Diabetes is a risk factor for the progression and prognosis of COVID‐19

1.0k indexed citations

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This paper, published in 2020, received 1.0k indexed citations. Written by Weina Guo, Mingyue Li, Yalan Dong, Haifeng Zhou, Zili Zhang, Chunxia Tian, Renjie Qin, Haijun Wang, Yin Shen and Lei Zhao covering the research area of Genetics, Epidemiology and Infectious Diseases. It is primarily cited by scholars working on Infectious Diseases (750 citations), Neurology (361 citations) and Genetics (231 citations). Published in Diabetes/Metabolism Research and Reviews.

Countries where authors are citing Diabetes is a risk factor for the progression and prognosis of COVID‐19

Specialization
Citations

This map shows the geographic impact of Diabetes is a risk factor for the progression and prognosis of COVID‐19. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Diabetes is a risk factor for the progression and prognosis of COVID‐19 with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Diabetes is a risk factor for the progression and prognosis of COVID‐19 more than expected).

Fields of papers citing Diabetes is a risk factor for the progression and prognosis of COVID‐19

Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of Diabetes is a risk factor for the progression and prognosis of COVID‐19. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the Diabetes is a risk factor for the progression and prognosis of COVID‐19.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

This paper is also available at doi.org/10.1002/dmrr.3319.

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