Blockchain technology for a Safe and Transparent Covid-19 Vaccination
- URL: http://arxiv.org/abs/2104.05428v1
- Date: Thu, 8 Apr 2021 09:55:44 GMT
- Title: Blockchain technology for a Safe and Transparent Covid-19 Vaccination
- Authors: Maha Filali Rotbi and Saad Motahhir and Abdelaziz El Ghzizal
- Abstract summary: In late 2019, we witnessed the apparition of the covid-19 virus.
The virus appeared first in Wuhan, and due to people travel was spread worldwide.
In this paper we suggest a system to manage the registration, storage, and distribution of the vaccines.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: In late 2019, we witnessed the apparition of the covid-19 virus. The virus
appeared first in Wuhan, and due to people travel was spread worldwide.
Exponential spread as well as high mortality rates, the two characteristics of
the SARS-CoV-2 virus that pushed the entire world into a global lock-down.
Health and economic crisis, along with social distancing have put the globe in
a highly challenging situation. Unprecedented pressure on the health care
system exposed many loopholes not only in this industry but many other sectors,
which resulted in a set of new challenges that researchers and scientists among
others must face. In all these circumstances, we could attend, in a
surprisingly short amount of time, the creation of multiple vaccine candidates.
The vaccines were clinically tested and approved, which brought us to the phase
of vaccination. Safety, security, transparency, and traceability are highly
required in this context. As a contribution to assure an efficient vaccination
campaign, in this paper we suggest a Blockchain-based system to manage the
registration, storage, and distribution of the vaccines.
Related papers
- Dense Feature Memory Augmented Transformers for COVID-19 Vaccination
Search Classification [60.49594822215981]
This paper presents a classification model for detecting COVID-19 vaccination related search queries.
We propose a novel approach of considering dense features as memory tokens that the model can attend to.
We show that this new modeling approach enables a significant improvement to the Vaccine Search Insights (VSI) task.
arXiv Detail & Related papers (2022-12-16T13:57:41Z) - Doctors vs. Nurses: Understanding the Great Divide in Vaccine Hesitancy
among Healthcare Workers [64.1526243118151]
We find that doctors are overall more positive toward the COVID-19 vaccines.
Doctors are more concerned with the effectiveness of the vaccines over newer variants.
Nurses pay more attention to the potential side effects on children.
arXiv Detail & Related papers (2022-09-11T14:22:16Z) - VacciNet: Towards a Smart Framework for Learning the Distribution Chain
Optimization of Vaccines for a Pandemic [0.0]
We put forward a novel framework leveraging Supervised Learning and Reinforcement Learning (RL) which we call VacciNet.
RL is capable of learning to predict the demand of vaccination in a state of a country as well as suggest optimal vaccine allocation in the state for minimum cost of procurement and supply.
arXiv Detail & Related papers (2022-08-01T19:37:33Z) - Assessing the influence of French vaccine critics during the two first
years of the COVID-19 pandemic [0.0]
We look at the capacity of vaccine-critical activists to influence a wider public on social media during the COVID-19 epidemic.
We analyze the evolution of debates over the COVID-19 vaccine on the French Twittosphere, during two first years of the pandemic.
While debates over vaccines experienced a surge during this period, the share of vaccine-critical contents in these debates remains stable except for a limited number of short periods associated with specific events.
arXiv Detail & Related papers (2022-02-22T14:47:41Z) - Vaccine skepticism detection by network embedding [0.0]
We develop techniques to efficiently differentiate between pro-vaxxer and vax-skeptic content.
We deploy several node embedding and community detection models that scale well for graphs with millions of edges.
arXiv Detail & Related papers (2021-10-20T12:30:51Z) - Sentiment Analysis and Topic Modeling for COVID-19 Vaccine Discussions [10.194753795363667]
We conduct an in-depth analysis of tweets related to the coronavirus vaccine on Twitter.
Results show that a majority of people are confident in the effectiveness of vaccines and are willing to get vaccinated.
Negative tweets are often associated with the complaints of vaccine shortages, side effects after injections and possible death after being vaccinated.
arXiv Detail & Related papers (2021-10-08T23:30:17Z) - Blockchain-based Covid Vaccination Registration and Monitoring [1.9573380763700712]
We have presented a blockchain-based system that seamlessly integrates testing and vaccination system.
The instant verification of any tamper-proof result and a transparent and efficient vaccination system have been exhibited.
We have also implemented the system as "Digital Vaccine Passport" (DVP) and analysed its performance.
arXiv Detail & Related papers (2021-09-20T11:55:02Z) - Rapid COVID-19 Risk Screening by Eye-region Manifestations [64.6260390977642]
There are more and more ocular manifestations that have been reported in the COVID-19 patients as growing clinical evidence.
We propose a new fast screening method of analyzing the eye-region images, captured by common CCD and CMOS cameras.
Our model for COVID-19 rapid prescreening have the merits of the lower cost, fully self-performed, non-invasive, importantly real-time, and thus enables the continuous health surveillance.
arXiv Detail & Related papers (2021-06-12T01:56:10Z) - The illicit trade of COVID-19 vaccines on the dark web [55.45786602961871]
Early analyses revealed that dark web marketplaces (DWMs) started offering COVID-19 related products (e.g., masks and COVID-19 tests) as soon as the COVID-19 pandemic started.
Here, we broaden the scope and depth of previous investigations by analysing 194 DWMs until July 2021, including the crucial period in which vaccines became available.
We show that recreational drugs are the most affected among traditional DWMs product, with COVID-19 mentions steadily increasing since March 2020.
arXiv Detail & Related papers (2021-02-10T14:52:54Z) - Understanding the temporal evolution of COVID-19 research through
machine learning and natural language processing [66.63200823918429]
The outbreak of the novel coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been continuously affecting human lives and communities around the world.
We used multiple data sources, i.e., PubMed and ArXiv, and built several machine learning models to characterize the landscape of current COVID-19 research.
Our findings confirm the types of research available in PubMed and ArXiv differ significantly, with the former exhibiting greater diversity in terms of COVID-19 related issues.
arXiv Detail & Related papers (2020-07-22T18:02:39Z) - Falling into the Echo Chamber: the Italian Vaccination Debate on Twitter [65.7192861893042]
We examine the extent to which the vaccination debate on Twitter is conductive to potential outreach to the vaccination hesitant.
We discover that the vaccination skeptics, as well as the advocates, reside in their own distinct "echo chambers"
At the center of these echo chambers we find the ardent supporters, for which we build highly accurate network- and content-based classifiers.
arXiv Detail & Related papers (2020-03-26T13:55:50Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.