Measured Dynamic Social Contact Patterns Explain the Spread of H1N1v Influenza

Eames, Ken T. D.; Tilston, Natasha L.; Brooks-Pollock, Ellen; Edmunds, W. John

doi:10.1371/journal.pcbi.1002425

Cited by 187 publications

(230 citation statements)

References 34 publications

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“…control but also, designing school closure strategies in planning for seasonal and pandemic influenza. We estimated transmission rates during school closures that are comparable with published estimates based on influenza surveillance data (41), sociological surveys (42), and measles outbreak dynamics (43). Unlike previous estimates, our analysis reflects contact patterns in normal vacation periods rather than during severe outbreaks, for which there may be additional changes in behavior that affect transmission rate.…”

Section: Discussionmentioning

confidence: 71%

Respiratory virus transmission dynamics determine timing of asthma exacerbation peaks: Evidence from a population-level model

Eggo

Scott

Galvani

et al. 2016

Proc. Natl. Acad. Sci. U.S.A.

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Asthma exacerbations exhibit a consistent annual pattern, closely mirroring the school calendar. Although respiratory viruses-the "common cold" viruses-are implicated as a principal cause, there is little evidence to link viral prevalence to seasonal differences in risk. We jointly fit a common cold transmission model and a model of biological and environmental exacerbation triggers to estimate effects on hospitalization risk. Asthma hospitalization rate, influenza prevalence, and air quality measures are available, but common cold circulation is not; therefore, we generate estimates of viral prevalence using a transmission model. Our deterministic multivirus transmission model includes transmission rates that vary when school is closed. We jointly fit the two models to 7 y of daily asthma hospitalizations in adults and children (66,000 events) in eight metropolitan areas. For children, we find that daily viral prevalence is the strongest predictor of asthma hospitalizations, with transmission reduced by 45% (95% credible interval =41-49%) during school closures. We detect a transient period of nonspecific immunity between infections lasting 19 (17-21) d. For adults, hospitalizations are more variable, with influenza driving wintertime peaks. Neither particulate matter nor ozone was an important predictor, perhaps because of the large geographic area of the populations. The school calendar clearly and predictably drives seasonal variation in common cold prevalence, which results in the "back-to-school" asthma exacerbation pattern seen in children and indirectly contributes to exacerbation risk in adults. This study provides a framework for anticipating the seasonal dynamics of common colds and the associated risks for asthmatics.

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Section: Discussionmentioning

confidence: 71%

Respiratory virus transmission dynamics determine timing of asthma exacerbation peaks: Evidence from a population-level model

Eggo

Scott

Galvani

et al. 2016

Proc. Natl. Acad. Sci. U.S.A.

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“…give a good fit to the patterns of influenza incidence observed in the UK in 2009 [21]. To one decimal place, these are as follows: …”

Section: Methodsmentioning

confidence: 80%

“…We used values of pre-epidemic immunity as estimated elsewhere [21,27,30]. Assuming that everyone is susceptible at the start of the outbreak makes little difference to our results; most immunity is in older individuals, whose social mixing behaviour means that they are less likely than children to become infected.…”

Section: Methodsmentioning

confidence: 99%

“…One likely result of school closure would be to reduce the amount of social contact between school children, and thus reduce incidence [3,9,14,[20][21][22]. While there are few studies that have measured the impact of disease-related school closure on contact patterns [16,17], the impact of school holidays on contact patterns is better documented.…”

Section: Introductionmentioning

confidence: 99%

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The influence of school holiday timing on epidemic impact

Eames

2013

Epidemiol. Infect.

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SUMMARYThe impact of reactive school closure on an epidemic is uncertain, since it is not clear how an unplanned closure will affect social mixing patterns. The effect of school holidays on social mixing patterns is better understood. Here, we use mathematical models to explore the influence of the timing of school holidays on the final size and peak incidence of an influenza-like epidemic. A well-timed holiday can reduce the impact of an epidemic, in particular substantially reducing an epidemic's peak. Final size and peak incidence cannot both be minimized: a later holiday is optimal for minimizing the final size, while an earlier holiday minimizes peak incidence. Using social mixing data from the UK, we estimated that, had the 2009 influenza epidemic not been interrupted by the school summer holidays, the final size would have been about 20% larger and the peak about 170% higher.

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“…Evaluating the effect of SVs on the spread of influenza is difficult, as typically the influenza season does not overlap with the school summer vacation. The early initiation of the pandemic has enabled one to examine and confirm the curtailing impact of SVs on influenza transmission [10,12,[15][16][17][18][19][20]. In Israel, the early initiation resulted in the pandemic overlapping not only with the summer vacation during July and August, but also with the Jewish New Year holidays in October (figure 1a).…”

Section: Introductionmentioning

confidence: 99%

Model-based reconstruction of an epidemic using multiple datasets: understanding influenza A/H1N1 pandemic dynamics in Israel

Yaari

Katriel

Stone

et al. 2016

J. R. Soc. Interface.

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Intensified surveillance during the 2009 A/H1N1 influenza pandemic in Israel resulted in large virological and serological datasets, presenting a unique opportunity for investigating the pandemic dynamics. We employ a conditional likelihood approach for fitting a disease transmission model to virological and serological data, conditional on clinical data. The model is used to reconstruct the temporal pattern of the pandemic in Israel in five age-groups and evaluate the factors that shaped it. We estimate the reproductive number at the beginning of the pandemic to be R ¼ 1.4. We find that the combined effect of varying absolute humidity conditions and school vacations (SVs) is responsible for the infection pattern, characterized by three epidemic waves. Overall attack rate is estimated at 32% (28-35%) with a large variation among the age-groups: the highest attack rates within school children and the lowest within the elderly. This pattern of infection is explained by a combination of the age-group contact structure and increasing immunity with age. We assess that SVs increased the overall attack rates by prolonging the pandemic into the winter. Vaccinating school children would have been the optimal strategy for minimizing infection rates in all age-groups.

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Measured Dynamic Social Contact Patterns Explain the Spread of H1N1v Influenza

Cited by 187 publications

References 34 publications

Respiratory virus transmission dynamics determine timing of asthma exacerbation peaks: Evidence from a population-level model

Respiratory virus transmission dynamics determine timing of asthma exacerbation peaks: Evidence from a population-level model

The influence of school holiday timing on epidemic impact

Model-based reconstruction of an epidemic using multiple datasets: understanding influenza A/H1N1 pandemic dynamics in Israel

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