26 Dec 2012

Drivers and consequences of influenza antiviral resistant-strain emergence in a capacity-constrained pandemic response (Epidemics, abstract, edited)

[Source: Science Direct, full text: (LINK). Abstract, edited.]

Epidemics

Available online 25 December 2012

Drivers and consequences of influenza antiviral resistant-strain emergence in a capacity-constrained pandemic response

Mathew P. Dafilisa Robert Mossa, b, c, Jodie McVernona, James McCawa

a Vaccine &Immunisation Research Group, Murdoch Childrens’ Research Institute and Melbourne School of Population Health, The University of Melbourne, Parkville, Australia; b IR4M UMR8081 CNRS, Universite Paris-Sud, Orsay, France; c InstitutGustaveRoussy, Villejuif, France, Vaccine & Immunisation Research Group, Murdoch Children's Research Institute & Melbourne School of Population Health, he University of Melbourne, Parkville VIC 3010, Australia

Corresponding author. Tel.: +61 3; fax: +61 3 9348 1827.

Received 13 August 2012 - Revised 6 November 2012  - Accepted 17 December 2012  - Available online 25 December 2012

http://dx.doi.org/10.1016/j.epidem.2012.12.002

 

Abstract

Antiviral agents remain a key component of most pandemic influenza preparedness plans, but there is considerable uncertainty regarding their optimal use. In particular, concerns exist regarding the likelihood of wide-scale distribution to select for drug-resistant variants.We used a model that considers the influence of logistical constraints on diagnosis and drug delivery to consider achievable ‘reach’ of alternative antiviral intervention strategies targeted at cases of varying severity, with or without pre-exposure prophylaxis of contacts. To identify key drivers of epidemic mitigation and resistance emergence, we used Latin hypercube sampling to explore plausible ranges of parameters describing characteristics of wild type and resistant viruses, along with intervention efficacy, target coverage and distribution capacity.Within our model framework, ‘real world’ constraints substantially reduced achievable drug coverage below stated targets as the epidemic progressed. In consequence, predictions of both intervention impact and selection for resistance were more modest than earlier work that did not consider such limitations. Definitive containment of transmission was unlikely but, where observed, achieved through early liberal post-exposure prophylaxis of known contacts of treated cases. Predictors of resistant strain dominance were high intrinsic fitness relative to the wild type virus, and early emergence in the course of the epidemic into a largely susceptible population, even when drug use was restricted to severe case treatment. Our work demonstrates the importance of consideration of ‘real world’ constraints in scenario analysis modeling, and highlights the utility of models to guide surveillance activities in preparedness and response.

 

Highlights

► Optimal use of antivirals in a pandemic to avoid drug resistance remains uncertain; ► We considered this question in a model that incorporated drug delivery constraints; ► In consequence, achievable drug coverage and intervention impact were reduced; ► Selective pressure for resistance was also lower – only highly fit mutants dominated; ► Our work shows the importance of real world constraints in scenario analysis modeling.

 

Keywords: Pandemic influenza; Drug resistance; Mathematical model; Antivirals

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