The Viability of Supply Chains with Interpretable Learning Systems: The Case of COVID-19 Vaccine Deliveries

Zaoui, Samia; Foguem, Clovis; Tchuente, Dieudonné; Fosso-Wamba, Samuel; Kamsu-Foguem, Bernard

doi:10.1007/s40171-023-00357-w

Cited by 17 publications

(2 citation statements)

References 72 publications

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“…By applying regression models and time series analysis, the researchers accurately predicted vaccine demand for various antigens, enabling proactive supply chain management and inventory optimization. The implementation of ML-based demand forecasting resulted in reduced stockouts, minimized wastage, and improved vaccine coverage in the target population (Zaoui et al, 2023). The ML-based demand forecasting approach led to a significant improvement in vaccine distribution efficiency, with a reduction in stockouts and wastage rates.…”

Section: Case Studiesmentioning

confidence: 99%

Leveraging machine learning for vaccine distribution in resource-limited settings: A synthesis of approaches

Charles Chukwudalu Ebulue,

Ogochukwu Virginia Ekkeh,

Ogochukwu Roseline Ebulue

et al. 2024

imsrj

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Vaccine distribution in resource-limited settings remains a crucial global health challenge, exacerbated by factors such as inadequate infrastructure, limited resources, and complex supply chains. Leveraging machine learning (ML) holds promise for optimizing distribution efficiency and ensuring equitable access to life-saving vaccines. This paper synthesizes various ML approaches aimed at addressing vaccine distribution challenges in resource-constrained environments. The literature review examines existing research on ML applications in healthcare and vaccine distribution, highlighting key findings and methodologies. Methodologically, criteria were established for selecting relevant studies, with a focus on ML techniques and their effectiveness in resource-limited contexts. Key ML approaches identified include predictive analytics for demand forecasting, route optimization algorithms for efficient vaccine delivery, and decision support systems for prioritizing distribution efforts. Case studies illustrate successful ML implementations in real-world settings, showcasing improved vaccine coverage and reduced wastage. Despite promising results, challenges persist, including data scarcity, model generalization, and ethical considerations. Future research directions include enhancing data collection methods, refining ML algorithms for specific contexts, and integrating ML solutions into existing healthcare systems. In conclusion, this synthesis underscores the transformative potential of ML in revolutionizing vaccine distribution in resource-limited settings. By addressing logistical barriers and optimizing resource allocation, ML-driven approaches offer a pathway towards achieving universal immunization coverage and mitigating the impact of infectious diseases on vulnerable populations. Keywords: Machine Learning, Vaccine Distribution, Resource-Limited Settings, Synthesis of Approaches.

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Section: Case Studiesmentioning

confidence: 99%

Leveraging machine learning for vaccine distribution in resource-limited settings: A synthesis of approaches

Charles Chukwudalu Ebulue,

Ogochukwu Virginia Ekkeh,

Ogochukwu Roseline Ebulue

et al. 2024

imsrj

View full text Add to dashboard Cite

show abstract

“…By analyzing real-time data on vaccine uptake, geographical distribution, and demographic information, authorities can identify areas with higher demand, allocate resources strategically, and adapt distribution plans as needed. This datadriven approach enhances the agility of the distribution system, ensuring that vaccines are deployed where they are most needed (Zaoui et al, 2023).…”

Section: Vaccine Distribution In Africamentioning

confidence: 99%

COVID-19 vaccine distribution: A review of strategies in Africa and the USA

Jeremiah Olawumi Arowoogun,

Jane Osareme Ogugua,

Ifeoma Pamela Odilibe

et al. 2024

World J. Adv. Res. Rev.

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Amid the ongoing global efforts to combat the COVID-19 pandemic, equitable vaccine distribution has emerged as a critical challenge. This review explores the contrasting strategies employed in Africa and the USA to distribute COVID-19 vaccines, shedding light on the unique challenges and opportunities faced by each region. In Africa, where healthcare infrastructure and resources vary widely, the distribution strategy involves collaborations with international organizations, leveraging existing vaccination programs, and addressing logistical hurdles. The review emphasizes the importance of tailored approaches to suit the diverse contexts within the African continent. In contrast, the USA's vaccine distribution strategy reflects a mix of federal and state-level initiatives, with a focus on mass vaccination sites, community clinics, and partnerships with private entities. The review delves into the challenges faced by the USA, including supply chain issues and disparities in vaccine access, and examines the innovative measures adopted to overcome these hurdles. Additionally, it analyzes the role of technology and data-driven approaches in optimizing vaccine distribution efficiency in both regions. The comparative analysis highlights the significance of considering socio-economic, geographical, and infrastructural factors in crafting effective vaccine distribution strategies. By exploring the successes and shortcomings in Africa and the USA, this review aims to contribute valuable insights to the ongoing global discourse on optimizing COVID-19 vaccine distribution, fostering collaboration, and ensuring equitable access to vaccines across diverse healthcare landscapes.

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Convergence innovation for sustainable development: Unraveling post‐COVID dynamics for a resilient future

Ali

2023

Sustainable Development

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This paper explores the potential of Convergence Innovation (CI) as an effective catalyst for dealing with the COVID‐19 pandemic and devising a trajectory toward a sustainable post‐crisis future. It offers novel perspectives on sustainable innovation approaches that can enhance organizational performance and extend beyond it. The paper makes significant contributions to both the theoretical and practical aspects of innovation. It illustrates the impact of market forces in the context of the Fourth Industrial Revolution (4IR) and Digital Technologies (DT), highlighting the significance of sustainable innovation for organizational prosperity and individual welfare. Convergence Innovation is explained as a self‐regulating ecosystem that promotes and goes beyond value creation. Different technologies in the ecosystem enhance CI's sensing capabilities for fluctuations in market forces and facilitate the transmission of data for decision‐making. The study recommends splitting off the S‐curve of innovation and the life cycle of competitive intelligence, as the S‐curve of CI tends to be shorter in the digital age. This paper provides managers with a new framework for thinking about innovation and its role in achieving sustainable business success. By embracing CI, managers can create more agile and adaptable organizations that are better positioned to navigate the complex and rapidly changing world of the digital age.

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The Viability of Supply Chains with Interpretable Learning Systems: The Case of COVID-19 Vaccine Deliveries

Cited by 17 publications

References 72 publications

Leveraging machine learning for vaccine distribution in resource-limited settings: A synthesis of approaches

Leveraging machine learning for vaccine distribution in resource-limited settings: A synthesis of approaches

COVID-19 vaccine distribution: A review of strategies in Africa and the USA

Convergence innovation for sustainable development: Unraveling post‐COVID dynamics for a resilient future

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