Nidhi Parikh

461 total citations
25 papers, 233 citations indexed

About

Nidhi Parikh is a scholar working on Epidemiology, Modeling and Simulation and Artificial Intelligence. According to data from OpenAlex, Nidhi Parikh has authored 25 papers receiving a total of 233 indexed citations (citations by other indexed papers that have themselves been cited), including 8 papers in Epidemiology, 7 papers in Modeling and Simulation and 6 papers in Artificial Intelligence. Recurrent topics in Nidhi Parikh's work include COVID-19 epidemiological studies (7 papers), Data-Driven Disease Surveillance (6 papers) and Misinformation and Its Impacts (3 papers). Nidhi Parikh is often cited by papers focused on COVID-19 epidemiological studies (7 papers), Data-Driven Disease Surveillance (6 papers) and Misinformation and Its Impacts (3 papers). Nidhi Parikh collaborates with scholars based in United States, India and Türkiye. Nidhi Parikh's co-authors include B. Igelnik, Lenwood S. Heath, Samarth Swarup, Geoffrey Fairchild, Ashlynn R. Daughton, Devaki A. Kelkar, Madhura Kulkarni, Madhav Marathe, Stephen Eubank and Moustafa Youssef and has published in prestigious journals such as Scientific Reports, Journal of Medical Internet Research and Physica A Statistical Mechanics and its Applications.

In The Last Decade

Nidhi Parikh

22 papers receiving 224 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Nidhi Parikh United States 8 65 64 38 33 27 25 233
Blessing Ogbuokiri South Africa 10 108 1.7× 34 0.5× 6 0.2× 28 0.8× 4 0.1× 19 287
Yannet Interian United States 9 45 0.7× 63 1.0× 39 1.0× 16 0.5× 5 0.2× 16 334
William Waites United Kingdom 10 80 1.2× 16 0.3× 8 0.2× 35 1.1× 97 3.6× 24 273
Andrew MacKinlay Australia 12 313 4.8× 46 0.7× 9 0.2× 25 0.8× 5 0.2× 30 432
Daniel Merl United States 6 31 0.5× 14 0.2× 3 0.1× 39 1.2× 25 0.9× 14 335
Anasse Bari United States 7 145 2.2× 21 0.3× 5 0.1× 43 1.3× 53 2.0× 20 359
K. S. M. Tozammel Hossain United States 5 108 1.7× 45 0.7× 7 0.2× 69 2.1× 12 0.4× 16 235
Rajib Bag India 7 323 5.0× 160 2.5× 22 0.6× 37 1.1× 14 0.5× 20 489
Skyler Speakman United States 8 105 1.6× 10 0.2× 18 0.5× 36 1.1× 8 0.3× 23 184
Aabid Shariff United States 8 69 1.1× 28 0.4× 12 0.3× 9 0.3× 2 0.1× 10 452

Countries citing papers authored by Nidhi Parikh

Since Specialization
Citations

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

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

20 of 20 papers shown
1.
Parikh, Nidhi, et al.. (2025). Evaluation of Seismic Artificial Intelligence with Uncertainty. Seismological Research Letters. 97(1). 471–486.
2.
3.
Parikh, Nidhi, et al.. (2021). “Thought I’d Share First” and Other Conspiracy Theory Tweets from the COVID-19 Infodemic: Exploratory Study. JMIR Public Health and Surveillance. 7(4). e26527–e26527. 54 indexed citations
4.
Daughton, Ashlynn R., et al.. (2021). Mining and Validating Social Media Data for COVID-19–Related Human Behaviors Between January and July 2020: Infodemiology Study. Journal of Medical Internet Research. 23(5). e27059–e27059. 7 indexed citations
5.
Parikh, Nidhi, Madhav Marathe, & Samarth Swarup. (2021). Contextualized Behavior Recommendation from Complex Agent-Based Simulations of Disasters. Journal of the Indian Institute of Science. 101(3). 403–417.
6.
Parikh, Nidhi, et al.. (2020). Improving Detection of Disease Re-emergence Using a Web-Based Tool (RED Alert): Design and Case Analysis Study. JMIR Public Health and Surveillance. 7(1). e24132–e24132. 3 indexed citations
7.
Parikh, Nidhi, et al.. (2020). "Thought I'd Share First": An Analysis of COVID-19 Conspiracy Theories and Misinformation Spread on Twitter. arXiv (Cornell University). 1 indexed citations
8.
Romero-Álvarez, Daniel, Nidhi Parikh, Dave Osthus, et al.. (2020). Google Health Trends performance reflecting dengue incidence for the Brazilian states. BMC Infectious Diseases. 20(1). 252–252. 12 indexed citations
9.
Kelkar, Devaki A., et al.. (2020). Meta-Analysis of Prevalence of Triple-Negative Breast Cancer and Its Clinical Features at Incidence in Indian Patients With Breast Cancer. JCO Global Oncology. 6(6). 1052–1062. 38 indexed citations
10.
Daughton, Ashlynn R., et al.. (2019). Development of a Supervised Learning Algorithm for Detection of Potential Disease Reemergence: A Proof of Concept. Health Security. 17(4). 255–267. 1 indexed citations
11.
Ziemann, Amanda, Nidhi Parikh, Amir Siraj, et al.. (2019). Understanding polynomial distributed lag models: truncation lag implications for a mosquito-borne disease risk model in Brazil. 93. 101–101. 1 indexed citations
12.
Parikh, Nidhi, Madhav Marathe, & Samarth Swarup. (2016). Simulation Summarization: (Extended Abstract). Adaptive Agents and Multi-Agents Systems. 1451–1452. 1 indexed citations
13.
Parikh, Nidhi, Madhav Marathe, & Samarth Swarup. (2016). Summarizing Simulation Results Using Causally-Relevant States. Lecture notes in computer science. 10003. 88–103. 6 indexed citations
14.
Parikh, Nidhi, et al.. (2016). A comparison of multiple behavior models in a simulation of the aftermath of an improvised nuclear detonation. Autonomous Agents and Multi-Agent Systems. 30(6). 1148–1174. 11 indexed citations
15.
Parikh, Nidhi, Samarth Swarup, Caitlin Rivers, et al.. (2013). Modeling human behavior in the aftermath of a hypothetical improvised nuclear detonation. Adaptive Agents and Multi-Agents Systems. 949–956. 11 indexed citations
16.
Parikh, Nidhi, Moustafa Youssef, Samarth Swarup, & Stephen Eubank. (2013). Modeling the effect of transient populations on epidemics in Washington DC. Scientific Reports. 3(1). 3152–3152. 16 indexed citations
17.
Parikh, Nidhi, et al.. (2013). A Review of MIMO Technology for Wireless Sensor Network. 2(12). 1 indexed citations
18.
Parikh, Nidhi, et al.. (2012). Modeling the Effects of Transient Populations on Epidemics. National Conference on Artificial Intelligence. 2 indexed citations
19.
Igelnik, B. & Nidhi Parikh. (2003). Kolmogorov's spline network. IEEE Transactions on Neural Networks. 14(4). 725–733. 34 indexed citations
20.
Parikh, Nidhi, et al.. (1984). Fatal toxoplasmosis of the central nervous system in a heroin user with acquired immunodeficiency disease.. PubMed. 84(9). 464–6. 2 indexed citations

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.

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