Identification of Optimum COVID-19 Vaccine Distribution Strategy Under Integrated Pythagorean Fuzzy Environment

Abstract Ensuring a more equitable distribution of vaccines worldwide is an effective strategy to control the COVID-19 pandemic and support global economic recovery. Here, we analyze the socioeconomic effects - defined as health gains, lockdown-easing benefit, and supply-chain rebuilding benefit - of a set of idealized vaccine distribution scenarios, by coupling an epidemiological model with a global trade-modeling framework. We find that overall a perfectly equitable vaccine distribution across the world (Altruistic Age-informed Distribution Strategy) would increase global economic benefits by 11.7% ($950 billion) per year, compared to a strategy focusing on vaccinating the entire population within vaccine-producing countries first and then distributing vaccines to non-vaccine-producing countries (Selfish Distribution Strategy). With limited doses among mid- and low-income countries, prioritizing the elderly who are at high risk of dying, together with the key workforce who are at high risk of exposure, is found to be economically beneficial. We further show that such a strategy would cascade the protection to other production sectors while rebuilding the supply chains. Our results point to a benefit-sharing mechanism which highlights the potential of collaboration between vaccine-producing and other countries to guide an economically preferable vaccine distribution worldwide.

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Generalizable multi-vaccine distribution strategy based on demographic and behavioral heterogeneity

10.1109/bibm52615.2021.9669682 ◽

2021 ◽

Author(s):

Satyaki Roy ◽

Pratyay Dutta ◽

Preetam Ghosh

Keyword(s):

Distribution Strategy ◽

Behavioral Heterogeneity ◽

Vaccine Distribution

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A feasible and more efficient SARS-Cov-2 vaccine allocation to states and counties in the USA

10.1101/2021.03.17.21253793 ◽

2021 ◽

Author(s):

Anthony R. Ives ◽

Claudio Bozzuto

Keyword(s):

Health Care ◽

Current Distribution ◽

Herd Immunity ◽

The Elderly ◽

Economic Consequences ◽

Health Care Personnel ◽

Distribution Strategy ◽

The Usa ◽

Vaccine Distribution

While discussion of vaccine allocation has centered around who should be prioritized (e.g., health care personnel and the elderly), we argue that vaccines should also be allocated to jurisdictions (e.g., counties within the USA) with the greatest immunization thresholds needed for ending the epidemic. At the current rate of vaccine distribution (March 15, 2021), universal herd immunity in the USA could be reached in roughly 4.5 months. However, distributing vaccines according to where the virus spreads more easily (dense counties with high R0 values), herd immunity would be reached simultaneously in all counties almost two months earlier and would require roughly 40% fewer vaccine doses. Furthermore, under the current distribution strategy densely populated counties would reach herd immunity last, with negative epidemiological and socio-economic consequences. In sum, it would be more fair and efficient to distribute vaccines to jurisdictions that need them most to reach herd immunity.

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Strategic spatiotemporal vaccine distribution increases the survival rate in an infectious disease like Covid-19

Scientific Reports ◽

10.1038/s41598-020-78447-3 ◽

2020 ◽

Vol 10 (1) ◽

Cited By ~ 1

Author(s):

Jens Grauer ◽

Hartmut Löwen ◽

Benno Liebchen

Keyword(s):

Survival Rate ◽

Risk Groups ◽

Time Frame ◽

Spatiotemporal Distribution ◽

Epidemic Disease ◽

Distribution Strategy ◽

Distribution Rules ◽

Reproduction Numbers ◽

Social Mixing ◽

Vaccine Distribution

AbstractPresent hopes to conquer the Covid-19 epidemic are largely based on the expectation of a rapid availability of vaccines. However, once vaccine production starts, it will probably take time before there is enough vaccine for everyone, evoking the question how to distribute it best. While present vaccination guidelines largely focus on individual-based factors, i.e. on the question to whom vaccines should be provided first, e.g. to risk groups or to individuals with a strong social-mixing tendency, here we ask if a strategic spatiotemporal distribution of vaccines, e.g. to prioritize certain cities, can help to increase the overall survival rate of a population subject to an epidemic disease. To this end, we propose a strategy for the distribution of vaccines in time and space, which sequentially prioritizes regions with the most new cases of infection during a certain time frame and compare it with the standard practice of distributing vaccines demographically. Using a simple statistical model we find that, for a locally well-mixed population, the proposed strategy strongly reduces the number of deaths (by about a factor of two for basic reproduction numbers of $$R_0\sim 1.5-4$$ R 0 ∼ 1.5 - 4 and by about 35% for $$R_0\sim 1$$ R 0 ∼ 1 ). The proposed vaccine distribution strategy establishes the idea that prioritizing individuals not only regarding individual factors, such as their risk of spreading the disease, but also according to the region in which they live can help saving lives. The suggested vaccine distribution strategy can be tested in more detailed models in the future and might inspire discussions regarding the importance of spatiotemporal distribution rules for vaccination guidelines.

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Optimal Vaccine Distribution Strategy for Different Age Groups of Population: A Differential Evolution Algorithm Approach

Mathematical Problems in Engineering ◽

10.1155/2014/702973 ◽

2014 ◽

Vol 2014 ◽

pp. 1-7 ◽

Cited By ~ 3

Author(s):

Xiao-Min Hu ◽

Jun Zhang ◽

Haihong Chen

Keyword(s):

Differential Evolution ◽

Disease Transmission ◽

Vaccine Coverage ◽

Age Groups ◽

Differential Evolution Algorithm ◽

Transmission Model ◽

Distribution Strategy ◽

De Algorithm ◽

Vaccine Distribution ◽

Disease Transmission Model

Vaccination is one of the effective ways for protecting susceptible individuals from infectious diseases. Different age groups of population have different vulnerability to the disease and different contact frequencies. In order to achieve the maximum effects, the distribution of vaccine doses to the groups of individuals needs to be optimized. In this paper, a differential evolution (DE) algorithm is proposed to address the problem. The performance of the proposed algorithm has been tested by a classical infectious disease transmission model and a series of simulations have been made. The results show that the proposed algorithm can always obtain the best vaccine distribution strategy which can minimize the number of infectious individuals during the epidemic outbreak. Furthermore, the effects of vaccination on different days and the vaccine coverage percentages have also been discussed.

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A generalized fuzzy cost efficiency model

RAIRO - Operations Research ◽

10.1051/ro/2019102 ◽

2020 ◽

Vol 54 (6) ◽

pp. 1775-1791

Author(s):

Nazila Aghayi ◽

Samira Salehpour

Keyword(s):

Cost Efficiency ◽

Minimum Cost ◽

Extensive Literature ◽

Data Envelopment ◽

Fuzzy Environment ◽

Decision Making Unit ◽

Production Possibility Set ◽

Fuzzy Cost ◽

The Cost ◽

Generalized Cost

The concept of cost efficiency has become tremendously popular in data envelopment analysis (DEA) as it serves to assess a decision-making unit (DMU) in terms of producing minimum-cost outputs. A large variety of precise and imprecise models have been put forward to measure cost efficiency for the DMUs which have a role in constructing the production possibility set; yet, there’s not an extensive literature on the cost efficiency (CE) measurement for sample DMUs (SDMUs). In an effort to remedy the shortcomings of current models, herein is introduced a generalized cost efficiency model that is capable of operating in a fuzzy environment-involving different types of fuzzy numbers-while preserving the Farrell’s decomposition of cost efficiency. Moreover, to the best of our knowledge, the present paper is the first to measure cost efficiency by using vectors. Ultimately, a useful example is provided to confirm the applicability of the proposed methods.

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