ArticleThe Lancet. Infectious diseases2017
Monitoring the fitness of antiviral-resistant influenza strains during an epidemic: a mathematical modelling study.
Article in The Lancet. Infectious diseases, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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12 citing papers in PubMed.
- Epidemic graph diagrams as analytics for epidemic control in the data-rich era.Nature communications · 2023Article
- The mechanism shaping the logistic growth of mutation proportion in epidemics at population scale.Infectious Disease Modelling · 2023Article
- Predicting the Trajectory of Replacements of SARS-CoV-2 Variants Using Relative Reproduction Numbers.Viruses · 2022Article
- The non-pharmaceutical interventions may affect the advantage in transmission of mutated variants during epidemics: A conceptual model for COVID-19.Journal of theoretical biology · 2022Article
- The importance of the generation interval in investigating dynamics and control of new SARS-CoV-2 variants.Journal of the Royal Society, Interface · 2022Article
- Genomic epidemiology of SARS-CoV-2 under an elimination strategy in Hong Kong.Nature communications · 2022Article
- Estimating the transmission advantage of the D614G mutant strain of SARS-CoV-2, December 2019 to June 2020.Euro surveillance : bulletin Europeen sur les maladies transmissibles = European communicable disease bulletin · 2021Article
- Real-time quantification of the transmission advantage associated with a single mutation in pathogen genomes: a case study on the D614G substitution of SARS-CoV-2.BMC infectious diseases · 2021Article
- The co-circulating transmission dynamics of SARS-CoV-2 Alpha and Eta variants in Nigeria: A retrospective modeling study of COVID-19.Journal of global health · 2021Article
- Early transmissibility assessment of the N501Y mutant strains of SARS-CoV-2 in the United Kingdom, October to November 2020.Euro surveillance : bulletin Europeen sur les maladies transmissibles = European communicable disease bulletin · 2021Article
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Abstract
backgroundAntivirals (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.
methodsWe 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.
findingsWe 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.
interpretationWe developed a simple method that can be easily integrated into contemporary influenza surveillance systems to provide reliable estimates of AVR fitness in real time.
fundingResearch 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 the National 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).
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