Living with influenza: Impacts of government imposed and voluntarily selected interventions

Nigmatulina, Karima; Larson, Richard C.

doi:10.1016/j.ejor.2008.02.016

Cited by 47 publications

(33 citation statements)

References 27 publications

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“…Suggestions range from prioritizing those persons at risk (Chowell et al 2009, Patel et al 2005 to vaccinating those individuals that contribute most to virus spread (Longini et al 2005, Nigmatulina andLarson 2009). Although these nonspatial issues are important to consider, we focus on a new higher-level algorithm to allocate limited supplies of vaccines to various spatially disparate regions based on information about the progression of the flu wave in each of the respective regions.…”

Section: Introductionmentioning

confidence: 98%

Multiregional Dynamic Vaccine Allocation During an Influenza Epidemic

Teytelman

Larson

2013

Service Science

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W e consider a sequential decision problem in national health policy: the weekly deployments of limited influenza vaccine doses, as they become available, to the various geographical regions of the United States during a pandemic event.As with the 2009 H1N1 pandemic flu, we assume that the progression of flu infection varies from region to region, with some regions starting their flu waves weeks before others. We also assume that vaccine doses only become available after flu waves have already started in some regions. Whereas the traditional deployment of vaccines is in direct proportion to resident population, without regard to flu status in any region, we show that a new policy that dynamically considers the status of the various regional flu waves can dramatically reduce the incidence of flu infection over the entire country. The method uses mathematical models of flu spread and requires capturing real-time data on flu incidence.

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Section: Introductionmentioning

confidence: 98%

Multiregional Dynamic Vaccine Allocation During an Influenza Epidemic

Teytelman

Larson

2013

Service Science

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“…While it is almost impossible to verify their quantitative accuracy the principles are intuitive and qualitative results are insightful. In this section we will present one of our approaches to modeling behavior and its impact on transmission, for more of these models refer to Nigmatulina and Larson [52] or Nigmatulina [53].…”

Section: Modeling Behavior Changesmentioning

confidence: 99%

“…Nigmatulina and R.C. Larson in a paper titled "Living with Influenza: Impacts of government imposed and voluntarily selected interventions"[52] 5. Transmission of influenza occurs through respiratory emissions from sick individuals when talking, sneezing or coughing.…”

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confidence: 99%

Engineering responses to pandemics

Larson¹,

Nigmatulina²

2009

Information Knowledge Systems Management

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Focusing on pandemic influenza, this chapter approaches the planning for and response to such a major worldwide health event as a complex engineering systems problem. Action-oriented analysis of pandemics requires a broad inclusion of academic disciplines since no one domain can cover a significant fraction of the problem. Numerous research papers and action plans have treated pandemics as purely medical happenings, focusing on hospitals, health care professionals, creation and distribution of vaccines and anti-virals, etc. But human behavior with regard to hygiene and social distancing constitutes a first-order partial brake or control of the spread and intensity of infection. Such behavioral options are "non-pharmaceutical interventions." (NPIs) The chapter employs simple mathematical models to study alternative controls of infection, addressing a well-known parameter in epidemiology, R0, the "reproductive number," defined as the mean number of new infections generated by an index case. Values of R0 greater than 1.0 usually indicate that the infection begins with exponential growth, the generation-to-generation growth rate being R0. R0 is broken down into constituent parts related to the frequency and intensity of human contacts, both partially under our control. It is suggested that any numerical value for R0 has little meaning outside the social context to which it pertains. Difference equation models are then employed to study the effects of heterogeneity of population social contact rates, the analysis showing that the disease tends to be driven by high frequency individuals. Related analyses show the futility of trying geographically to isolate the disease. Finally, the models are operated under a variety of assumptions related to social distancing and changes in hygienic behavior. The results are promising in terms of potentially reducing the total impact of the pandemic.1389-1995/09/$17.00  2009 -IOS Press and the authors. All rights reserved 312 R.C. Larson and K.R. Nigmatulina / Engineering responses to pandemicsglobal economy [28]. It is for these reasons, and more -discussed below -that we select influenza pandemics as our focus in a chapter entitled, Engineering Responses to Pandemics.A common definition of "engineering' is as follows: "The application of scientific and mathematical principles to practical ends such as the design, manufacture, and operation of efficient and economical structures, machines, processes, and systems." 1 In engineering the response to a pandemic, we need to use "scientific and mathematical principles" to design processes and systems to mitigate the seriousness and consequences of the flu and to create a total system response to it.A standard engineering approach towards pandemic flu has been to tackle obvious more traditional engineering problems. These problems range from "optimizing" vaccine and anti-viral distribution and stockpiling strategies, to hospital surge capacity analysis, to developing solutions to supply chain 1 http://www.answers.com/topic/engineering Cit...

