Supply chain planning of vaccine and pharmaceutical clusters under uncertainty: The case of COVID-19

Kochakkashani, Farid; Kayvanfar, Vahid; Haji, Alireza

doi:10.1016/j.seps.2023.101602

Cited by 24 publications

(5 citation statements)

References 51 publications

(50 reference statements)

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“…It has been applied to a wide range of healthcare problems, including modeling control strategies and disease dynamics for COVID-19 [ 49 , 50 , 51 ], influenza [ 52 , 53 ], polio [ 54 ], and Ebola [ 55 , 56 , 57 ], among other infectious disease outbreaks. Several studies have used SD to consider the impact of polio outbreaks following eradication on stockpile needs [ 58 , 59 ], while others have used optimization-based methods to inform supply planning of COVID-19 [ 60 , 61 ] and monkeypox [ 62 ] vaccines under demand uncertainty. However, models focused on production and distribution networks may not adequately capture the underlying drivers that give rise to dynamic behavior.…”

Section: Methodsmentioning

confidence: 99%

Modeling Supply and Demand Dynamics of Vaccines against Epidemic-Prone Pathogens: Case Study of Ebola Virus Disease

Guttieres,

Diepvens,

Decouttere

et al. 2023

Vaccines

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Health emergencies caused by epidemic-prone pathogens (EPPs) have increased exponentially in recent decades. Although vaccines have proven beneficial, they are unavailable for many pathogens. Furthermore, achieving timely and equitable access to vaccines against EPPs is not trivial. It requires decision-makers to capture numerous interrelated factors across temporal and spatial scales, with significant uncertainties, variability, delays, and feedback loops that give rise to dynamic and unexpected behavior. Therefore, despite progress in filling R&D gaps, the path to licensure and the long-term viability of vaccines against EPPs continues to be unclear. This paper presents a quantitative system dynamics modeling framework to evaluate the long-term sustainability of vaccine supply under different vaccination strategies. Data from both literature and 50 expert interviews are used to model the supply and demand of a prototypical Ebolavirus Zaire (EBOV) vaccine. Specifically, the case study evaluates dynamics associated with proactive vaccination ahead of an outbreak of similar magnitude as the 2018–2020 epidemic in North Kivu, Democratic Republic of the Congo. The scenarios presented demonstrate how uncertainties (e.g., duration of vaccine-induced protection) and design criteria (e.g., priority geographies and groups, target coverage, frequency of boosters) lead to important tradeoffs across policy aims, public health outcomes, and feasibility (e.g., technical, operational, financial). With sufficient context and data, the framework provides a foundation to apply the model to a broad range of additional geographies and priority pathogens. Furthermore, the ability to identify leverage points for long-term preparedness offers directions for further research.

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Section: Methodsmentioning

confidence: 99%

Modeling Supply and Demand Dynamics of Vaccines against Epidemic-Prone Pathogens: Case Study of Ebola Virus Disease

Guttieres,

Diepvens,

Decouttere

et al. 2023

Vaccines

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“…In this architecture, the energy consumption for write operations is determined by multiplying the number of writes with the energy required for a single write, considering ∆=40.3. The formula representing write energy is provided as (4), where all requests (100%) are directed towards the non-volatile section. The use of percentages enables the normalization of energy consumption.…”

Section: B Optimum Energy -Baseline -First Approachmentioning

confidence: 99%

“…On the other hand, the device also generates data that is recorded and used for research and prevention strategies. This data needs to be stored for a longer time [4]. To ensure it stays preserved, the device uses a memory that can keep data even when power is lost, like Flash memory.…”

mentioning

confidence: 99%

Multi-Retention STT-MRAM Architectures for IoT: Evaluating the Impact of Retention Levels and Memory Mapping Schemes

