Antiviral resistance during pandemic influenza: implications for stockpiling and drug use
The anticipated extent of antiviral use during an influenza pandemic can have adverse consequences for the development of drug resistance and rationing of limited stockpiles. The strategic use of drugs is therefore a major public health concern in planning for effective pandemic responses.
We employed a mathematical model that includes both sensitive and resistant strains of a virus with pandemic potential, and applies antiviral drugs for treatment of clinical infections. Using estimated parameters in the published literature, the model was simulated for various sizes of stockpiles to evaluate the outcome of different antiviral strategies.
We demonstrated that the emergence of highly transmissible resistant strains has no significant impact on the use of available stockpiles if treatment is maintained at low levels or the reproduction number of the sensitive strain is sufficiently high. However, moderate to high treatment levels can result in a more rapid depletion of stockpiles, leading to run-out, by promoting wide-spread drug resistance. We applied an antiviral strategy that delays the onset of aggressive treatment for a certain amount of time after the onset of the outbreak. Our results show that if high treatment levels are enforced too early during the outbreak, a second wave of infections can potentially occur with a substantially larger magnitude. However, a timely implementation of wide-scale treatment can prevent resistance spread in the population, and minimize the final size of the pandemic.
Our results reveal that conservative treatment levels during the early stages of the outbreak, followed by a timely increase in the scale of drug-use, will offer an effective strategy to manage drug resistance in the population and avoid run-out. For a 1918-like strain, the findings suggest that pandemic plans should consider stockpiling antiviral drugs to cover at least 20% of the population.
KeywordsInfluenza Treatment Level Antiviral Drug Reproduction Number Sensitive Strain
Future outbreaks of emerging infectious pathogens are virtually certain to occur, and pandemic influenza is one that seemingly poses a significant threat to human populations. While the characteristics of the next pandemic strain remain unknown, the virulence of the currently circulating avian influenza A virus H5N1 is of great concern [1, 2]. Given uncertainties regarding the timing, origin, and virulence of future pandemic strains, as well as the possibility of an unprecedented spread of the deadly H5N1 virus in humans, planning strategies for an effective response has become the top priority of global public health efforts [3, 4, 5, 6, 7].
Pandemic preparedness measures encompass disease surveillance, case identification and treatment, prevention of community-wide spread of disease, maintenance of essential services, and research and evaluation . Specific approaches to influenza infection control include the use of pharmaceutical products (such as vaccines and antiviral drugs), and non-pharmaceutical measures (such as personal protective equipment and social distancing). Although vaccination remains the most effective strategy for reducing the risk of infection and subsequent complications , an effective vaccine may not be available for several months following the declaration of a pandemic. This highlights the importance of antiviral drugs as the primary tool for prevention and treatment of infection , especially in light of the insufficient impact that non-pharmaceutical measures may have on disease mitigation .
Considering that there may be insufficient supply of drugs, limited production capacity, and a surge in demand for antiviral therapy with the progression of a pandemic, the use of antivirals for treatment will likely take precedence over their preventive (prophylactic) use. The primary goal in treatment of influenza infection is to relieve symptoms and limit the severity of infection by inhibiting virus replication. This will in turn contribute to the containment of disease spread in the population as a result of reduced viral transmission. It is therefore imperative to formulate antiviral policies that are most likely to optimize the health of the greatest number of individuals in the face of an influenza pandemic.
Although antiviral treatment appears to be crucial in any pandemic response, the emergence of drug-resistance will impose significant threats to the effectiveness of drugs [12, 13, 14, 15, 16, 17], and possibly wasteful depletion of stockpiles without achieving the desired mitigation impact. Previous modelling studies suggest that, in the ideal situation where adequate supply of antiviral drugs is secured, conservative treatment levels at the early stages of the outbreak, followed by a timely increase in the scale of drug-use, would preserve the potential for minimizing the final size of a pandemic while preventing large outbreaks of drug-resistant infections [18, 19]. In this study, we further investigate the merits of application of antiviral treatment under the scenario in which the supply of drugs may be limited and run-out is possible. By employing a mathematical model, we show the relationship between the drug stockpile and treatment level (the fraction of clinical infections being treated) for a range of reproduction numbers estimated for the past three pandemics . We discuss the influence of emergence of antiviral resistance for the use of drugs and demonstrate possible scenarios of disease outbreaks, including a second wave of infections in a single outbreak. Our findings extend a previous work , in which emergence and transmission of resistance are neglected. We also evaluate the impact of an adaptive antiviral strategy on disease mitigation [18, 19], where aggressive treatment of clinical infections is delayed for a certain amount of time after the onset of the outbreak. Finally, we discuss model predictions and their implications for stockpiling and drug use in pandemic planning.
