Nidhi Parikh
- Artificial Intelligence
- Sociology and Political Science
- Statistical and Nonlinear Physics top 10%
- Epidemiology
- Modeling and Simulation top 10%
- Co-authors
- B. IgelnikLenwood S. HeathSamarth SwarupGeoffrey FairchildAshlynn R. DaughtonMadhura KulkarniDevaki A. KelkarMadhav Marathe
- Topics
- COVID-19 epidemiological studies (7 papers)Data-Driven Disease Surveillance (6 papers)Misinformation and Its Impacts (3 papers)
- Journals
- Scientific ReportsJournal of Medical Internet ResearchPhysica A Statistical Mechanics and its Applications
- Partner nations
- United StatesIndiaTürkiye
In The Last Decade
Nidhi Parikh
22 papers receiving 224 citations
Peers
Comparison fields: 5 of 85
- Artificial Intelligence 65
- Sociology and Political Science 64
- Statistical and Nonlinear Physics 38
- Epidemiology 33
- Modeling and Simulation 27
Countries citing papers authored by Nidhi Parikh
This map shows the geographic impact of Nidhi Parikh'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 Nidhi Parikh with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Nidhi Parikh more than expected).
Fields of papers citing papers by Nidhi Parikh
This network shows the impact of papers produced by Nidhi Parikh. 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 Nidhi Parikh. The network helps show where Nidhi Parikh may publish in the future.
Co-authorship network of co-authors of Nidhi Parikh
This figure shows the co-authorship network connecting the top 25 collaborators of Nidhi Parikh. A scholar is included among the top collaborators of Nidhi Parikh based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Nidhi Parikh. Nidhi Parikh is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 1 | |
| 3 | 1 | |
| 4 | 7 | |
| 5 | 54 | |
| 6 | 0 | |
| 7 | 3 | |
| 8 | "Thought I'd Share First": An Analysis of COVID-19 Conspiracy Theories and Misinformation Spread on Twitter | 1 |
| 9 | 12 | |
| 10 | 1 | |
| 11 | 1 | |
| 12 | 5 | |
| 13 | 1 | |
| 14 | 6 | |
| 15 | 11 | |
| 16 | 11 | |
| 17 | 16 | |
| 18 | A Review of MIMO Technology for Wireless Sensor Network | 1 |
| 19 | Modeling the Effects of Transient Populations on Epidemics | 2 |
| 20 | 34 |
About Nidhi Parikh
Nidhi Parikh is a scholar working on Modeling and Simulation, Health and Statistical and Nonlinear Physics, having authored 25 papers that have together received 233 indexed citations. Recurring topics across this work include COVID-19 epidemiological studies (7 papers), Data-Driven Disease Surveillance (6 papers) and Misinformation and Its Impacts (3 papers). The work is most often cited by research in Modeling and Simulation (27 citations), Statistical and Nonlinear Physics (38 citations) and Health (26 citations). Nidhi Parikh has collaborated with scholars based in United States, India and Türkiye. Frequent co-authors include B. Igelnik, Lenwood S. Heath, Samarth Swarup, Geoffrey Fairchild, Ashlynn R. Daughton, Madhura Kulkarni, Devaki A. Kelkar, Madhav Marathe, Stephen Eubank and Moustafa Youssef. Their work appears in journals such as Scientific Reports, Journal of Medical Internet Research and Physica A Statistical Mechanics and its Applications.
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.