strongly promote the spread of resistant strains at the population level, leading to a prevalence of tens of percent by the end of a pandemic. On the other hand, even in circumstances in which a resistant strain spreads widely, the use of antivirals may significantly delay and/or reduce the total size of the pandemic. If resistant strains carry some fitness cost, then, despite widespread emergence of resistance, antivirals could slow pandemic spread by months or more, and buy time for vaccine development; this delay would be prolonged by nondrug control measures (e.g., social distancing) that reduce transmission, or use of a stockpiled suboptimal vaccine. Surprisingly, the model suggests that such nondrug control measures would increase the proportion of the epidemic caused by resistant strains.</p> <h3>Conclusions</h3><p>The benefits of antiviral drug use to control an influenza pandemic may be reduced, although not completely offset, by drug resistance in the virus. Therefore, the risk of resistance should be considered in pandemic planning and monitored closely during a pandemic.</p> </div>", "links"=>[], "tags"=>["antiviral", "pandemic", "influenza"], "article_id"=>152495, "categories"=>["Biotechnology", "Cancer", "Medicine"], "users"=>["Marc Lipsitch", "Ted Cohen", "Megan Murray", "Bruce R Levin"], "doi"=>["https://dx.doi.org/10.1371/journal.pmed.0040015.sd001", "https://dx.doi.org/10.1371/journal.pmed.0040015.st001"], "stats"=>{"downloads"=>27, "page_views"=>27, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Antiviral_Resistance_and_the_Control_of_Pandemic_Influenza/152495", "title"=>"Antiviral Resistance and the Control of Pandemic Influenza", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2007-01-23 00:41:35"}

PMC Usage Stats | Further Information

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Relative Metric

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F1000Prime