Model parameters and estimations
mean infectious period of asymptomatic infection
mean infectious period of untreated symptomatic infection
mean infectious period of treated symptomatic infection
death rate of untreated symptomatic infection
death rate of treated symptomatic infection
death rate of resistant infection (low fitness)
death rate of resistant infection (high fitness)
relative transmissibility of asymptomatic infection
relative transmissibility of treated symptomatic infection
relative transmissibility of resistant strain (high fitness)
probability of developing clinical symptoms
rate of emergence of de novo resistance
rate of conversion between resistant mutants
In single-strain epidemic models, these reproduction numbers can be used to determine the final size of the outbreak . However, the final size relation for multi-strain models, such as the one considered in this study, may be difficult to obtain. If only resistant strains with LTF are present, then the final size of the pandemic can be expressed in terms of the control reproduction number of the sensitive strain, [see additional file 1].
We considered various scenarios of disease outbreak in the presence of antiviral treatment, when the reproduction number of the sensitive strain (R0), the treatment level of clinical cases, and the size of drug stockpile vary in their respective ranges. Due to the unknown transmissibility of the pandemic strain, we used reproduction numbers estimated for pandemics of the last century, ranging from 1.5 to 2.5 [20, 29]. We simulated the model by introducing a single case infected with the sensitive strain into a susceptible population of size S0. The rate of de novo resistance (α) that generates mutants with LTF is reported to range from 0.018 to 0.072 day-1 [19, 25], and we assumed a baseline value of α = 0.0018 day-1, which results in the emergence of drug-resistance in approximately 6.8% of treated patients in our model. The rate at which treated individuals (hosting resistant viruses with LTF) develop resistance with HTF (90% relative to that of the sensitive strain) is assumed to be 5-fold smaller, taking the baseline value of γ = 0.0036 day-1 . These rates contribute to an overall 0.1% incidence of resistance with HTF (without considering direct transmission) in our model. Other parameter values used in simulations are given in Table 1.
Adaptive treatment strategy
Since emergence of resistant mutants with HTF can potentially result in a rapid depletion of drug stockpiles, management of drug resistance in the population is crucial for the success of any antiviral strategy, in particular when supplies are limited. A recent evaluation of antiviral strategies suggests that, if a pandemic virus is not contained at the source, delaying aggressive treatment can substantially reduce the likelihood of emergence and population-wide spread of resistance . Not only can this adaptive strategy prevent large outbreaks of drug-resistant infections, but it can also minimize the overall pandemic burden if followed by a timely increase in the scale of drug-use.
As nations prepare to confront the next influenza pandemic, disease mitigation strategies are being carefully gauged to project the effectiveness of preventive, therapeutic, and social distancing measures. Published modelling studies suggest that the pandemic can be contained at the source if early treatment of diagnosed cases is combined with targeted blanket prophylaxis and social distancing measures [3, 7]. Significant assumptions are embedded in the core of such models, most of which are unlikely to be fulfilled in a real world environment, and therefore containment failure should be anticipated when devising effective preparedness countermeasures.
While application of antiviral drugs has been rationalized as the first-line defence against a pandemic, public health authorities are concerned with the strategic use of drug supply in order to maximize both short-term population-wide benefits and long-term epidemiological effects of antiviral therapy. In this study, we developed a mathematical model to assess the impact of various antiviral strategies on curtailing disease, by considering the interplay between three confounding factors: (i) the treatment level of clinical infections; (ii) the emergence and spread of antiviral resistance; and (iii) the size of the stockpile. In the absence of resistance, we have shown that an intensive treatment early on during the outbreak minimizes the overall disease burden regardless of the size of the stockpile. In this case, if R0 is not too high, containment of the disease may be achieved with sufficiently high level of treatment. This strategy is particularly beneficial when stockpile is limited, since it significantly reduces the spread of disease in the population, and therefore requires fewer courses of antiviral drugs.
Our results suggest a significantly different strategy for antiviral use if resistance were to develop with a transmission fitness comparable to that of the sensitive strain. As indicated by simulations (Figures 3, 4), emergence of drug-resistance has no considerable impact on the use of drugs, and therefore on the depletion of stockpiles, if treatment is maintained at low levels throughout the outbreak, or R0 is sufficiently high. However, for moderate to high treatment levels, the spread of resistance leads to a more rapid consumption of available stockpiles, and run-out is likely to occur even for low values of R0 (Figure 4b–c). For comparison purposes, we applied an adaptive antiviral strategy that has been thoroughly evaluated in previous work , and observed that delaying aggressive treatment can potentially eliminate the possibility of wide-spread drug resistance, and also minimize the final size of the outbreak. This strategy allows for the initial prevalence of the drug-sensitive strain under low pressure of drugs to deplete a sizable portion of susceptible hosts , and therefore prevents the outgrowth of resistance when selection occurs. However, a timely increase in the scale of drug use plays a critical role in the success of this adaptive treatment policy. We demonstrated that, if high treatment levels are implemented too early during the outbreak, a second peak of infections can occur due to run-out with limited stockpile, or as a result of population-wide spread of resistance (Figure 6a–c).
