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. 2008 Aug;136(8):1035-45.
doi: 10.1017/S0950268807009284. Epub 2007 Sep 13.

Modelling control measures to reduce the impact of pandemic influenza among schoolchildren

S-C Chen  1 , C-M Liao
Affiliations

Modelling control measures to reduce the impact of pandemic influenza among schoolchildren

S-C Chen et al. Epidemiol Infect. 2008 Aug.

Abstract

We coupled the Wells-Riley equation and the susceptible-exposed-infected-recovery (SEIR) model to quantify the impact of the combination of indoor air-based control measures of enhanced ventilation and respiratory masking in containing pandemic influenza within an elementary school. We integrated indoor environmental factors of a real elementary school and aetiological characteristics of influenza to estimate the age-specific risk of infection (P) and basic reproduction number (R(0)). We combined the enhanced ventilation rates of 0.5, 1, 1.5, and 2/h and respiratory masking with 60%, 70%, 80%, and 95% efficacies, respectively, to predict the reducing level of R0. We also took into account the critical vaccination coverage rate among schoolchildren. Age-specific P and R(0) were estimated respectively to be 0.29 and 16.90; 0.56 and 16.11; 0.59 and 12.88; 0.64 and 16.09; and 0.07 and 2.80 for five age groups 4-6, 7-8, 9-10, 11-12, and 25-45 years, indicating pre-schoolchildren have the highest transmission potential. We conclude that our integrated approach, employing the mechanism of transmission of indoor respiratory infection, population-dynamic transmission model, and the impact of infectious control programmes, is a powerful tool for risk profiling prediction of pandemic influenza among schoolchildren.

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Figures

Fig. 1
Fig. 1
Pandemic influenza modelling where we consider the transmission dynamics of the following population: susceptible (S), exposed (E), infected (I), and recovery (R). (See text for detailed description of symbols.)
Fig. 2
Fig. 2
(a) The effects of respiratory masking with 60%, 70%, 80%, and 95% efficacy for an infectious person at an elementary school population with different age groups 4–6, 7–8, 9–10, 11–12, and 25–45 years. (b) The effects of enhancing the ventilation condition (enhanced ACH) with 0·5, 1·0, 1·5, and 2·0 ACH for decreasing R0. (c) Modelling the impact on R0 by enhancing ACH based on the different initial ventilation conditions with 0·2, 0·5, and 1·0 ACH, respectively.
Fig. 3
Fig. 3
Modelling the impact of the combination of respiratory masking and enhanced ventilation condition on R0 for age groups 4–6, 7–8, 9–10, 11–12, and 25–45 years.
Fig. 4
Fig. 4
Modelling the progress of a 30-day influenza outbreak at an elementary school with a kindergarten. (a) We also represent the proportions of time-dependent infected number/total number because the population size is different between age groups. (b) Time-dependent infected number was estimated by the SEIR model. Parameter values: β=0·043, 0·082, 0·086, 0·094, and 0·011 for age groups 4–6, 7–8, 9–10, 11–12, and 25–45 years, respectively.
Fig. 5
Fig. 5
The prediction for the transmission pattern of infected number and intervention by different vaccination with 0%, 65%, 70%, 80%, and 90% efficacies, respectively for (a) 4–6, (b) 7–8, (c) 9–10, (d) 11–12, and (e) 25–45 years.
Fig. 6
Fig. 6
Impact of multiple measures on infected numbers in the 4–6 years age group. Control measures consider the situation without intervention and five integrated control methods including 80% vaccination coverage rate, 80% respiratory masking efficacy, and enhanced 1·5 ACH.
Fig. 7
Fig. 7
Modelling the control measures such as the vaccination at birth, vaccination at influenza epidemic season, and infected student to be isolated at home. Vaccination coverage rates p1 and p2 are newly born individuals that are vaccinated at birth and individuals that are vaccinated in the influenza epidemic season. Infectious individuals recover at rate υ to become immune, and the vaccination loss rate ρ is assumed to be 4–5 months. (See text for detailed description of symbols.)
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