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Impact of School Cycles and Environmental Forcing on the Timing of Pandemic Influenza Activity in Mexican States, May-December 2009.

Tamerius J, Viboud C, Shaman J, Chowell G - PLoS Comput. Biol. (2015)

Bottom Line: In particular, school effects may predominate in pandemic seasons marked by an atypical concentration of cases among children.For the 2009 A/H1N1 pandemic, Mexico is a particularly interesting case study due to its broad geographic extent encompassing temperate and tropical regions, well-documented regional variation in the occurrence of pandemic outbreaks, and coincidence of several school breaks during the pandemic period.We use these models to explore the effect of environmental, school-related and travel factors on the generation of spatially-heterogeneous pandemic waves.

View Article: PubMed Central - PubMed

Affiliation: Department of Geographical and Sustainability Sciences, University of Iowa, Iowa City, Iowa, United States of America.

ABSTRACT
While a relationship between environmental forcing and influenza transmission has been established in inter-pandemic seasons, the drivers of pandemic influenza remain debated. In particular, school effects may predominate in pandemic seasons marked by an atypical concentration of cases among children. For the 2009 A/H1N1 pandemic, Mexico is a particularly interesting case study due to its broad geographic extent encompassing temperate and tropical regions, well-documented regional variation in the occurrence of pandemic outbreaks, and coincidence of several school breaks during the pandemic period. Here we fit a series of transmission models to daily laboratory-confirmed influenza data in 32 Mexican states using MCMC approaches, considering a meta-population framework or the absence of spatial coupling between states. We use these models to explore the effect of environmental, school-related and travel factors on the generation of spatially-heterogeneous pandemic waves. We find that the spatial structure of the pandemic is best understood by the interplay between regional differences in specific humidity (explaining the occurrence of pandemic activity towards the end of the school term in late May-June 2009 in more humid southeastern states), school vacations (preventing influenza transmission during July-August in all states), and regional differences in residual susceptibility (resulting in large outbreaks in early fall 2009 in central and northern Mexico that had yet to experience fully-developed outbreaks). Our results are in line with the concept that very high levels of specific humidity, as present during summer in southeastern Mexico, favor influenza transmission, and that school cycles are a strong determinant of pandemic wave timing.

No MeSH data available.


Related in: MedlinePlus

Variability of R0 and Re during May-December 2009 as a function of changes in specific humidity, interventions, school cycles, and susceptibility, and the resulting impact on pandemic influenza activity, as predicted by the model.(A) The relationship between specific humidity and R0. The gray lines are 500 samples generated from the posterior distributions, and the black line corresponds to the mean value of the posterior means. (B) The time series of average specific humidity for central and northern states (solid line) and southeastern states (dashed line). (C) Time series of R0 for best-fit parameter combination. The step features are related to spring break, the intervention period, summer break and winter break, respectively. (D) Time series of simulated Re. (E) Time series of simulated population level susceptibility. (F) Time series of simulated case proportions. For B-F, shaded areas in background correspond to spring vacation, period of school closures and intervention measures, summer vacation, and winter vacation, respectively. The solid lines correspond to the central and northern states, and the dashed lines correspond to the southeastern states. The simulated values were generated using the mean value of the posterior means for each parameter.
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pcbi.1004337.g005: Variability of R0 and Re during May-December 2009 as a function of changes in specific humidity, interventions, school cycles, and susceptibility, and the resulting impact on pandemic influenza activity, as predicted by the model.(A) The relationship between specific humidity and R0. The gray lines are 500 samples generated from the posterior distributions, and the black line corresponds to the mean value of the posterior means. (B) The time series of average specific humidity for central and northern states (solid line) and southeastern states (dashed line). (C) Time series of R0 for best-fit parameter combination. The step features are related to spring break, the intervention period, summer break and winter break, respectively. (D) Time series of simulated Re. (E) Time series of simulated population level susceptibility. (F) Time series of simulated case proportions. For B-F, shaded areas in background correspond to spring vacation, period of school closures and intervention measures, summer vacation, and winter vacation, respectively. The solid lines correspond to the central and northern states, and the dashed lines correspond to the southeastern states. The simulated values were generated using the mean value of the posterior means for each parameter.

Mentions: The estimated relationship between specific humidity and R0 was J-shaped, with greatest R0 at high levels of specific humidity and minimal R0 at moderate levels of specific humidity (Fig 5; Table 3). Our estimated R0 values ranged between 1.14 and 1.26, depending on specific humidity conditions.