A comparative evaluation of antiviral use indicates that the overall healthcare benefits of an adaptive strategy may be much higher than a constant treatment policy. Assuming R0 = 2, the adaptive strategy with an initial 0% treatment level (increased to 80% at time t* = 35) requires a stockpile of size 18.5% (relative to S0), and results in 24% reduction in the total number of clinical infections compared with the constant antiviral treatment at the optimal level 41%, which requires a stockpile of size 17.5%. We observed a similar outcome of the adaptive strategy with 25% initial treatment level (increased to 80% at time t* = 50) and 19% stockpile, leading to 22% reduction in the total number of clinical infections compared with the constant treatment at the optimal level. While the adaptive treatment policy places a demand for slightly larger stockpiles, its increased financial burden must be weighted against the inevitably far greater cost savings that would be obtained through substantial reduction in morbidity and therefore hospitalizations during the pandemic.
For a novel influenza strain with the reproduction number similar to that of the 1918 pandemic [20, 29], the findings suggest that in order to reduce the risk of a subsequent wave of infections within an adaptive treatment strategy, pandemic plans should consider stockpiling antiviral drugs with a minimum capacity of 20% (relative to the population size). Given that drugs may also be used for pre-exposure prophylaxis of front-line healthcare workers and emergency responders, and considering that prophylaxis makes a greater contribution to the spread of resistance , much larger stockpiles would be required to avoid run-out. It is, however, suggested that allocating different drugs for treatment and prophylaxis may constrain resistance development in the population, and therefore reduce the use of antiviral courses .
Our efforts in this study are based on simulating a compartmental epidemic model using parameters estimated in the published literature that involve some degree of uncertainty, particularly with regard to the duration of asymptomatic infection and the effectiveness of antiviral treatment. For the impact of antivirals, we assumed a 60% reduction in absolute infectiousness from the start of treatment, which is consistent with a recent meta-analysis of antiviral effects on secondary attack rates observed in household studies . Since exposed individuals cannot transmit the disease during the short period of latency, we simplified the model to exclude the classes of exposed individuals. Furthermore, treatment of infected individuals is not feasible until after the latent period has elapsed and may be initiated upon diagnosis during symptomatic infection. Although the inclusion of these classes and delay in start of treatment more realistically represents the epidemiology of disease , the results are expected to alter quantitatively. While emphasizing the qualitative aspects of the results, we understand that this modelling approach is subjected to several limitations, particularly with regard to heterogeneity in population interactions and stochastic effects at the early stages of an outbreak. There is also much uncertainty about the parameters governing resistance in vivo , and how a novel influenza strain would affect different populations with distinctly different mobility patterns . Nonetheless, combined with the previous work on strategic use of drugs for reducing the likelihood of resistance emergence , our results suggest that prolonging the effectiveness of antiviral drugs would need to be considered in practical implementation of treatment strategies, especially with the expected delay in availability of a strain-specific vaccine. We should point out that in our model, there is no parameter quantifying the detection of the outbreak. However, in the case of pandemic influenza, the previous threshold used for identification of seasonal outbreaks when ~1 – 5% of the population has been infected seems unrealistically high , especially in light of the ongoing surveillance and also recent experience with SARS and other emerging infectious diseases. Furthermore, given the uncertainty of parameter estimation, the timing results of this work should not be interpreted quantitatively, but rather as a general principle for antiviral strategies that delaying the onset of wide-scale treatment can potentially reduce the overall disease burden while preventing large resistant outbreaks.
Our simulations show that a second wave of infections can occur due to the emergence of highly transmissible resistance or as a result of run-out under the scenario of limited antiviral stockpile. The results demonstrate that conservative treatment levels during the early stages of the outbreak, followed by a timely increase in the scale of drug-use, can minimize the likelihood of both resistance emergence and run-out. The findings suggest that, for a 1918-like influenza virus, pandemic plans should consider stockpiling antiviral drugs for treatment of at least 20% of the population.
This study was in part supported by the Natural Sciences and Engineering Research Council of Canada (NSERC), and the Mathematics of Information Technology and Complex Systems (MITACS). The authors would like to thank the reviewers for insightful comments that have greatly improved the paper.
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