Shweta Bansal
- Modeling and Simulation top 0.1%
- COVID-19 epidemiological studies 43
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- Complex Network Analysis Techniques 10
- Infectious Diseases top 2%
- Agronomy and Crop Science top 2%
- Animal Disease Management and Epidemiology 10
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- Mathematical and Theoretical Epidemiology and Ecology Models 11
- Zoonotic diseases and public health 8
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- Influenza Virus Research Studies 21
- Data-Driven Disease Surveillance 12
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- Evolution and Genetic Dynamics 7
- Co-authors
- Lauren Ancel MeyersBryan T. GrenfellColin J. CarlsonCasey M. ZipfelPratha SahCécile ViboudGregory F. AlberyGerardo Chowell
- Journals
- Nature (1 paper)Proceedings of the National Academy of Sciences (1 paper)Nature Communications (3 papers)
- Partner nations
- United StatesIndiaUnited Kingdom
In The Last Decade
Shweta Bansal
111 papers receiving 3.8k citations
Hit Papers
Peers
Comparison fields: 5 of 171
- Modeling and Simulation 1.5k
- Statistical and Nonlinear Physics 570
- Infectious Diseases 733
- Agronomy and Crop Science 386
- Public Health, Environmental and Occupational Health 942
Countries citing papers authored by Shweta Bansal
This map shows the geographic impact of Shweta Bansal's research. 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 Shweta Bansal with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Shweta Bansal more than expected).
Fields of papers citing papers by Shweta Bansal
This network shows the impact of papers produced by Shweta Bansal. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Shweta Bansal. The network helps show where Shweta Bansal may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Shweta Bansal, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2025 | 1 | |
| 2 | 2025 | 1 | |
| 3 | 2025 | 1 | |
| 4 | 2024 | 0 | |
| 5 | 2023 | 0 | |
| 6 | 2023 | 2 | |
| 7 | 2022 | 19 | |
| 8 | 2022 | 22 | |
| 9 | 2021 | 26 | |
| 10 | 2021 | 13 | |
| 11 | 2020 | 78 | |
| 12 | 2020 | 20 | |
| 13 | 2019 | 13 | |
| 14 | 2018 | 19 | |
| 15 | 2018 | 23 | |
| 16 | 2017 | 100 | |
| 17 | 2016 | 143 | |
| 18 | A newborn presenting with epidermolysis bullosa with duodenal atresia: A very rare case report and review of the literature | 2015 | 2 |
| 19 | 2015 | 39 | |
| 20 | 2009 | 68 |
About Shweta Bansal
Shweta Bansal is a scholar working on Modeling and Simulation, Health and Epidemiology, having authored 118 papers that have together received 3.9k indexed citations. Recurring topics across this work include COVID-19 epidemiological studies (43 papers), Influenza Virus Research Studies (21 papers), Data-Driven Disease Surveillance (12 papers), Mathematical and Theoretical Epidemiology and Ecology Models (11 papers), Complex Network Analysis Techniques (10 papers), Animal Disease Management and Epidemiology (10 papers), Zoonotic diseases and public health (8 papers) and Evolution and Genetic Dynamics (7 papers). The work is most often cited by research in Modeling and Simulation (1.5k citations), Statistical and Nonlinear Physics (570 citations) and Infectious Diseases (733 citations). Shweta Bansal has collaborated with scholars based in United States, India and United Kingdom. Frequent co-authors include Lauren Ancel Meyers, Bryan T. Grenfell, Colin J. Carlson, Casey M. Zipfel, Pratha Sah, Cécile Viboud, Gregory F. Albery, Gerardo Chowell, Babak Pourbohloul and Lisa Sattenspiel. Their work appears in journals such as Nature, Proceedings of the National Academy of Sciences and Nature Communications.
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.