Effective containment explains subexponential growth in recent confirmed COVID-19 cases in China

561 indexed citations

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This paper, published in 2020, received 561 indexed citations. Written by Benjamin F. Maier and Dirk Brockmann covering the research area of Infectious Diseases, Modeling and Simulation and Economics and Econometrics. It is primarily cited by scholars working on Modeling and Simulation (430 citations), Economics and Econometrics (216 citations) and Infectious Diseases (131 citations). Published in Science.

Countries where authors are citing Effective containment explains subexponential growth in recent confirmed COVID-19 cases in China

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This map shows the geographic impact of Effective containment explains subexponential growth in recent confirmed COVID-19 cases in China. 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 Effective containment explains subexponential growth in recent confirmed COVID-19 cases in China with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Effective containment explains subexponential growth in recent confirmed COVID-19 cases in China more than expected).

Fields of papers citing Effective containment explains subexponential growth in recent confirmed COVID-19 cases in China

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Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of Effective containment explains subexponential growth in recent confirmed COVID-19 cases in China. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the Effective containment explains subexponential growth in recent confirmed COVID-19 cases in China.

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/10.1126/science.abb4557.

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