Limits...
Does the effectiveness of control measures depend on the influenza pandemic profile?

Kernéis S, Grais RF, Boëlle PY, Flahault A, Vergu E - PLoS ONE (2008)

Bottom Line: A multivariate sensitivity analysis showed that the transmission rate and proportion of susceptibles have a strong impact on the pandemic diffusion.In both cases, the date of introduction for five control measures (masks, isolation, prophylactic or therapeutic use of antivirals, vaccination) correlated strongly with pandemic outcomes.Conversely, the coverage and efficacy of these interventions only moderately correlated with pandemic outcomes in the case of a massive pandemic.

View Article: PubMed Central - PubMed

Affiliation: Université Pierre et Marie Curie-Paris6, UMR-S 707, Paris, France.

ABSTRACT

Background: Although strategies to contain influenza pandemics are well studied, the characterization and the implications of different geographical and temporal diffusion patterns of the pandemic have been given less attention.

Methodology/main findings: Using a well-documented metapopulation model incorporating air travel between 52 major world cities, we identified potential influenza pandemic diffusion profiles and examined how the impact of interventions might be affected by this heterogeneity. Clustering methods applied to a set of pandemic simulations, characterized by seven parameters related to the conditions of emergence that were varied following Latin hypercube sampling, were used to identify six pandemic profiles exhibiting different characteristics notably in terms of global burden (from 415 to >160 million of cases) and duration (from 26 to 360 days). A multivariate sensitivity analysis showed that the transmission rate and proportion of susceptibles have a strong impact on the pandemic diffusion. The correlation between interventions and pandemic outcomes were analyzed for two specific profiles: a fast, massive pandemic and a slow building, long-lasting one. In both cases, the date of introduction for five control measures (masks, isolation, prophylactic or therapeutic use of antivirals, vaccination) correlated strongly with pandemic outcomes. Conversely, the coverage and efficacy of these interventions only moderately correlated with pandemic outcomes in the case of a massive pandemic. Pre-pandemic vaccination influenced pandemic outcomes in both profiles, while travel restriction was the only measure without any measurable effect in either.

Conclusions: our study highlights: (i) the great heterogeneity in possible profiles of a future influenza pandemic; (ii) the value of being well prepared in every country since a pandemic may have heavy consequences wherever and whenever it starts; (iii) the need to quickly implement control measures and even to anticipate pandemic emergence through pre-pandemic vaccination; and (iv) the value of combining all available control measures except perhaps travel restrictions.

Show MeSH

Related in: MedlinePlus

Results of the clustering analysis: the six profiles (profile A in red, B in green, C in blue, D in light blue, E in pink and F in orange) are represented according to three criteria: the total duration, the total number of cases and the mean time to peak.
© Copyright Policy
Related In: Results  -  Collection


getmorefigures.php?uid=PMC2198944&req=5

pone-0001478-g003: Results of the clustering analysis: the six profiles (profile A in red, B in green, C in blue, D in light blue, E in pink and F in orange) are represented according to three criteria: the total duration, the total number of cases and the mean time to peak.

Mentions: As is shown in Figure 3, where axes represent three of the discriminating criteria, profiles could be grouped based on (i) the total number of cases: massive pandemics (group A), moderate pandemics (groups B, C and D) and mild pandemics (groups E and F), (ii) duration (groups A and F distinct from groups B, D and E), and (iii) the mean time to peak (groups A and C distinct from groups B and E).


Does the effectiveness of control measures depend on the influenza pandemic profile?

Kernéis S, Grais RF, Boëlle PY, Flahault A, Vergu E - PLoS ONE (2008)

Results of the clustering analysis: the six profiles (profile A in red, B in green, C in blue, D in light blue, E in pink and F in orange) are represented according to three criteria: the total duration, the total number of cases and the mean time to peak.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC2198944&req=5

pone-0001478-g003: Results of the clustering analysis: the six profiles (profile A in red, B in green, C in blue, D in light blue, E in pink and F in orange) are represented according to three criteria: the total duration, the total number of cases and the mean time to peak.
Mentions: As is shown in Figure 3, where axes represent three of the discriminating criteria, profiles could be grouped based on (i) the total number of cases: massive pandemics (group A), moderate pandemics (groups B, C and D) and mild pandemics (groups E and F), (ii) duration (groups A and F distinct from groups B, D and E), and (iii) the mean time to peak (groups A and C distinct from groups B and E).

Bottom Line: A multivariate sensitivity analysis showed that the transmission rate and proportion of susceptibles have a strong impact on the pandemic diffusion.In both cases, the date of introduction for five control measures (masks, isolation, prophylactic or therapeutic use of antivirals, vaccination) correlated strongly with pandemic outcomes.Conversely, the coverage and efficacy of these interventions only moderately correlated with pandemic outcomes in the case of a massive pandemic.

View Article: PubMed Central - PubMed

Affiliation: Université Pierre et Marie Curie-Paris6, UMR-S 707, Paris, France.

ABSTRACT

Background: Although strategies to contain influenza pandemics are well studied, the characterization and the implications of different geographical and temporal diffusion patterns of the pandemic have been given less attention.

Methodology/main findings: Using a well-documented metapopulation model incorporating air travel between 52 major world cities, we identified potential influenza pandemic diffusion profiles and examined how the impact of interventions might be affected by this heterogeneity. Clustering methods applied to a set of pandemic simulations, characterized by seven parameters related to the conditions of emergence that were varied following Latin hypercube sampling, were used to identify six pandemic profiles exhibiting different characteristics notably in terms of global burden (from 415 to >160 million of cases) and duration (from 26 to 360 days). A multivariate sensitivity analysis showed that the transmission rate and proportion of susceptibles have a strong impact on the pandemic diffusion. The correlation between interventions and pandemic outcomes were analyzed for two specific profiles: a fast, massive pandemic and a slow building, long-lasting one. In both cases, the date of introduction for five control measures (masks, isolation, prophylactic or therapeutic use of antivirals, vaccination) correlated strongly with pandemic outcomes. Conversely, the coverage and efficacy of these interventions only moderately correlated with pandemic outcomes in the case of a massive pandemic. Pre-pandemic vaccination influenced pandemic outcomes in both profiles, while travel restriction was the only measure without any measurable effect in either.

Conclusions: our study highlights: (i) the great heterogeneity in possible profiles of a future influenza pandemic; (ii) the value of being well prepared in every country since a pandemic may have heavy consequences wherever and whenever it starts; (iii) the need to quickly implement control measures and even to anticipate pandemic emergence through pre-pandemic vaccination; and (iv) the value of combining all available control measures except perhaps travel restrictions.

Show MeSH
Related in: MedlinePlus