Optimal planning of the COVID-19 vaccine supply chain.
Ontology highlight
ABSTRACT:
SUBMITTER: Georgiadis GP
PROVIDER: S-EPMC8313510 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:In response to the COVID-19 pandemic, vaccine manufacturers quickly pivoted to develop vaccines, offering limited visibility into production timelines and quantities. Low- and middle-income countries developed vaccine introduction plans within the context of constrained global supply, unknown vaccine availability or timelines, and unknown demand. The Vaccine Collaborative Supply Planning (VCSP) Initiative was established to address the gaps in visibility of supply and demand of COVID-19 vaccines for improved supply planning. The Initiative drew from experiences with supply planning for other health program products. The Initiative's 2 goals for introducing COVID-19 vaccines were to: (1) incorporate the use of supply chain data for forecasting, supply planning, and resupply decisions; and (2) establish a collaborative model for governments and partners to learn and implement. Beginning in September 2021, the Initiative was operational in 15 countries through strategic partnerships with government immunization programs and 7 partnering organizations. Partners noted the Initiative offered several benefits: enabled visibility into stock status; provided ability to triangulate service delivery and stock data; provided insight into products at risk of expiry; facilitated tracking of monthly consumption patterns to inform decisions; offered ability to create forecasting scenarios; provided support to national logistics working groups; improved coordination at the country level; created trust through transparency across global partners; and offered opportunity for real-time adjustments through adaptive learning. Partners also noted challenges, including the influence of political decisions and the availability of quality data. The Initiative successfully applied best practices for supply planning, established a collaborative model with government decision-makers and partners, and used an adaptive learning approach to respond to the challenges of rolling out the COVID-19 vaccine. Lessons learned and recommendations suggest how this approach can be used to strengthen forecasting and supply planning for all vaccines as the COVID-19 vaccine is integrated into routine services.
| S-EPMC10948121 | biostudies-literature
Project description:Initial supply of the coronavirus disease (COVID-19) vaccine may be limited, necessitating its effective use. Herein, an age-structured model of COVID-19 spread in South Korea is parameterized to understand the epidemiological characteristics of COVID-19. The model determines optimal vaccine allocation for minimizing infections, deaths, and years of life lost while accounting for population factors, such as country-specific age distribution and contact structure, and various levels of vaccine efficacy. A transmission-blocking vaccine should be prioritized in adults aged 20-49 years and those older than 50 years to minimize the cumulative incidence and mortality, respectively. A strategy to minimize years of life lost involves the vaccination of adults aged 40-69 years, reflecting the relatively high case-fatality rates and years of life lost in this age group. An incidence-minimizing vaccination strategy is highly sensitive to vaccine efficacy, and vaccines with lower efficacy should be administered to teenagers and adults aged 50-59 years. Consideration of age-specific contact rates and vaccine efficacy is critical to optimize vaccine allocation. New recommendations for COVID-19 vaccines under consideration by the Korean Centers for Disease Control and Prevention are mainly based on a mortality-minimizing allocation strategy.
| S-EPMC7914460 | biostudies-literature
Project description:The global spread of the novel coronavirus, also known as the COVID-19 pandemic, has had a devastating impact on supply chains. Since the pandemic started, scholars have been researching and publishing their studies on the various supply-chain-related issues raised by COVID-19. However, while the number of articles on this subject has been steadily increasing, due to the absence of any systematic literature reviews, it remains unclear what aspects of this disruption have already been studied and what aspects still need to be investigated. The present study systematically reviews existing research on the COVID-19 pandemic in supply chain disciplines. Through a rigorous and systematic search, we identify 74 relevant articles published on or before 28 September 2020. The synthesis of the findings reveals that four broad themes recur in the published work: namely, impacts of the COVID-19 pandemic, resilience strategies for managing impacts and recovery, the role of technology in implementing resilience strategies, and supply chain sustainability in the light of the pandemic. Alongside the synthesis of the findings, this study describes the methodologies, context, and theories used in each piece of research. Our analysis reveals that there is a lack of empirically designed and theoretically grounded studies in this area; hence, the generalizability of the findings, thus far, is limited. Moreover, the analysis reveals that most studies have focused on supply chains for high-demand essential goods and healthcare products, while low-demand items and SMEs have been largely ignored. We also review the literature on prior epidemic outbreaks and other disruptions in supply chain disciplines. By considering the findings of these articles alongside research on the COVID-19 pandemic, this study offers research questions and directions for further investigation. These directions can guide scholars in designing and conducting impactful research in the field.
