Modeling influenza progression within a continuous-attribute heterogeneous population

Teytelman, Anna; Larson, Richard C.

doi:10.1016/j.ejor.2012.01.027

Cited by 18 publications

(14 citation statements)

References 17 publications

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“…To formulate this problem precisely, we will use a discrete-time influenza spread model defined by Teytelman and Larson (2012), where we call the unit of time a "day." However, the allocation heuristics presented here may just as well be adapted to any influenza spread model.…”

Section: Problem Formulationmentioning

confidence: 99%

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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: Problem Formulationmentioning

confidence: 99%

“…Benefits derived from the shipped vaccines varied considerably from state to state, as discussed by Finkelstein et al (2011) and Teytelman and Larson (2012). States that received vaccines early with respect to their epidemic peak had a higher uptake rate in the population.…”

Section: Introductionmentioning

confidence: 96%

Multiregional Dynamic Vaccine Allocation During an Influenza Epidemic

Teytelman

Larson

2013

Service Science

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“…One may call the approach "models of statistical clones." Teytelman and Larson (2012) generalize that approach to eliminate the need for a finite number of groups-classes of statistically identical individuals, and instead, introduce a continuous distribution for all the key parameters in question, in essence employing an infinite number of classes. Their generalized model deals with all three attributes previously introduced: social activity, proneness to infection, and proneness to spread infection.…”

Section: The Reproductive Number Rmentioning

confidence: 98%

Engineering Effective Responses to Influenza Outbreaks

Finkelstein

Larson

Nigmatulina³

et al. 2015

Service Science

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W e present a policy-oriented summary of our six-year "service-systems-focused" research into pandemic influenza. We cover three topics: (1) R 0 , the basic reproductive number for the flu; (2) NPIs, non-pharmaceutical inventions to reduce the chance of becoming infected; and (3) flu vaccine allocations. We use a service-systems framing and mathematical modeling approach incorporating theories and data on the spread and control of influenza. We examine how behavioral actions and governmental policies, thoughtfully derived, can minimize influenza's societal impact. There is widespread misinterpretation that R 0 is a numerical constant of a given virus. We argue that it is not, but rather that its value is largely determined by local conditions and actions, many under our individual and collective control. This control is, in the absence of vaccine, intelligent use of NPIs-highly effective in reducing the spread of influenza. Our vaccine analysis relies on government data depicting flu-like cases and vaccines administered during the 2009 H1N1 outbreak. During that outbreak, barely half of all states received allotments of vaccine in time to protect any citizens. The method of vaccine deployment-in proportion to census population-ignored the temporally uneven flu wave progression across the United States.

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“…Examples of capability assessment strategies developed by the PERRCs included a toolkit to evaluate Medical Reserve Corps performance 34 and studies examining how to better use after-action reports [35][36][37] and exercises [38][39][40][41][42] for performance improvement. PERRC research pursued modeling of various PHP system phenomena, including mass evacuation, 43 the impact of different vaccine distribution strategies, 44,45 infectious disease progression in a population, 46,47 and effects of variation in mitigation strategies, such as the impact of different school closure approaches. 31,[45][46][47][48][49][50] Broad incorporation of modeling findings into policy and practice could reduce system performance variability by helping to standardize guidance for preparedness planning.…”

Section: Realization Of the Perrc Program Objectivesmentioning

confidence: 99%

“…PERRC research pursued modeling of various PHP system phenomena, including mass evacuation, 43 the impact of different vaccine distribution strategies, 44,45 infectious disease progression in a population, 46,47 and effects of variation in mitigation strategies, such as the impact of different school closure approaches. 31,[45][46][47][48][49][50] Broad incorporation of modeling findings into policy and practice could reduce system performance variability by helping to standardize guidance for preparedness planning.…”

Section: Realization Of the Perrc Program Objectivesmentioning

confidence: 99%

Preparedness and Emergency Response Research Centers: Using a Public Health Systems Approach to Improve All-Hazards Preparedness and Response

2014

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In 2008, at the request of the Centers for Disease Control and Prevention (CDC), the Institute of Medicine (IOM) prepared a report identifying knowledge gaps in public health systems preparedness and emergency response and recommending near-term priority research areas. In accordance with the Pandemic and All-Hazards Preparedness Act mandating new public health systems research for preparedness and emergency response, CDC provided competitive awards establishing nine Preparedness and Emergency Response Research Centers (PERRCs) in accredited U.S. schools of public health. The PERRCs conducted research in four IOM-recommended priority areas: (1) enhancing the usefulness of public health preparedness and response (PHPR) training, (2) creating and maintaining sustainable preparedness and response systems, (3) improving PHPR communications, and (4) identifying evaluation criteria and metrics to improve PHPR for all hazards. The PERRCs worked closely with state and local public health, community partners, and advisory committees to produce practice-relevant research findings. PERRC research has generated more than 130 peer-reviewed publications and nearly 80 practice and policy tools and recommendations with the potential to significantly enhance our nation's PHPR to all hazards and that highlight the need for further improvements in public health systems.

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Modeling influenza progression within a continuous-attribute heterogeneous population

Cited by 18 publications

References 17 publications

Multiregional Dynamic Vaccine Allocation During an Influenza Epidemic

Multiregional Dynamic Vaccine Allocation During an Influenza Epidemic

Engineering Effective Responses to Influenza Outbreaks

Preparedness and Emergency Response Research Centers: Using a Public Health Systems Approach to Improve All-Hazards Preparedness and Response

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