Dataset Information

Global landscape of patents related to human coronaviruses.

ABSTRACT: At present, the COVID-19 pandemic is running rampant, having caused 2.18 million deaths. Characterizing the global patent landscape of coronaviruses is essential not only for informing research and policy, given the current pandemic crisis, but also for anticipating important future developments. While patents are a promising indicator of technological knowledge production widely used in innovation research, they are often an underused resource in biological sciences. In this study, we present a patent landscape for the seven coronaviruses known to infect humans. The information included in this paper provides a strong intellectual groundwork for the ongoing development of therapeutic agents and vaccines along with a deeper discussion of intellectual property rights under epidemic conditions. The results show that there has been a rapid increase in human coronavirus patents, especially COVID-19 patents. China and the United States play an outstanding role in global cooperation and patent application. The leading role of academic institutions and government is increasingly apparent. Notable technological issues related to human coronaviruses include pharmacochemical treatment, diagnosis of viral infection, viral-vector vaccines, and traditional Chinese medicine. Furthermore, a critical challenge lies in balancing commercial competition, enterprise profit, knowledge sharing, and public interest.

SUBMITTER: Liu K

PROVIDER: S-EPMC8071764 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature

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Global landscape of patents related to human coronaviruses.

Liu Kunmeng K Gu Zixuan Z Islam Md Sahidul MS Scherngell Thomas T Kong Xiangjun X Zhao Jing J Chen Xin X Hu Yuanjia Y

International journal of biological sciences 20210410 6

At present, the COVID-19 pandemic is running rampant, having caused 2.18 million deaths. Characterizing the global patent landscape of coronaviruses is essential not only for informing research and policy, given the current pandemic crisis, but also for anticipating important future developments. While patents are a promising indicator of technological knowledge production widely used in innovation research, they are often an underused resource in biological sciences. In this study, we present a ...[more]

PMID: 33907523

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Project description:BACKGROUND:In these trying times, we developed an R package about bibliographic references on coronaviruses. Working with reproducible research principles based on open science, disseminating scientific information, providing easy access to scientific production on this particular issue, and offering a rapid integration in researchers' workflows may help save time in this race against the virus, notably in terms of public health. OBJECTIVE:The goal is to simplify the workflow of interested researchers, with multidisciplinary research in mind. With more than 60,500 medical bibliographic references at the time of publication, this package is among the largest about coronaviruses. METHODS:This package could be of interest to epidemiologists, researchers in scientometrics, biostatisticians, as well as data scientists broadly defined. This package collects references from PubMed and organizes the data in a data frame. We then built functions to sort through this collection of references. Researchers can also integrate the data into their pipeline and implement them in R within their code libraries. RESULTS:We provide a short use case in this paper based on a bibliometric analysis of the references made available by this package. Classification techniques can also be used to go through the large volume of references and allow researchers to save time on this part of their research. Network analysis can be used to filter the data set. Text mining techniques can also help researchers calculate similarity indices and help them focus on the parts of the literature that are relevant for their research. CONCLUSIONS:This package aims at accelerating research on coronaviruses. Epidemiologists can integrate this package into their workflow. It is also possible to add a machine learning layer on top of this package to model the latest advances in research about coronaviruses, as we update this package daily. It is also the only one of this size, to the best of our knowledge, to be built in the R language.

Dataset Information

Global landscape of patents related to human coronaviruses.

Publications

Global landscape of patents related to human coronaviruses.

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