Difference between revisions of "Influenza: Dose Response Models"

Revision as of 15:14, 5 October 2011

Influenza

General overview

Influenza A viruses are members of the family Orthomyxoviridae, which comprises enveloped viruses with segmented, negative-sense RNA genomes. Based on the antigenicity of the two surface glycoproteins, hemagglutinin (HA) and neuraminidase (NA), influenza A viruses are currently divided into 16 HA and 9 NA subtypes, designated as H1-H16 and N1-N9. Over the past century, viruses of the H1N1, H2N2, H3N2, and H1N2 subtypes have circulated in humans. Additionally, new subtypes such as H5N1 and H7N9 have been recently isolated from human as well as poultry. Influenza A virus is one of the most common causes of human respiratory infections and the most significant because they cause high morbidity and mortality. Transmission of influenza can be achieved via environmental reservoirs or human-to-human communication (Kitajima et al. under review; Watanabe et al. under review).

Summary Data

Murphy et al. (1984) intranasally challenged adult volunteers with influenza A (H1N1) California/10/78 cold-adpted viruses. Infection was defined as virus recovery and/or antibody response.

Murphy et al.(1985) challenged adult volunteers with influenza A (H3N2) Washington/897/80 avian-human reassortant viruses via intranasal route. Infection was defined as virus isolation and/or antibody response.

Fan et al. (2009) exposed six-week-old SPF BALB/c mice (five mice/dose) intranasally with a highly pathogenic avian influenza A (H5N1) virus (DKGX/35 strain).

The data from experiments 1 and 2 were able to be statistically pooled.

^*Recommended Model

It is recommended that the pooled experiments 1 and 2 should be used as the best dose-response model. Both strains are common in human outbreaks. The pooling narrows the range of the confidence region of the parameter estimates and enhances the statistical precision.

Optimized Models and Fitting Analyses

Optimization Output for experiment 1

Figure 4.1 Parameter scatter plot for beta Poisson model ellipses signify the 0.9, 0.95 and 0.99 confidence of the parameters.

Figure 4.2 beta Poisson model plot, with confidence bounds around optimized model

Optimization Output for experiment 2

Figure 4.3 Parameter scatter plot for beta Poisson model ellipses signify the 0.9, 0.95 and 0.99 confidence of the parameters.

Figure 4.4 beta Poisson model plot, with confidence bounds around optimized model

Optimization Output for experiment 3

Figure 4.5 Parameter histogram for exponential model (uncertainty of the parameter)

Figure 4.6 Exponential model plot, with confidence bounds around optimized model

Optimization Output for experiment 4

Figure 4.7 Parameter scatter plot for beta Poisson model ellipses signify the 0.9, 0.95 and 0.99 confidence of the parameters.

Figure 4.8 beta Poisson model plot, with confidence bounds around optimized model

Advanced Dose Response Model

Incorporating the time postinoculation into the classical dose-response models for microbial infection generates a class of time-dose-response (TDR) models. The parameter k in the exponential dose-response model (equation 1) and the parameter N50 in the beta-Poisson model (equation 2) were set equal to functions of time that represent in vivo bacterial kinetics. Equations 1-2 with candidate G(t; θ,…) were fit to the time-dependent dose response data from experiment 3. The beta-Poisson TDR model (equation 2) incorporating an exponential-inverse-power distribution provided the best fit to the data. In Fig. 4.9, the best TDR models are plotted to compare with the observed mortalities (Kitajima et al. under review). As shown, the clear difference between the different times postinoculation gives a visible representation of the quantified results that the modification added to the classical models has a substantial effect on the dose response.

FIG. 4.9. The best-fit TDR model (curves) compared to observed mortalities against doses (symbols) from experiment 3.

Summary

The pooling results indicate that the human responses to HIN1 and H3N2 viruses may have similar patterns.

References

Fan, S., Deng, G., Song, J., Tian, G., Suo, Y., Jiang, Y., Guan, Y., Bu, Z., Kawaoka, Y. and Chen, H. (2009) Two amino acid residues in the matrix protein m1 contribute to the virulence difference of h5n1 avian influenza viruses in mice. Virology 384, 28-32.

Kitajima, M., Huang, Y., Watanabe, T., Katayama, H. and Haas, C.N. (under review) Dose-response time modeling for highly pathogenic avian influenza a (h5n1) virus infection. Letters in Applied Microbiology.

Murphy, B.R., Clements, M.L., Madore, H.P., Steinberg, J., O'Donnell, S., Betts, R., Demico, D., Reichman, R.C., Dolin, R. and Maassab, H.F. (1984) Dose response of cold-adapted, reassortant influenza a/california/10/78 virus (h1n1) in adult volunteers. Journal of Infectious Diseases 149, 816.

Murphy, B.R., Clements, M.L., Tierney, E.L., Black, R.E., Stienberg, J. and Chanock, R.M. (1985) Dose response of influenza a/washington/897/80 (h3n2) avian-human reassortant virus in adult volunteers. Journal of Infectious Diseases 152, 225-229.

Watanabe, T., Bartrand, T.A., Omura, T. and Haas, C.N. (under review) Dose-response assessment for influenza a virus based on the datasets of infection with its live attenuated reassortants. Risk Analysis.