| S-EPMC7881707 | biostudies-literature
Project description:In the last two years, the coronavirus disease 19 (COVID-19) pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been a scientific and social challenge worldwide. Vaccines have been the most effective intervention for reducing virus transmission and disease severity. However, virus genetic variants are still circulating among vaccinated individuals with different symptomatology disease cases. Understanding the protective or disease associated mechanisms in vaccinated individuals is relevant to advance in vaccine development and implementation. To address this objective, serum protein profiles were characterized by quantitative proteomics and data analysis algorithms in four cohorts of vaccinated individuals uninfected and SARS-CoV-2 infected with asymptomatic, nonsevere and severe disease symptomatology. The results showed that immunoglobulins were the most overrepresented proteins in infected cohorts when compared to PCR-negative individuals. The immunoglobulin profile varied between different infected cohorts and correlated with protective or disease associated capacity. Overrepresented immunoglobulins in PCR-positive individuals correlated with protective response against SARS-CoV-2, other viruses, and thrombosis in asymptomatic cases. In nonsevere cases, correlates of protection against SARS-CoV-2 and HBV together with risk of myasthenia gravis and allergy and autoantibodies were observed. Patients with severe symptoms presented risk for allergy, chronic idiopathic thrombocytopenic purpura, and autoantibodies. The analysis of underrepresented immunoglobulins in PCR-positive compared to PCR-negative individuals identified vaccine-induced protective epitopes in various coronavirus proteins including the Spike receptor-binding domain RBD. Non-immunoglobulin proteins were associated with COVID-19 symptoms and biological processes. These results evidence host-associated differences in response to vaccination and the possibility of improving vaccine efficacy against SARS-CoV-2.
2022-10-14 | PXD031969 | Pride
Project description:The COVID-19 pandemic has revealed the fragility of global supply chains arising from raw material scarcity, production and transportation disruption, and social distancing. Firms need to carefully anticipate the difficulties during recovery and formulate appropriate strategies to ensure the survival of their businesses and supply chains. To enhance awareness of the issues, this research aims to identify and model recovery challenges in the context of the Bangladeshi ready-made garment industry. A Delphi-based grey decision-making trial and evaluation laboratory (DEMATEL) methodology was used to analyze the data. While the Delphi method helped identify the major supply chain recovery challenges from the impacts of the COVID-19 pandemic, the grey DEMATEL approach helped categorize the causal relationships among these challenges. Of the 23 recovery challenges finalized, 12 are causal challenges. The study's findings can assist decision-makers in developing strategic policies to overcome the recovery challenges in the post-COVID-19 era.
| S-EPMC8437773 | biostudies-literature
Project description:Supply chain management that considers the flow of raw materials, products and information has become a focal issue in modern manufacturing and service systems. Supply chain management requires effective use of assets and information that has far reaching implications beyond satisfaction of customer demand, flow of goods, services or capital. Aggregate planning, a fundamental decision model in supply chain management, refers to the determination of production, inventory, capacity and labor usage levels in the medium term. Traditionally standard mathematical programming formulation is used to devise the aggregate plan so as to minimize the total cost of operations. However, this formulation is purely an economic model that does not include sustainability considerations. In this study, we revise the standard aggregate planning formulation to account for additional environmental and social criteria to incorporate triple bottom line consideration of sustainability. We show how these additional criteria can be appended to traditional cost accounting in order to address sustainability in aggregate planning. We analyze the revised models and interpret the results on a case study from real life that would be insightful for decision makers.
| S-EPMC4725800 | biostudies-literature
Project description:The SARS-CoV-2 Delta (B.1.617.2) variant is capable of infecting vaccinated persons. An open question remains as to whether deficiencies in specific vaccine-elicited immune responses result in susceptibility to vaccine breakthrough infection. We investigated 55 vaccine breakthrough infection cases (mostly Delta) in Singapore, comparing them against 86 vaccinated close contacts who did not contract infection. Vaccine breakthrough cases showed lower memory B cell frequencies against SARS-CoV-2 receptor binding domain (RBD). Compared to plasma antibodies, antibodies secreted by memory B cells retained a higher fraction of neutralizing properties against the Delta variant. Inflammatory cytokines including IL-1β and TNF were lower in vaccine breakthrough infections than primary infection of similar disease severity, underscoring the usefulness of vaccination in preventing inflammation. This report highlights the importance of memory B cells against vaccine breakthrough, and suggests that lower memory B cell levels may be a correlate of risk for Delta vaccine breakthrough infection.
