Antiviral resistance during pandemic influenza: implications for stockpiling and drug use

<p>Abstract</p> <p>Background</p> <p>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.</p> <p>Methods</p> <p>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.</p> <p>Results</p> <p>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.</p> <p>Conclusion</p> <p>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.</p

Modeling influenza epidemics and pandemics: insights into the future of swine flu (H1N1)

Here we present a review of the literature of influenza modeling studies, and discuss how these models can provide insights into the future of the currently circulating novel strain of influenza A (H1N1), formerly known as swine flu. We discuss how the feasibility of controlling an epidemic critically depends on the value of the Basic Reproduction Number (R0). The R0 for novel influenza A (H1N1) has recently been estimated to be between 1.4 and 1.6. This value is below values of R0 estimated for the 1918–1919 pandemic strain (mean R0~2: range 1.4 to 2.8) and is comparable to R0 values estimated for seasonal strains of influenza (mean R0 1.3: range 0.9 to 2.1). By reviewing results from previous modeling studies we conclude it is theoretically possible that a pandemic of H1N1 could be contained. However it may not be feasible, even in resource-rich countries, to achieve the necessary levels of vaccination and treatment for control. As a recent modeling study has shown, a global cooperative strategy will be essential in order to control a pandemic. This strategy will require resource-rich countries to share their vaccines and antivirals with resource-constrained and resource-poor countries. We conclude our review by discussing the necessity of developing new biologically complex models. We suggest that these models should simultaneously track the transmission dynamics of multiple strains of influenza in bird, pig and human populations. Such models could be critical for identifying effective new interventions, and informing pandemic preparedness planning. Finally, we show that by modeling cross-species transmission it may be possible to predict the emergence of pandemic strains of influenza

Transmission of Aerosolized Seasonal H1N1 Influenza A to Ferrets

Influenza virus is a major cause of morbidity and mortality worldwide, yet little quantitative understanding of transmission is available to guide evidence-based public health practice. Recent studies of influenza non-contact transmission between ferrets and guinea pigs have provided insights into the relative transmission efficiencies of pandemic and seasonal strains, but the infecting dose and subsequent contagion has not been quantified for most strains. In order to measure the aerosol infectious dose for 50% (aID50) of seronegative ferrets, seasonal influenza virus was nebulized into an exposure chamber with controlled airflow limiting inhalation to airborne particles less than 5 µm diameter. Airborne virus was collected by liquid impinger and Teflon filters during nebulization of varying doses of aerosolized virus. Since culturable virus was accurately captured on filters only up to 20 minutes, airborne viral RNA collected during 1-hour exposures was quantified by two assays, a high-throughput RT-PCR/mass spectrometry assay detecting 6 genome segments (Ibis T5000™ Biosensor system) and a standard real time RT-qPCR assay. Using the more sensitive T5000 assay, the aID50 for A/New Caledonia/20/99 (H1N1) was approximately 4 infectious virus particles under the exposure conditions used. Although seroconversion and sustained levels of viral RNA in upper airway secretions suggested established mucosal infection, viral cultures were almost always negative. Thus after inhalation, this seasonal H1N1 virus may replicate less efficiently than H3N2 virus after mucosal deposition and exhibit less contagion after aerosol exposure

Quantification system for the viral dynamics of a highly pathogenic simian/human immunodeficiency virus based on an in vitro experiment and a mathematical model

<p>Abstract</p> <p>Background</p> <p>Developing a quantitative understanding of viral kinetics is useful for determining the pathogenesis and transmissibility of the virus, predicting the course of disease, and evaluating the effects of antiviral therapy. The availability of data in clinical, animal, and cell culture studies, however, has been quite limited. Many studies of virus infection kinetics have been based solely on measures of total or infectious virus count. Here, we introduce a new mathematical model which tracks both infectious and total viral load, as well as the fraction of infected and uninfected cells within a cell culture, and apply it to analyze time-course data of an SHIV infection <it>in vitro</it>.</p> <p>Results</p> <p>We infected HSC-F cells with SHIV-KS661 and measured the concentration of Nef<it>-</it>negative (target) and Nef<it>-</it>positive (infected) HSC-F cells, the total viral load, and the infectious viral load daily for nine days. The experiments were repeated at four different MOIs, and the model was fitted to the full dataset simultaneously. Our analysis allowed us to extract an infected cell half-life of 14.1 h, a half-life of SHIV-KS661 infectiousness of 17.9 h, a virus burst size of 22.1 thousand RNA copies or 0.19 TCID₅₀, and a basic reproductive number of 62.8. Furthermore, we calculated that SHIV-KS661 virus-infected cells produce at least 1 infectious virion for every 350 virions produced.</p> <p>Conclusions</p> <p>Our method, combining <it>in vitro </it>experiments and a mathematical model, provides detailed quantitative insights into the kinetics of the SHIV infection which could be used to significantly improve the understanding of SHIV and HIV-1 pathogenesis. The method could also be applied to other viral infections and used to improve the <it>in vitro </it>determination of the effect and efficacy of antiviral compounds.</p

Epidemiological Models: A Study of Two Retroviruses, HIV and HTLV-I

HIV is an example of a disease where the pathogen mutates so that it is not recognized by the immune system. In this paper, we have studied several models and two retroviruses, viz., HIV and Human T-lymphotropic virus (HTLV-I).We have used SIMULINK to draw graphs and study the associated modeling problems. © Springer India 2014