Optimizing Tactics for use of the U.S. Antiviral Strategic National Stockpile for Pandemic (H1N1) Influenza, 2009

Dimitrov, Nedialko B.; Goll, Sebastian; Hupert, Nathaniel; Pourbohloul, Babak; Meyers, Lauren Ancel

doi:10.1371/currents.rrn1127

Cited by 15 publications

(21 citation statements)

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“…In this case, the nodes are cities and the edges reflect travel patterns between cities via air and ground transportation, as reported by the U.S. Census Bureau, the U.S. Bureau of Transportation Statistics, Instituto Nacional de Estadística y Geografía, and Statistics Canada. In this case, the edges of our network are weighted by travel flux and diseases spread within cities via simple compartmental models (Dimitrov et al [17]) (see Figures 5(d) and 9(a)). …”

Section: Building a Contact Network Modelmentioning

confidence: 99%

“…In this section, we present one recent approach to searching large sets of infectious disease intervention policies (Dimitrov et al [17]). As we describe here, the method is parallelizable, scalable, usable in real time, and can be adapted to work with diverse epidemic models.…”

Section: Optimizing Disease Control Policiesmentioning

confidence: 99%

“…To computationally address the above optimization problem, we have designed and implemented the Disease Control System (DiCon) (Dimitrov et al [17], Goll [25]). DiCon is a modular and extensible optimization platform specialized to disease control with the following features:…”

Section: Optimizing Disease Control Policiesmentioning

confidence: 99%

“…The same performance not necessarily occur for other strains of influenza with different characteristics. A preliminary version of DiCon has been used to compute release schedules for the U.S. National Antiviral Stockpile with the purposes of delaying an influenza epidemic (Dimitrov et al [17]). In that application, the sequence of actions A 1 , A 2 , . .…”

Section: Optimizing Disease Control Policiesmentioning

confidence: 99%

“…Some of the key results of the optimization are summarized in Figure 9. For more details, see Dimitrov et al [17] Tutorials in Operations Research, c 2010 INFORMS…”

Section: Optimizing Disease Control Policiesmentioning

confidence: 99%

See 4 more Smart Citations

Mathematical Approaches to Infectious Disease Prediction and Control

Meyers¹,

Dimitrov²

2010

Risk and Optimization in an Uncertain World

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Mathematics has long been an important tool for understanding and controlling the spread of infectious diseases. Here, we begin with an overview of compartmental models, the traditional approach to modeling infectious disease dynamics, and then introduce contact network epidemiology, a relatively new approach that applies bond percolation on random graphs to model the spread of infectious disease through heterogeneous populations. As we illustrate, these methods can be used to address public health challenges and have recently been coupled with powerful computational methods to optimize epidemic control strategies.

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Section: Building a Contact Network Modelmentioning

confidence: 99%

Section: Optimizing Disease Control Policiesmentioning

confidence: 99%

Section: Optimizing Disease Control Policiesmentioning

confidence: 99%

Section: Optimizing Disease Control Policiesmentioning

confidence: 99%

“…Some of the key results of the optimization are summarized in Figure 9. For more details, see Dimitrov et al [17] Tutorials in Operations Research, c 2010 INFORMS…”

Section: Optimizing Disease Control Policiesmentioning

confidence: 99%

See 3 more Smart Citations

Mathematical Approaches to Infectious Disease Prediction and Control

Meyers¹,

Dimitrov²

2010

Risk and Optimization in an Uncertain World

Self Cite

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Identifying cost‐effective dynamic policies to control epidemics

Yaesoubi

Cohen

2016

Statistics in Medicine

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We describe a mathematical decision model for identifying dynamic health policies for controlling epidemics. These dynamic policies aim to select the best current intervention based on accumulating epidemic data and the availability of resources at each decision point. We propose an algorithm to approximate dynamic policies that optimize the population’s net health benefit, a performance measure which accounts for both health and monetary outcomes. We further illustrate how dynamic policies can be defined and optimized for the control of a novel viral pathogen, where a policy maker must decide (i) when to employ or lift a transmission-reducing intervention (e.g. school closure) and (ii) how to prioritize population members for vaccination when a limited quantity of vaccines first become available. Within the context of this application, we demonstrate that dynamic policies can produce higher net health benefit than more commonly described static policies that specify a pre-determined sequence of interventions to employ throughout epidemics.

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Optimization methods for large-scale vaccine supply chains: a rapid review

et al. 2022

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Global vaccine revenues are projected at $59.2 billion, yet large-scale vaccine distribution remains challenging for many diseases in countries around the world. Poor management of the vaccine supply chain can lead to a disease outbreak, or at worst, a pandemic. Fortunately, a large number of those challenges, such as decision-making for optimal allocation of resources, vaccination strategy, inventory management, among others, can be improved through optimization approaches. This work aims to understand how optimization has been applied to vaccine supply chain and logistics. To achieve this, we conducted a rapid review and searched for peer-reviewed journal articles, published between 2009 and March 2020, in four scientific databases. The search resulted in 345 articles, of which 25 unique studies met our inclusion criteria. Our analysis focused on the identification of article characteristics such as research objectives, vaccine supply chain stage addressed, the optimization method used, whether outbreak scenarios were considered, among others. Approximately 64% of the studies dealt with vaccination strategy, and the remainder dealt with logistics and inventory management. Only one addressed market competition (4%). There were 14 different types of optimization methods used, but control theory, linear programming, mathematical model and mixed integer programming were the most common (12% each). Uncertainties were considered in the models of 44% of the studies. One resulting observation was the lack of studies using optimization for vaccine inventory management and logistics. The results provide an understanding of how optimization models have been used to address challenges in large-scale vaccine supply chains.

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Optimizing Tactics for use of the U.S. Antiviral Strategic National Stockpile for Pandemic (H1N1) Influenza, 2009

Cited by 15 publications

References 0 publications

Mathematical Approaches to Infectious Disease Prediction and Control

Mathematical Approaches to Infectious Disease Prediction and Control

Identifying cost‐effective dynamic policies to control epidemics

Optimization methods for large-scale vaccine supply chains: a rapid review

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