Simulation of influenza propagation: Model development, parameter estimation, and mitigation strategies

Andradóttir, Sigrún; Chiu, Wenchi; Goldsman, David; Lee, Mi Lim

doi:10.1080/19488300.2014.880093

Cited by 7 publications

(6 citation statements)

References 99 publications

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“…Second, we did not assess the quality of the modeling studies. Input parameters used in simulation models include the population characteristics that describe exposure points (e.g., households, schools, workplaces); the population’s behaviors that represent exposure frequencies (e.g., contact rates and durations); and disease transmission parameters [ 15 , 48 ]. There are few empirical studies on contact rates at workplaces [ 30 ].…”

Section: Discussionmentioning

confidence: 99%

“…A reproduction number greater than 1 indicates that the infection will grow in the population, whereas a value less than 1 indicates that the infection will decline [ 14 ]. Higher R 0 values are associated with higher cumulative attack rates [ 15 ]. Factors that affect R 0 include the population contact rate, the probability of infection per contact, and the duration of illness.…”

Section: Methodsmentioning

confidence: 99%

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Effectiveness of workplace social distancing measures in reducing influenza transmission: a systematic review

2018

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BackgroundSocial distancing is one of the community mitigation measures that may be recommended during influenza pandemics. Social distancing can reduce virus transmission by increasing physical distance or reducing frequency of congregation in socially dense community settings, such as schools or workplaces. We conducted a systematic review to assess the evidence that social distancing in non-healthcare workplaces reduces or slows influenza transmission.MethodsElectronic searches were conducted using MEDLINE, Embase, Scopus, Cochrane Library, PsycINFO, CINAHL, NIOSHTIC-2, and EconLit to identify studies published in English from January 1, 2000, through May 3, 2017. Data extraction was done by two reviewers independently. A narrative synthesis was performed.ResultsFifteen studies, representing 12 modeling and three epidemiological, met the eligibility criteria. The epidemiological studies showed that social distancing was associated with a reduction in influenza-like illness and seroconversion to 2009 influenza A (H1N1). However, the overall risk of bias in the epidemiological studies was serious. The modeling studies estimated that workplace social distancing measures alone produced a median reduction of 23% in the cumulative influenza attack rate in the general population. It also delayed and reduced the peak influenza attack rate. The reduction in the cumulative attack rate was more pronounced when workplace social distancing was combined with other nonpharmaceutical or pharmaceutical interventions. However, the effectiveness was estimated to decline with higher basic reproduction number values, delayed triggering of workplace social distancing, or lower compliance.ConclusionsModeling studies support social distancing in non-healthcare workplaces, but there is a paucity of well-designed epidemiological studies.Systematic review registration numberPROSPERO registration # CRD42017065310.Electronic supplementary materialThe online version of this article (10.1186/s12889-018-5446-1) contains supplementary material, which is available to authorized users.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Effectiveness of workplace social distancing measures in reducing influenza transmission: a systematic review

2018

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“…In terms of potential drawback (i), it has come to pass that the use of simulation to model pandemic disease spread is now widely accepted, even if some of the modeling elements are sometimes regarded as ad hoc. To address this latter issue, there has been a great deal of work in the literature that incorporates sophisticated population modeling techniques-including population mixing and movement of individuals from location to location-along with a variety of mitigation strategies; see Andradóttir et al [11] for additional motivation and references. With respect to (ii), one simply simulates any particular scenario over many independent replications and then conducts what can be regarded as standard, run-of-the-mill data analysis on the independent outputs.…”

Section: Introductionmentioning

confidence: 99%

Green Simulation of Pandemic Disease Propagation

2019

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This paper is concerned with the efficient stochastic simulation of multiple scenarios of an infectious disease as it propagates through a population. In particular, we propose a simple “green” method to speed up the simulation of disease transmission as we vary the probability of infection of the disease from scenario to scenario. After running a baseline scenario, we incrementally increase the probability of infection, and use the common random numbers variance reduction technique to avoid re-simulating certain events in the new scenario that would not otherwise have changed from the previous scenario. A set of Monte Carlo experiments illustrates the effectiveness of the procedure. We also propose various extensions of the method, including its use to estimate the sensitivity of propagation characteristics in response to small changes in the infection probability.

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“…In the literature, compartmental models [ 9 – 16 ] and agent-based simulations [ 17 – 22 ] are frequently employed to make mitigation plans for influenza pandemics and evaluate the effectiveness of various public health interventions [ 23 – 25 ]. Although it is known that the infection propagation is different in these two approaches [ 26 ], a detailed comparison of the strategies derived by using them is often omitted.…”

Section: Introductionmentioning

confidence: 99%

Deriving effective vaccine allocation strategies for pandemic influenza: Comparison of an agent-based simulation and a compartmental model

et al. 2017

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Individuals are prioritized based on their risk profiles when allocating limited vaccine stocks during an influenza pandemic. Computationally expensive but realistic agent-based simulations and fast but stylized compartmental models are typically used to derive effective vaccine allocation strategies. A detailed comparison of these two approaches, however, is often omitted. We derive age-specific vaccine allocation strategies to mitigate a pandemic influenza outbreak in Seattle by applying derivative-free optimization to an agent-based simulation and also to a compartmental model. We compare the strategies derived by these two approaches under various infection aggressiveness and vaccine coverage scenarios. We observe that both approaches primarily vaccinate school children, however they may allocate the remaining vaccines in different ways. The vaccine allocation strategies derived by using the agent-based simulation are associated with up to 70% decrease in total cost and 34% reduction in the number of infections compared to the strategies derived by using the compartmental model. Nevertheless, the latter approach may still be competitive for very low and/or very high infection aggressiveness. Our results provide insights about potential differences between the vaccine allocation strategies derived by using agent-based simulations and those derived by using compartmental models.

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Simulation of influenza propagation: Model development, parameter estimation, and mitigation strategies

Cited by 7 publications

References 99 publications

Effectiveness of workplace social distancing measures in reducing influenza transmission: a systematic review

Effectiveness of workplace social distancing measures in reducing influenza transmission: a systematic review

Green Simulation of Pandemic Disease Propagation

Deriving effective vaccine allocation strategies for pandemic influenza: Comparison of an agent-based simulation and a compartmental model

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