Evaluating Temporal Factors in Combined Interventions of Workforce Shift and School Closure for Mitigating the Spread of Influenza

Tianyou Zhang,Gaoxi Xiao,Michael Lees,Stefan Ma,Terence Hung,Xiuju Fu,Gary Kee Khoon Lee,Limsoon Wong,Chee Keong Kwoh,Alessandro Vespignani

doi:10.1371/journal.pone.0032203

Abstract

BackgroundIt is believed that combined interventions may be more effective than individual interventions in mitigating epidemic. However there is a lack of quantitative studies on performance of the combination of individual interventions under different temporal settings.Methodology/Principal FindingsTo better understand the problem, we develop an individual-based simulation model running on top of contact networks based on real-life contact data in Singapore. We model and evaluate the spread of influenza epidemic with intervention strategies of workforce shift and its combination with school closure, and examine the impacts of temporal factors, namely the trigger threshold and the duration of an intervention. By comparing simulation results for intervention scenarios with different temporal factors, we find that combined interventions do not always outperform individual interventions and are more effective only when the duration is longer than 6 weeks or school closure is triggered at the 5% threshold; combined interventions may be more effective if school closure starts first when the duration is less than 4 weeks or workforce shift starts first when the duration is longer than 4 weeks.Conclusions/SignificanceWe therefore conclude that identifying the appropriate timing configuration is crucial for achieving optimal or near optimal performance in mitigating the spread of influenza epidemic. The results of this study are useful to policy makers in deliberating and planning individual and combined interventions.

Highlights

Past influenza pandemics and the recent H1N1 pandemic alert people the unpredictability and potentially overwhelming impacts of influenza outbreaks
We aim to provide a more comprehensive view on the impacts of temporal factors on social distancing interventions for influenza epidemic, helping to answer three key questions: a) do combined interventions always outperform single interventions? b) how do trigger threshold and duration affect the effectiveness of combined interventions? c) does the implementation sequence in a combined intervention make a difference in its effectiveness?
We found when the base transmission probability is 0.04, the empirical tests give the best approximation to R0&1.9 (95% Confident Interval (CI) 1.871–1.924), which yields the mean generation time of 2.5 days

Summary

Introduction

Past influenza pandemics and the recent H1N1 pandemic alert people the unpredictability and potentially overwhelming impacts of influenza outbreaks. While it is certain that the pandemic will arrive in human societies, it is almost impossible to predict the virus type, transmission manner, and attack and mortality rates etc. Such unpredictability seriously challenges the public health system. Supplies of vaccine and pharmaceuticals may not be available or may be in shortage for a few months or even longer while a substantial number of infected cases has been reported Under such critical circumstances, non-pharmaceutical interventions are usually considered in the first place, aiming at mitigating the spread and lowering the attack rate and fatality. There is a lack of quantitative studies on performance of the combination of individual interventions under different temporal settings

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