Analysis of the effectiveness of interventions used during the 2009 A/H1N1 influenza pandemic

Halder, Nilimesh; Kelso, Joel; Milne, George J.

doi:10.1186/1471-2458-10-168

Cited by 79 publications

(106 citation statements)

References 38 publications

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“…Longer closures will interfere with the education of children, with the occupations of their parents, and with social or economic activity [16,[25][26][27]. Our findings suggest that short-term closures, even closures of a few days, effectively decrease the number of infections after the resumption of classes.…”

Section: Discussionmentioning

confidence: 91%

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Effect of short-term school closures on the H1N1 pandemic in Japan: a comparative case study

et al. 2012

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Purpose. The 2009 worldwide influenza A/H1N1 pandemic particularly affected younger people, including schoolchildren. We assessed the effects of class/school closure during the pandemic on the spread of H1N1 infection in Japan. Methods. We prospectively monitored 2141 schoolchildren in 57 classes at 2 elementary schools and 2 junior high schools in Japan, and we evaluated the effects of class/school closures on the spread of H1N1 using descriptive epidemiological methods.Results. The cumulative rate of H1N1 infection among these children was 40.9% (876 children). There was a total of 53 closures of 40 classes, including school closures, during the pandemic. Time-course changes in the epidemic curve showed that school closure reduced the following epidemic peak more than class closure. A Poisson regression model showed that a longer duration of closure was significantly related to decreased H1N1 occurrence after the resumption of classes. Conclusions. School closure more effectively inhibits subsequent epidemic outbreaks than class closure. Longer school closures are effective in reducing the spread of infection, and school closure should be implemented as early as possible.

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Section: Discussionmentioning

confidence: 91%

“…Measures instituted to prevent the transmission of H1N1 included school closures [12][13][14][15]. The effects of school closures were evaluated in simulation models [14][15][16] and some reports have shown the effects of actual closures [17,18].…”

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confidence: 99%

Effect of short-term school closures on the H1N1 pandemic in Japan: a comparative case study

et al. 2012

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“…More realistically, multiple intervention strategies are employed that often overlap in time resulting in synergistic effects (e.g. vaccination, antiviral drug therapy, and social distancing measures) 13 . All of these will have significant impacts on epidemic outcomes.…”

Section: Discussionmentioning

confidence: 99%

The Impact of Demographic Variables on Disease Spread: Influenza in Remote Communities

Laskowski

Mostaço-Guidolin

Greer

et al. 2011

Sci Rep

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The role of demographic variables in disease spread in remote and isolated communities is poorly understood. We developed an agent-based model of a small indigenous community to qualitatively study the impact of pre-existing immunity in both young and elderly populations. We observed that in crowded living conditions, the age distribution of the population is a critical factor influencing epidemic spread. As the average age of the population increases, the effect of the pre-existing immunity in older individuals becomes more pronounced in decreasing disease incidence, even when pre-existing immunity levels in young individuals are low. However, in a non-crowded setting with relatively low average persons-per-household, the pre-existing immunity levels of young individuals remains a determining factor, regardless of the age distribution of the population. We suggest that for optimizing public health policies, social and demographic complexities of the remote and vulnerable communities should be carefully evaluated in modeling intervention strategies. In the event of an emerging disease, public health officials are tasked with identifying the geographic spread and time course of the outbreak, and identifying the most effective utilization of available health interventions and resources to mitigate disease outcomes. In addition to the natural history and biology of the disease, the demographic characteristics of the population at risk play an important role in determining the pattern of epidemic spread and identifying the type and intensity of public health intervention measures required for disease control 1,2,3 . Furthermore, the differential prevalence of predisposing health conditions and other types of health disparities increases uncertainty about how a novel disease would affect different populations with distinctly different mobility patterns, social interactions, and health characteristics 4 . The importance of these factors in influencing disease burden was highlighted during outbreaks of the 2009 H1N1 pandemic in several Canadian population settings, including First Nation reserves in northern Manitoba, remote and isolated communities in Nunavut, and Aboriginal communities on Vancouver Island 5,6,7 . Understanding the interplay between demographic, health, infection and control parameters requires the development of a modeling framework that can identify individuals with their assigned information, and describe disease spread in the population in silico (i.e., via computer simulations). Agent-based models, which specifically encapsulate individual-level characteristics, behaviors and population profiles, provide such a framework that is capable of reproducing observed scenarios in epidemics and exploring plausible contingency plans and control measures for curtailing an emerging disease 8,9 . Agent based modeling typically uses a bottom-up approach in which complex phenomena emerge from interactions between autonomous entities (i.e., agents) that perceive, make decisions, and act within an environment.He...

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“…It turned out to be more aggressive than the typical seasonal influenza and caused hospitalizations of children and adults as well as people with chronic conditions (World Health Organization 2010; Centers for Disease Control and Prevention 2010; Centers for Disease Control and Prevention 2012). Halder et al (2010) estimate that the H1N1 influenza had an attack rate between 27 and 37 % with an average of 32.5 %. Also, the influenza pandemic reached its peak within 45 days and caused an illness rate of 6.6 % of the total population.…”

Section: Introductionmentioning

confidence: 98%

A stochastic recovery model of influenza pandemic effects on interdependent workforce systems

Haimar

Santos

2015

Nat Hazards

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Outbreaks of infectious diseases, such as pandemics, can result in adverse consequences and major economic losses across various economic sectors. Based on findings from the 2009 A H1N1 pandemic in the National Capital Region (NCR), this paper presents a recovery analysis for workforce disruptions using economic input-output modeling. The model formulation takes into consideration the dynamic interdependencies across sectors in an economic system in addition to the inherent characteristics of the economic sectors. From a macroeconomic perspective, the risk of the influenza disaster can be modeled using two risk metrics. First, there is the level of inoperability, which represents the percentage difference between the ideal production level and the degraded production level. Second, the economic loss metric represents the financial value associated with the reduced output. The contribution of this work revolves around the modeling of uncertainties triggered by new perturbations to interdependent economic sectors within an influenza pandemic timeline. We model the level of inoperability of economic sectors throughout their recovery horizon from the initial outbreak of the disaster using a dynamic model. Moreover, we use the level of inoperability values to quantify the cumulative economic losses incurred by the sectors within the recovery horizon. Finally, we revisit the 2009 NCR pandemic scenario to demonstrate the use of uncertainty analysis in modeling the inoperability and economic loss behaviors due to time-varying perturbations and their associated ripple effects to interdependent economic sectors.

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Analysis of the effectiveness of interventions used during the 2009 A/H1N1 influenza pandemic

Cited by 79 publications

References 38 publications

Effect of short-term school closures on the H1N1 pandemic in Japan: a comparative case study

Effect of short-term school closures on the H1N1 pandemic in Japan: a comparative case study

The Impact of Demographic Variables on Disease Spread: Influenza in Remote Communities

A stochastic recovery model of influenza pandemic effects on interdependent workforce systems

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