| S-SCDT-EMM-2021-15227 | biostudies-other
Project description:BackgroundThe COVID-19 pandemic has disrupted global health supply chains including manufacturing, storage, and delivery of essential medicines, testing kits, personal protective equipment, and laboratory reagents. We sought to document how pandemic impacted the procurement, prices, and supply chain of medical products in Zimbabwe.MethodsWe conducted semistructured in-depth key informant interviews with 36 health system stakeholders in Zimbabwe involved in medicine procurement. Respondents included pharmacists, regulatory officers, and procurement and supply chain management professionals from public and private sectors.ResultsBefore the COVID-19 pandemic, respondents described experiencing long-standing resource constraints, medicine shortages, foreign currency shortages, and supply chain inefficiencies. The pandemic exacerbated this situation due to supply constraints, export restrictions, medicine shortages, and movement restrictions that disrupted logistical and stock management systems. Competitive bidding and tendering processes experienced reduced participation by international suppliers. Significant price increases were initially observed among internationally shipped medicines and for personal protective equipment to cover additional freight costs. COVID-19 pandemic impacts were moderated by reduced patient demand and lower health services utilization, resulting in fewer supply shocks and less price volatility. Further, health system adaptations such as switching treatment regimens, modifying dispensing schedules based on stock availability, redistributing stock of medicines among facilities, and new service delivery models such as integrated outreach services helped ensure continued patient access to medicines.ConclusionsOur findings highlight the need for policies that ensure continuity in access to health services and medical products, even during a pandemic, by avoiding blanket restrictions on medical product exports and imports. Pooled procurement, especially at regional and global levels, with long-term service agreements may help achieve greater resiliency to supply and price shocks from supply chain disruptions. Interventions across manufacturing, trade, and regulatory policy and service delivery models are also needed for supply chain resiliency.
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Project description:This study presents a new risk-averse multi-stage stochastic epidemics-ventilator-logistics compartmental model to address the resource allocation challenges of mitigating COVID-19. This epidemiological logistics model involves the uncertainty of untested asymptomatic infections and incorporates short-term human migration. Disease transmission is also forecasted through a new formulation of transmission rates that evolve over space and time with respect to various non-pharmaceutical interventions, such as wearing masks, social distancing, and lockdown. The proposed multi-stage stochastic model overviews different scenarios on the number of asymptomatic individuals while optimizing the distribution of resources, such as ventilators, to minimize the total expected number of newly infected and deceased people. The Conditional Value at Risk (CVaR) is also incorporated into the multi-stage mean-risk model to allow for a trade-off between the weighted expected loss due to the outbreak and the expected risks associated with experiencing disastrous pandemic scenarios. We apply our multi-stage mean-risk epidemics-ventilator-logistics model to the case of controlling COVID-19 in highly-impacted counties of New York and New Jersey. We calibrate, validate, and test our model using actual infection, population, and migration data. We also define a new region-based sub-problem and bounds on the problem and then show their computational benefits in terms of the optimality and relaxation gaps. The computational results indicate that short-term migration influences the transmission of the disease significantly. The optimal number of ventilators allocated to each region depends on various factors, including the number of initial infections, disease transmission rates, initial ICU capacity, the population of a geographical location, and the availability of ventilator supply. Our data-driven modeling framework can be adapted to study the disease transmission dynamics and logistics of other similar epidemics and pandemics.
| S-EPMC8632406 | biostudies-literature
Project description:The impacts of COVID-19 on labor in the food supply chain and on workers' decisions to accept essential jobs are discussed. We then analyze surveys administered to low-skilled domestic workers before and during the pandemic to assess respondents' attitudes toward food production, guest workers, immigration policy, and the government's response to COVID-19. Results suggest the outbreak resulted in respondents, on average, shifting their view toward food being a national security issue and a higher degree of empathy for H-2A workers. Regression analysis shows that gender, current agricultural workers, and information on COVID-19 and agricultural field workers influenced respondents' answers.
| S-EPMC7646642 | biostudies-literature