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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.

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Definition of a pandemic profile and of the outcome variables considered.(A) The upper graph represents the daily incidence of flu in each city affected by the influenza pandemic. The first outcome variable, the cumulated number of cases at the end of the pandemic within all affected cities, is given by sum of areas under the curves of incidence. The second outcome variable, the number of cities affected by the pandemic is given by the number of incidence curves. (B) The day of peak is defined as the day when the incidence rate is maximal. It is represented in each city affected by the pandemic by a deep black square, the level of grey in the other squares being proportional with the daily incidence of flu (scaled separately on the maximum for each city). The cities are represented in the order in which they are affected by the pandemic. (C) The third outcome variable, the mean time to peak, is calculated as the mean time between the start of the pandemic and its peak over all cities affected. The fourth outcome variable represented is the total duration of the pandemic and is defined by the time lag between the apparition of the first case in the first city affected and the last one in the last city. The fifth variable not represented on this graph is the standard deviation of time to peak, calculated as the standard deviation of the time between the start of the pandemic and its peak over all affected cities.
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pone-0001478-g002: Definition of a pandemic profile and of the outcome variables considered.(A) The upper graph represents the daily incidence of flu in each city affected by the influenza pandemic. The first outcome variable, the cumulated number of cases at the end of the pandemic within all affected cities, is given by sum of areas under the curves of incidence. The second outcome variable, the number of cities affected by the pandemic is given by the number of incidence curves. (B) The day of peak is defined as the day when the incidence rate is maximal. It is represented in each city affected by the pandemic by a deep black square, the level of grey in the other squares being proportional with the daily incidence of flu (scaled separately on the maximum for each city). The cities are represented in the order in which they are affected by the pandemic. (C) The third outcome variable, the mean time to peak, is calculated as the mean time between the start of the pandemic and its peak over all cities affected. The fourth outcome variable represented is the total duration of the pandemic and is defined by the time lag between the apparition of the first case in the first city affected and the last one in the last city. The fifth variable not represented on this graph is the standard deviation of time to peak, calculated as the standard deviation of the time between the start of the pandemic and its peak over all affected cities.

Mentions: Pandemic profiles were described by five outcome variables: (1) the cumulative number of cases at the end of the pandemic for all affected cities; (2) the total duration of the pandemic defined by the time lag between the first case in the first city affected and the last case in the last city; (3) the number of cities affected by the pandemic; (4) the mean time to peak, calculated as the mean time between the start of the pandemic and its peak over all cities affected; and (5) the standard deviation of the time to peak. The first three outcome variables explored the global burden of the pandemic whereas the last two focused on the dynamics of the pandemic within the network of cities. Figure 2 represents the pandemic's course within four cities of the network, the total duration, the mean time to peak and the total number of cases (the area under the curve of the global incidence). We considered that a city was affected if the daily incidence rate reached 1/100,000. The day of peak was defined as the day when the incidence rate is maximal in each city.


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)

Definition of a pandemic profile and of the outcome variables considered.(A) The upper graph represents the daily incidence of flu in each city affected by the influenza pandemic. The first outcome variable, the cumulated number of cases at the end of the pandemic within all affected cities, is given by sum of areas under the curves of incidence. The second outcome variable, the number of cities affected by the pandemic is given by the number of incidence curves. (B) The day of peak is defined as the day when the incidence rate is maximal. It is represented in each city affected by the pandemic by a deep black square, the level of grey in the other squares being proportional with the daily incidence of flu (scaled separately on the maximum for each city). The cities are represented in the order in which they are affected by the pandemic. (C) The third outcome variable, the mean time to peak, is calculated as the mean time between the start of the pandemic and its peak over all cities affected. The fourth outcome variable represented is the total duration of the pandemic and is defined by the time lag between the apparition of the first case in the first city affected and the last one in the last city. The fifth variable not represented on this graph is the standard deviation of time to peak, calculated as the standard deviation of the time between the start of the pandemic and its peak over all affected cities.
© Copyright Policy
Related In: Results  -  Collection

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

pone-0001478-g002: Definition of a pandemic profile and of the outcome variables considered.(A) The upper graph represents the daily incidence of flu in each city affected by the influenza pandemic. The first outcome variable, the cumulated number of cases at the end of the pandemic within all affected cities, is given by sum of areas under the curves of incidence. The second outcome variable, the number of cities affected by the pandemic is given by the number of incidence curves. (B) The day of peak is defined as the day when the incidence rate is maximal. It is represented in each city affected by the pandemic by a deep black square, the level of grey in the other squares being proportional with the daily incidence of flu (scaled separately on the maximum for each city). The cities are represented in the order in which they are affected by the pandemic. (C) The third outcome variable, the mean time to peak, is calculated as the mean time between the start of the pandemic and its peak over all cities affected. The fourth outcome variable represented is the total duration of the pandemic and is defined by the time lag between the apparition of the first case in the first city affected and the last one in the last city. The fifth variable not represented on this graph is the standard deviation of time to peak, calculated as the standard deviation of the time between the start of the pandemic and its peak over all affected cities.
Mentions: Pandemic profiles were described by five outcome variables: (1) the cumulative number of cases at the end of the pandemic for all affected cities; (2) the total duration of the pandemic defined by the time lag between the first case in the first city affected and the last case in the last city; (3) the number of cities affected by the pandemic; (4) the mean time to peak, calculated as the mean time between the start of the pandemic and its peak over all cities affected; and (5) the standard deviation of the time to peak. The first three outcome variables explored the global burden of the pandemic whereas the last two focused on the dynamics of the pandemic within the network of cities. Figure 2 represents the pandemic's course within four cities of the network, the total duration, the mean time to peak and the total number of cases (the area under the curve of the global incidence). We considered that a city was affected if the daily incidence rate reached 1/100,000. The day of peak was defined as the day when the incidence rate is maximal in each city.

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