Deepit Bhatia
Impact in
- Modeling and Simulation top 5%
- COVID-19 epidemiological studies
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- Viral Infections and Vectors
- COVID-19 Clinical Research Studies
Papers in
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- COVID-19 epidemiological studies 5
-
- Data-Driven Disease Surveillance 2
- Co-authors
- Isaac I. Bogoch (4 shared papers)Ashleigh R. Tuite (4 shared papers)Kamran Khan (3 shared papers)Alexander Watts (2 shared papers)Andrea Thomas-Bachli (1 shared paper)Carmen Huber (1 shared paper)Jean Hai Ein Yong (1 shared paper)Rahim Moineddin (2 shared papers)
- Journals
- Journal of Travel Medicine (2 papers)Vaccine (1 paper)Annals of Internal Medicine (1 paper)Journal of Transport & Health (1 paper)The Annals of Family Medicine (1 paper)
- Partner nations
- CanadaUnited StatesUnited Kingdom
In The Last Decade
Deepit Bhatia
7 papers receiving 205 citations
Peers
Comparison fields: 5 of 67
- Modeling and Simulation 71
- Infectious Diseases 71
- Public Health, Environmental and Occupational Health 83
- Health 21
- Transportation 16
Countries citing papers authored by Deepit Bhatia
This map shows the geographic impact of Deepit Bhatia'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 Deepit Bhatia with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Deepit Bhatia more than expected).
Fields of papers citing papers by Deepit Bhatia
This network shows the impact of papers produced by Deepit Bhatia. 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 Deepit Bhatia. The network helps show where Deepit Bhatia may publish in the future.
Co-authors
The 25 scholars most cited alongside Deepit Bhatia, 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 | 2018 | 66 | |
| 2 | 2018 | 40 | |
| 3 | 2020 | 34 | |
| 4 | 2020 | 21 | |
| 5 | 2020 | 20 | |
| 6 | 2016 | 14 | |
| 7 | 2017 | 13 | |
| 8 | 2025 | 0 |
About Deepit Bhatia
Deepit Bhatia is a scholar working on Modeling and Simulation, Epidemiology, Public Health, Environmental and Occupational Health, Infectious Diseases and Health, having authored 8 papers that have together received 208 indexed citations. Recurring topics across this work include COVID-19 epidemiological studies (5 papers), Data-Driven Disease Surveillance (2 papers), Mosquito-borne diseases and control (2 papers), Urban Transport and Accessibility (1 paper), Injury Epidemiology and Prevention (1 paper), Global Public Health Policies and Epidemiology (1 paper), Traffic and Road Safety (1 paper) and COVID-19 Digital Contact Tracing (1 paper). The work is most often cited by research in Modeling and Simulation (71 citations), Infectious Diseases (71 citations), Public Health, Environmental and Occupational Health (83 citations), Health (21 citations) and Transportation (16 citations). Deepit Bhatia has collaborated with scholars based in Canada, United States and United Kingdom. Frequent co-authors include Isaac I. Bogoch, Ashleigh R. Tuite, Kamran Khan, Alexander Watts, Andrea Thomas-Bachli, Carmen Huber, Jean Hai Ein Yong, Rahim Moineddin, Nathan M. Stall and Vasily Giannakeas. Their work appears in journals such as Journal of Travel Medicine, Vaccine, Annals of Internal Medicine, Journal of Transport & Health and The Annals of Family Medicine.
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.