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. 2017 Mar;17(3):339-347.
doi: 10.1016/S1473-3099(16)30465-0. Epub 2016 Dec 1.

Monitoring the Fitness of Antiviral-Resistant Influenza Strains During an Epidemic: A Mathematical Modelling Study

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Monitoring the Fitness of Antiviral-Resistant Influenza Strains During an Epidemic: A Mathematical Modelling Study

Kathy Leung et al. Lancet Infect Dis. .
Free PMC article

Abstract

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 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).

Conflict of interest statement

Conflicts of interest

We declare that we have no conflicts of interest.

Figures

Figure 1
Figure 1. Validating the accuracy and precision of AVR fitness estimates when the sensitivity and specificity of AVR testing are both 100%
One hundred epidemic scenarios are randomly generated and 100 stochastic realizations of the data streams are simulated for each scenario (see Methods). AVR fitness is inferred at the end of each simulated epidemic. A Frequency distribution of the relative error in the fitness estimates σ^ (i.e. 1E[σ^]/σ)) across all scenarios and realizations when the daily AVR testing capacity is 2, 5, 10, 20 and 80 samples. The smaller the relative error, the more accurate the estimates. B Frequency distribution of the coefficient of variation of σ^. The smaller the coefficient of variation, the more precise the estimates.
Figure 2
Figure 2. A simulated example to illustrate the timeliness of reliable AVR fitness estimates
The epidemic parameters are RS(0)=1.4 and Tg=2.8days. At time 0, 50% of each age group are susceptible and the epidemic is seeded with 10 AVS and 10 AVR infections. A–B Incidence of AVS and AVR infections in two fitness scenarios: σ = 1.05 or 0.95. C–D The daily number of reported cases. E–F The daily number of influenza-positive isolates that are AVS and AVR with a testing capacity of 10 samples per day. G–H Posterior distribution of the fitness estimate σ^ on each day. Circles and error bars indicate the posterior medians and the 95% credible intervals, respectively. I–J The posterior probability that AVR fitness is above 1.
Figure 3
Figure 3. Surveillance data for seasonal influenza A(H1N1) and fitness estimates for the oseltamivir-resistant strain during 2007–2008 in Canada, Luxembourg, United Kingdom, Germany, France, Japan, Netherlands, United States, Norway and Hong Kong
A The number of positive A(H1N1) virus isolates and the number of oseltamivir-sensitive and resistant A(H1N1) isolates over time in each population. B Fitness estimates for the oseltamivir-resistant A(H1N1) virus under three assumed generation time distributions. The pooled AVR fitness estimate (at the top) is obtained by assuming that AVR fitness was the same in all populations.
Figure 4
Figure 4. Retrospective real-time fitness estimate for the oseltamivir-resistant A(H1N1)pdm09 virus that circulated in Hokkaido, Japan during the 20132014 influenza season
A Data on influenza A(H1N1) activity and AVR surveillance. B Weekly fitness estimate using the same generation time distributions considered in Figure 3. C The posterior probability that AVR fitness was above 1.
Figure 5
Figure 5. Estimating AVR fitness and drug pressure on the AVS strain posed by large-scale antiviral prophylaxis
The epidemic parameters are the same as that in Figure 2 with intrinsic AVR fitness σ0 = 0.95. We assume that antiviral prophylaxis reduces susceptibility by 81% and the prophylaxis coverage is 10%, 15% and 20% so that the drug pressure μ is 0.08, 0.12 and 0.16, respectively. Large-scale antiviral intervention is suspended after the posterior probability of σ > 1 is greater than 0.9 for seven consecutive days. Cyan shade indicates the time period during which large-scale antiviral intervention is implemented. A The daily number of reported cases. B The daily number of influenza-positive isolates that are AVS and AVR with a testing capacity of 10 samples per day. C Posterior distribution of the AVR fitness estimate on each day. Circles and error bars indicate the posterior medians and the 95% credible intervals, respectively. D Posterior distribution of the estimates for drug pressure on the AVS strain at the baseline level (i.e. before large-scale antiviral interventions is suspended). E The posterior probability that AVR fitness is above 1.

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