Dataset Information

Estimation of COVID-19 spread curves integrating global data and borrowing information.

ABSTRACT: Currently, novel coronavirus disease 2019 (COVID-19) is a big threat to global health. The rapid spread of the virus has created pandemic, and countries all over the world are struggling with a surge in COVID-19 infected cases. There are no drugs or other therapeutics approved by the US Food and Drug Administration to prevent or treat COVID-19: information on the disease is very limited and scattered even if it exists. This motivates the use of data integration, combining data from diverse sources and eliciting useful information with a unified view of them. In this paper, we propose a Bayesian hierarchical model that integrates global data for real-time prediction of infection trajectory for multiple countries. Because the proposed model takes advantage of borrowing information across multiple countries, it outperforms an existing individual country-based model. As fully Bayesian way has been adopted, the model provides a powerful predictive tool endowed with uncertainty quantification. Additionally, a joint variable selection technique has been integrated into the proposed modeling scheme, which aimed to identify possible country-level risk factors for severe disease due to COVID-19.

SUBMITTER: Lee SY

PROVIDER: S-EPMC7390340 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Estimation of COVID-19 spread curves integrating global data and borrowing information.

Lee Se Yoon SY Lei Bowen B Mallick Bani B

PloS one 20200729 7

Currently, novel coronavirus disease 2019 (COVID-19) is a big threat to global health. The rapid spread of the virus has created pandemic, and countries all over the world are struggling with a surge in COVID-19 infected cases. There are no drugs or other therapeutics approved by the US Food and Drug Administration to prevent or treat COVID-19: information on the disease is very limited and scattered even if it exists. This motivates the use of data integration, combining data from diverse sourc ...[more]

PMID: 32726361

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Project description:BackgroundPrevious studies on COVID-19 scholarly articles have primarily focused on bibliometric characteristics, neglecting the identification of institutional actors that cite recent scientific contributions related to COVID-19 in the policy domain, and their locations.ObjectiveThe purpose of this study was to assess the online citation network and knowledge structure of COVID-19 research across policy domains over 2 years from January 2020 to January 2022, with a particular emphasis on geographical frequency. Two research questions were addressed. The first question was related to who has been the most active in policy engagement with science and research information sharing during the COVID-19 pandemic, particularly in terms of countries and organization types. The second question was related to whether there are significant differences in the types of coronavirus research shared among countries and continents.MethodsThe Altmetric database was used to collect policy report citations of scientific articles for 3 topic terms (COVID-19, COVID-19 vaccine, and COVID-19 variants). Altmetric provides the URLs of policy agencies that have cited COVID-19 research. The scientific articles used for Altmetric citations are extracted from journals indexed by PubMed. The numbers of COVID-19, COVID-19 vaccine, and COVID-19 variant research outputs between January 1, 2020, and January 31, 2022, were 216,787, 16,748, and 2777, respectively. The study examined the frequency of citations based on policy institutional domains, such as intergovernmental organizations, national and domestic governmental organizations, and nongovernmental organizations (think tanks and academic institutions).ResultsThe World Health Organization (WHO) stood out as the most notable institution citing COVID-19-related research outputs. The WHO actively sought and disseminated information regarding the COVID-19 pandemic. The COVID-19 vaccine citation network exhibited the most extensive connections in terms of degree centrality, 2-local eigenvector centrality, and eigenvector centrality among the 3 key terms. The Netherlands, the United States, the United Kingdom, and Australia were the countries that sought and shared the most information on COVID-19 vaccines, likely due to their high numbers of COVID-19 cases. Developing nations, although gaining quicker access to COVID-19 vaccine information, appeared to be relatively isolated from the enriched COVID-19 pandemic content in the global network.ConclusionsThe global scientific network ecology during the COVID-19 pandemic revealed distinct types of links primarily centered around the WHO. Western countries demonstrated effective networking practices in constructing these networks. The prominent position of the key term "COVID-19 vaccine" demonstrates that nation-states align with global authority regardless of their national contexts. In summary, the citation networking practices of policy agencies have the potential to uncover the global knowledge distribution structure as a proxy for the networking strategy employed during a pandemic.

Dataset Information

Estimation of COVID-19 spread curves integrating global data and borrowing information.

Publications

Estimation of COVID-19 spread curves integrating global data and borrowing information.

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