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Register a new userValidating Optimal COVID-19 Vaccine Distribution Models
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Added by
Dhivya Chandrasekaran
Publication date
2021
fields
Informatics Engineering
and research's language is
English
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With the approval of vaccines for the coronavirus disease by many countries worldwide, most developed nations have begun, and developing nations are gearing up for the vaccination process. This has created an urgent need to provide a solution to optimally distribute the available vaccines once they are received by the authorities. In this paper, we propose a clustering-based solution to select optimal distribution centers and a Constraint Satisfaction Problem framework to optimally distribute the vaccines taking into consideration two factors namely priority and distance. We demonstrate the efficiency of the proposed models using real-world data obtained from the district of Chennai, India. The model provides the decision making authorities with optimal distribution centers across the district and the optimal allocation of individuals across these distribution centers with the flexibility to accommodate a wide range of demographics.
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The COVID-19 pandemic has reshaped the demand for goods and services worldwide. The combination of a public health emergency, economic distress, and misinformation-driven panic have pushed customers and vendors towards the shadow economy. In particular, dark web marketplaces (DWMs), commercial websites accessible via free software, have gained significant popularity. Here, we analyse 851,199 listings extracted from 30 DWMs between January 1, 2020 and November 16, 2020. We identify 788 listings directly related to COVID-19 products and monitor the temporal evolution of product categories including Personal Protective Equipment (PPE), medicines (e.g., hydroxyclorochine), and medical frauds. Finally, we compare trends in their temporal evolution with variations in public attention, as measured by Twitter posts and Wikipedia page visits. We reveal how the online shadow economy has evolved during the COVID-19 pandemic and highlight the importance of a continuous monitoring of DWMs, especially now that real vaccines are available and in short supply. We anticipate our analysis will be of interest both to researchers and public agencies focused on the protection of public health.
The novel coronavirus disease (COVID-19) has rapidly spread around the globe in 2020, with the U.S. becoming the epicenter of COVID-19 cases since late March. As the U.S. begins to gradually resume economic activity, it is imperative for policymakers and power system operators to take a scientific approach to understanding and predicting the impact on the electricity sector. Here, we release a first-of-its-kind cross-domain open-access data hub, integrating data from across all existing U.S. wholesale electricity markets with COVID-19 case, weather, cellular location, and satellite imaging data. Leveraging cross-domain insights from public health and mobility data, we uncover a significant reduction in electricity consumption across that is strongly correlated with the rise in the number of COVID-19 cases, degree of social distancing, and level of commercial activity.
The importance of vaccination and the logistics involved in the procurement, storage and distribution of vaccines across their cold chain has come to the forefront during the COVID-19 pandemic. In this paper, we present a decision support framework for optimizing multiple aspects of vaccine distribution across a multi-tier cold chain network. We propose two multi-period optimization formulations within this framework: first to minimize inventory, ordering, transportation, personnel and shortage costs associated with a single vaccine; the second being an extension of the first for the case when multiple vaccines with differing efficacies and costs are available for the same disease. Vaccine transportation and administration lead times are also incorporated within the models. We use the case of the Indian state of Bihar and COVID-19 vaccines to illustrate the implementation of the framework. We present computational experiments to demonstrate: (a) the organization of the model outputs; (b) how the models can be used to assess the impact of storage capacities (at the cold chain points, transportation vehicle capacities) and manufacturer capacities on the optimal vaccine distribution pattern; and (c) the impact of vaccine efficacies and associated costs such as ordering and transportation costs on the vaccine selection decision informed by the model. We then consider the computational expense of the framework for realistic problem instances, and suggest multiple preprocessing techniques to reduce their computational burden. Our study presents public health authorities and other stakeholders with a vaccine distribution and capacity planning tool for multi-tier cold chain networks.
In this study, we investigate the scientific research response from the early stages of the pandemic, and we review key findings on how the early warning systems developed in previous epidemics responded to contain the virus. The data records are analysed with commutable statistical methods, including R Studio, Bibliometrix package, and the Web of Science data mining tool. We identified few different clusters, containing references to exercise, inflammation, smoking, obesity and many additional factors. From the analysis on Covid-19 and vaccine, we discovered that although the USA is leading in volume of scientific research on Covid 19 vaccine, the leading 3 research institutions (Fudan, Melbourne, Oxford) are not based in the USA. Hence, it is difficult to predict which country would be first to produce a Covid 19 vaccine.
This paper explains the scalable methods used for extracting and analyzing the Covid-19 vaccine data. Using Big Data such as Hadoop and Hive, we collect and analyze the massive data set of the confirmed, the fatality, and the vaccination data set of Covid-19. The data size is about 3.2 Giga-Byte. We show that it is possible to store and process massive data with Big Data. The paper proceeds tempo-spatial analysis, and visual maps, charts, and pie charts visualize the result of the investigation. We illustrate that the more vaccinated, the fewer the confirmed cases.
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