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“…Larson [59] has also modeled epidemic control for pandemic influenza. Nigmatulina and Larson [69] discuss government imposed and voluntarily selected control interventions. These interventions involve the implementation and timing of non-pharmaceutical strategies such as travel restrictions, social distancing, quarantine and improved hygiene.…”

Section: Epidemic Control Of Contagious and Other Population-wide Dismentioning

confidence: 99%

Operations research

Pierskalla¹

2009

Information Knowledge Systems Management

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Prologue:In Evita, Andrew Lloyd Webber and Tim Rice wrote: Politics, the Art of the Possible. To those of us in the operations research community, we postulate: Operations Research, the Science of Better -(i.e. better processes, better systems and better decisions). Using our own and other scientific, engineering, mathematical, and social sciences methodologies, operations researchers help decision makers make better decisions; decisions leading to improvements: greater quality, lower costs, greater revenues, better access, better scheduling, lower risks, more satisfaction -with the goal of always striving for the best or optimal decisions.1389-1995/09/$17.00  2009 -IOS Press and the authors. All rights reserved 242 W.P. Pierskalla / Operations research sectors, health-care sectors, and virtually every other sector in which strategic or operational decisions must be made to improve planning, performance and outcomes.Most operations research applications involve analyzing complex situations. The approach taken begins by looking at the complex situation from a systems perspective. Inside the system, processes are diagrammed and analyzed and a model is constructed based on the flows of people, products, services, technologies and information. Using this structure, the approach models the objectives desired to be achieved, the fixed and variable constraints on the system, all available options for decisions, evaluation of all possible outcomes, estimation of risks and bringing to bear the latest tools and techniques leading to improved or optimal decisions on system operation and outcomes. In most applications, it is frequently the case that multiple objectives such as to improve quality, lower costs, improve access and improve satisfaction are simultaneously achieved. In some cases, multiple objectives trade-off against one another and the decision maker has to weigh these objectives and choose among them. In this latter situation, operations research also provides decision analytic methodologies for optimally weighing and choosing among competing objectives.Because this book is about "Engineering the Health Care System", this chapter will be devoted to operations research applications in health care delivery with mentions of theory and methodologies as appropriate. We have space to cover only some of the applications from the thousands available but hope to do so with enough breadth of topic and depth of description to give a flavor for the capabilities of operations research in developing better or best solutions for many complex health care issues. If one wishes to have a brief introduction to the theory and methodologies mentioned in this chapter see Gass and Harris [35] and the website Wikipedia, http://en.wikipedia.org/wiki/Main Page.Operations research has been used in the development of theories and methodologies and in applications in many areas of health services delivery. Some of these areas are strategic in decision making and others are operational. Some examples are:1. Health care and health care syst...

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Living with influenza: Impacts of government imposed and voluntarily selected interventions

Cited by 47 publications

References 27 publications

Multiregional Dynamic Vaccine Allocation During an Influenza Epidemic

Multiregional Dynamic Vaccine Allocation During an Influenza Epidemic

Engineering responses to pandemics

Operations research

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