Jahannia,

Ghasemi,

Farbeh

2024

IEEE Access

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In recent years, the energy consumption of IoT edge nodes has significantly increased due to the communication process. This necessitates the need to offload more computation to the edge nodes to minimize data transmission over the network. To achieve this, higher-performance CPUs and memory are required on the edge nodes. In this context, we propose an energy-efficient memory architecture specifically designed for edge nodes. STT-MRAM is a promising memory technology that offers potential replacements for SRAM and Flash in IoT devices. STT-MRAM exhibits notable advantages over traditional memory technologies, such as non-volatility for data retention without continuous power supply and energy efficiency, resulting in extended battery life for portable devices and IoT applications. Its potential for higher memory density and scalability through standard fabrication processes further enhances its appeal for next-generation memory solutions. However, the high write energy consumption is its main disadvantage. Previous works have explored non-volatility relaxation in CPU cache but there is a need to extend this approach to main memory in IoT devices. In this paper, we propose a multi-retention STT-MRAM architecture for IoT main memory. Additionally, we propose a memory mapping scheme for the suggested memory architecture and examine the impact of more relaxed retention levels on energy consumption. To the best of our knowledge, this is the first study to thoroughly investigate the optimal thermal stability factor value for STT-MRAM in IoT applications while also considering optimal memory mapping. The proposed architecture reduces energy consumption by an average of 70% and up to 83% compared to the currently used non-volatile STT-MRAM architecture. Furthermore, we propose two memory mappings that are easy to use and achieve an average energy savings that is just 5% away from the ideal mapping.

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“…Hospital supply chain faces increasing vulnerability due to a range of disruptive events, including sudden shortages of raw materials, production halts, and unforeseen public health crises ( 1 , 2 ). These disruptions not only jeopardize the stability and security of the supply chain to provide the medication and equipment used by patients, but also pose significant risks to the quality of healthcare services and patient safety ( 3 ).…”

Section: Introductionmentioning

confidence: 99%

“…Distinguished by the specialized demands of the healthcare industry, hospital supply chains must be prepared for extraordinary events such as pandemics, chemical/biological threats, and widespread illness outbreaks ( 10 ). The complexities of hospital supply chains, the movement of highly valuable commodities, and the overarching imperative to protect human lives, justify tolerating certain inefficiencies, such as maintaining higher-than-usual stock levels, which would be deemed unacceptable in other contexts, particularly in non-profitable public hospitals ( 2 , 11 ). Given the distinct challenges and the pivotal role of hospital supply chains in disaster response and routine healthcare delivery, there is an urgent need for a resilience assessment framework specifically designed for healthcare institutions.…”

Section: Introductionmentioning

confidence: 99%

Confirmation of a measurement model for hospital supply chain resilience

Ding,

Yang,

Gao

et al. 2024

Front. Public Health

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BackgroundThe hospital supply chain has revealed increasing vulnerabilities and disruptions in the wake of the COVID-19 pandemic, threatening the healthcare services and patient safety. The resilience of hospital supply chains has emerged as a paramount concern within the healthcare system. However, there is a lack of systematic research to develop an instrument tailored to the healthcare industry that is both valid and reliable for measuring hospital supply chain resilience. Therefore, this study aims to construct and validate a comprehensive scale for assessing hospital supply chain resilience, based on dynamic capability theory.MethodsThis study followed rigorous scale development steps, starting with a literature review and 15 semi-structured interviews to generate initial items. These items were then refined through expert panel feedback and three rounds of Delphi studies. Using data from 387 hospitals in Province S, mainland China, the scale underwent rigorous testing and validation using structural equation modeling. To ensure the most effective model, five alternative models were examined to determine the most suitable parsimonious model.ResultsThe study produced a 26-item scale that captures five dimensions of resilience in line with dynamic capability theory: anticipation, adaptation, response, recovery, and learning, all showing satisfactory consistency, reliability and validity.ConclusionThe multi-dimensional scale offers hospital managers a valuable tool to identify areas needing attention and improvement, benchmark resilience against their counterparts, and ultimately strengthen their supply chains against unexpected risks.

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Supply chain planning of vaccine and pharmaceutical clusters under uncertainty: The case of COVID-19

Cited by 24 publications

References 51 publications

Modeling Supply and Demand Dynamics of Vaccines against Epidemic-Prone Pathogens: Case Study of Ebola Virus Disease

Modeling Supply and Demand Dynamics of Vaccines against Epidemic-Prone Pathogens: Case Study of Ebola Virus Disease

Multi-Retention STT-MRAM Architectures for IoT: Evaluating the Impact of Retention Levels and Memory Mapping Schemes

Confirmation of a measurement model for hospital supply chain resilience

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