Monitoring the fitness of antiviral-resistant influenza strains during an epidemic: a mathematical modelling study. Issue 3 (March 2017) - Leung, Kathy; Lipsitch, Marc; Yuen, Kwok Yung; Wu, Joseph T
Summary: Background: Antivirals (eg, oseltamivir) are important for mitigating influenza epidemics. In 2007, an oseltamivir-resistant influenza seasonal A H1N1 strain emerged and spread to global fixation within 1 year. This event showed that antiviral-resistant (AVR) strains can be intrinsically more transmissible than their contemporaneous antiviral-sensitive (AVS) counterpart. Surveillance of AVR fitness is therefore essential. Our objective was to develop a simple method for estimating AVR fitness from surveillance data. Methods: We defined the fitness of AVR strains as their reproductive number relative to their co-circulating AVS counterparts. We developed a simple method for real-time estimation of AVR fitness from surveillance data. This method requires only information on generation time without other specific details regarding transmission dynamics. We first used simulations to validate this method by showing that it yields unbiased and robust fitness estimates in most epidemic scenarios. We then applied this method to two retrospective case studies and one hypothetical case study. Findings: We estimated that the oseltamivir-resistant A H1N1 strain that emerged in 2007 was 4% (95% credible interval [CrI] 3–5) more transmissible than its oseltamivir-sensitive predecessor and the oseltamivir-resistant pandemic A H1N1 strain that emerged and circulated in Japan during 2013–14 was 24% (95% CrI 17–30) less transmissible than its oseltamivir-sensitive counterpart. We show that in the event of large-scale antiviral interventions during a pandemic with co-circulation of AVS and AVR strains, our method can be used to inform optimal use of antivirals by monitoring intrinsic AVR fitness and drug pressure on the AVS strain. Interpretation: We developed a simple method that can be easily integrated into contemporary influenza surveillance systems to provide reliable estimates of AVR fitness in real time. Funding: Research Fund for the Control of Infectious Disease (09080792) and a commissioned grant from the Health and Medical Research Fund from the Government of the Hong Kong Special Administrative Region, Harvard Center for Communicable Disease Dynamics from theNational Institute of General Medical Sciences (grant number U54 GM088558 ), Area of Excellence Scheme of the Hong Kong University Grants Committee (grant number AoE/M-12/06 ).