Impact of School Cycles and Environmental Forcing on the Timing of Pandemic Influenza Activity in Mexican States, May-December 2009.

Tamerius J, Viboud C, Shaman J, Chowell G - PLoS Comput. Biol. (2015)

Variability of R0 and Re during May-December 2009 as a function of changes in specific humidity, interventions, school cycles, and susceptibility, and the resulting impact on pandemic influenza activity, as predicted by the model.(A) The relationship between specific humidity and R0. The gray lines are 500 samples generated from the posterior distributions, and the black line corresponds to the mean value of the posterior means. (B) The time series of average specific humidity for central and northern states (solid line) and southeastern states (dashed line). (C) Time series of R0 for best-fit parameter combination. The step features are related to spring break, the intervention period, summer break and winter break, respectively. (D) Time series of simulated Re. (E) Time series of simulated population level susceptibility. (F) Time series of simulated case proportions. For B-F, shaded areas in background correspond to spring vacation, period of school closures and intervention measures, summer vacation, and winter vacation, respectively. The solid lines correspond to the central and northern states, and the dashed lines correspond to the southeastern states. The simulated values were generated using the mean value of the posterior means for each parameter.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4546376&req=5

pcbi.1004337.g005: Variability of R0 and Re during May-December 2009 as a function of changes in specific humidity, interventions, school cycles, and susceptibility, and the resulting impact on pandemic influenza activity, as predicted by the model.(A) The relationship between specific humidity and R0. The gray lines are 500 samples generated from the posterior distributions, and the black line corresponds to the mean value of the posterior means. (B) The time series of average specific humidity for central and northern states (solid line) and southeastern states (dashed line). (C) Time series of R0 for best-fit parameter combination. The step features are related to spring break, the intervention period, summer break and winter break, respectively. (D) Time series of simulated Re. (E) Time series of simulated population level susceptibility. (F) Time series of simulated case proportions. For B-F, shaded areas in background correspond to spring vacation, period of school closures and intervention measures, summer vacation, and winter vacation, respectively. The solid lines correspond to the central and northern states, and the dashed lines correspond to the southeastern states. The simulated values were generated using the mean value of the posterior means for each parameter.
Mentions: The estimated relationship between specific humidity and R0 was J-shaped, with greatest R0 at high levels of specific humidity and minimal R0 at moderate levels of specific humidity (Fig 5; Table 3). Our estimated R0 values ranged between 1.14 and 1.26, depending on specific humidity conditions.

Bottom Line: In particular, school effects may predominate in pandemic seasons marked by an atypical concentration of cases among children.For the 2009 A/H1N1 pandemic, Mexico is a particularly interesting case study due to its broad geographic extent encompassing temperate and tropical regions, well-documented regional variation in the occurrence of pandemic outbreaks, and coincidence of several school breaks during the pandemic period.We use these models to explore the effect of environmental, school-related and travel factors on the generation of spatially-heterogeneous pandemic waves.

View Article: PubMed Central - PubMed

Affiliation: Department of Geographical and Sustainability Sciences, University of Iowa, Iowa City, Iowa, United States of America.

ABSTRACT
While a relationship between environmental forcing and influenza transmission has been established in inter-pandemic seasons, the drivers of pandemic influenza remain debated. In particular, school effects may predominate in pandemic seasons marked by an atypical concentration of cases among children. For the 2009 A/H1N1 pandemic, Mexico is a particularly interesting case study due to its broad geographic extent encompassing temperate and tropical regions, well-documented regional variation in the occurrence of pandemic outbreaks, and coincidence of several school breaks during the pandemic period. Here we fit a series of transmission models to daily laboratory-confirmed influenza data in 32 Mexican states using MCMC approaches, considering a meta-population framework or the absence of spatial coupling between states. We use these models to explore the effect of environmental, school-related and travel factors on the generation of spatially-heterogeneous pandemic waves. We find that the spatial structure of the pandemic is best understood by the interplay between regional differences in specific humidity (explaining the occurrence of pandemic activity towards the end of the school term in late May-June 2009 in more humid southeastern states), school vacations (preventing influenza transmission during July-August in all states), and regional differences in residual susceptibility (resulting in large outbreaks in early fall 2009 in central and northern Mexico that had yet to experience fully-developed outbreaks). Our results are in line with the concept that very high levels of specific humidity, as present during summer in southeastern Mexico, favor influenza transmission, and that school cycles are a strong determinant of pandemic wave timing.

No MeSH data available.


Related in: MedlinePlus