Rumi Chunara

5.5k total citations · 2 hit papers
79 papers, 3.5k citations indexed

About

Rumi Chunara is a scholar working on Epidemiology, Health and Modeling and Simulation. According to data from OpenAlex, Rumi Chunara has authored 79 papers receiving a total of 3.5k indexed citations (citations by other indexed papers that have themselves been cited), including 28 papers in Epidemiology, 14 papers in Health and 14 papers in Modeling and Simulation. Recurrent topics in Rumi Chunara's work include Data-Driven Disease Surveillance (24 papers), COVID-19 epidemiological studies (14 papers) and Influenza Virus Research Studies (8 papers). Rumi Chunara is often cited by papers focused on Data-Driven Disease Surveillance (24 papers), COVID-19 epidemiological studies (14 papers) and Influenza Virus Research Studies (8 papers). Rumi Chunara collaborates with scholars based in United States, Canada and Pakistan. Rumi Chunara's co-authors include John S. Brownstein, David Mann, Paul Testa, Oded Nov, Ji Chen, Jason R. Andrews, Clark C. Freifeld, Qingyu Yuan, Mark S. Smolinski and Oktawia Wójcik and has published in prestigious journals such as SHILAP Revista de lepidopterología, Applied Physics Letters and PLoS ONE.

In The Last Decade

Rumi Chunara

73 papers receiving 3.4k citations

Hit Papers

COVID-19 transforms health care through telemedicine: Evi... 2020 2026 2022 2024 2020 2020 250 500 750

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Rumi Chunara United States 27 1.1k 1.1k 792 500 428 79 3.5k
Elaine O. Nsoesie United States 26 1.0k 1.0× 558 0.5× 304 0.4× 433 0.9× 103 0.2× 78 3.1k
Justin Clark Australia 34 783 0.7× 749 0.7× 791 1.0× 183 0.4× 708 1.7× 155 5.3k
Megan L. Ranney United States 34 318 0.3× 949 0.9× 1.2k 1.5× 675 1.4× 411 1.0× 170 5.0k
Daniel Westreich United States 38 1.5k 1.4× 702 0.7× 939 1.2× 424 0.8× 259 0.6× 184 6.9k
Jay Bhattacharya United States 39 942 0.9× 862 0.8× 1.3k 1.7× 290 0.6× 275 0.6× 169 5.5k
Stephen B. Thacker United States 39 1.2k 1.1× 955 0.9× 1.0k 1.3× 304 0.6× 152 0.4× 110 5.2k
Daniel D. Reidpath Malaysia 39 795 0.7× 1.1k 1.0× 1.5k 1.8× 605 1.2× 191 0.4× 234 5.4k
Felix Greaves United Kingdom 31 832 0.8× 822 0.8× 2.1k 2.6× 386 0.8× 69 0.2× 88 4.6k
Shaun R. Seaman United Kingdom 27 703 0.7× 546 0.5× 384 0.5× 262 0.5× 349 0.8× 91 5.2k
Xinguang Chen United States 47 897 0.8× 710 0.7× 1.4k 1.8× 1.2k 2.5× 144 0.3× 275 6.4k

Countries citing papers authored by Rumi Chunara

Since Specialization
Citations

This map shows the geographic impact of Rumi Chunara'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 Rumi Chunara with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Rumi Chunara more than expected).

Fields of papers citing papers by Rumi Chunara

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Rumi Chunara. 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 Rumi Chunara. The network helps show where Rumi Chunara may publish in the future.

Co-authorship network of co-authors of Rumi Chunara

This figure shows the co-authorship network connecting the top 25 collaborators of Rumi Chunara. A scholar is included among the top collaborators of Rumi Chunara 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 Rumi Chunara. Rumi Chunara 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.
3.
Rahman, Salman, et al.. (2024). Understanding Disparities in Post Hoc Machine Learning Explanation. 2374–2388. 2 indexed citations
4.
Zhao, Yuan, et al.. (2024). Constructing Social Vulnerability Indexes with Increased Data and Machine Learning Highlight the Importance of Wealth Across Global Contexts. Social Indicators Research. 175(2). 639–657. 3 indexed citations
5.
Chunara, Rumi, Aysha Habib Khan, Afia Zafar, et al.. (2023). Prevalence of familial hypercholesterolemia in a country-wide laboratory network in Pakistan: 10-year data from 988, 306 patients. Progress in Cardiovascular Diseases. 79. 19–27. 4 indexed citations
6.
Blecker, Saul, Xiyue Li, Ian M. Kronish, et al.. (2023). Neighborhood-Level Socioeconomic Status and Prescription Fill Patterns Among Patients With Heart Failure. JAMA Network Open. 6(12). e2347519–e2347519. 18 indexed citations
7.
Adhikari, Samrachana, Xiyue Li, Rumi Chunara, et al.. (2023). Cohort profile: a large EHR-based cohort with linked pharmacy refill and neighbourhood social determinants of health data to assess heart failure medication adherence. BMJ Open. 13(12). e076812–e076812. 1 indexed citations
8.
Margolin, Drew, et al.. (2022). Search Term Identification Methods for Computational Health Communication: Word Embedding and Network Approach for Health Content on YouTube. JMIR Medical Informatics. 10(8). e37862–e37862. 7 indexed citations
9.
Adhikari, Samrachana, Xiyue Li, John A. Dodson, et al.. (2022). Association Between Copayment Amount and Filling of Medications for Angiotensin Receptor Neprilysin Inhibitors in Patients With Heart Failure. Journal of the American Heart Association. 11(24). e027662–e027662. 8 indexed citations
10.
Mandal, Soumik, Batia M. Wiesenfeld, David Mann, et al.. (2022). Evidence for Telemedicine’s Ongoing Transformation of Health Care Delivery Since the Onset of COVID-19: Retrospective Observational Study. JMIR Formative Research. 6(10). e38661–e38661. 14 indexed citations
11.
Chunara, Rumi, et al.. (2022). Generalizability challenges of mortality risk prediction models: A retrospective analysis on a multi-center database. SHILAP Revista de lepidopterología. 1(4). e0000023–e0000023. 28 indexed citations
12.
Padilla, Lace, et al.. (2022). Impact of COVID-19 forecast visualizations on pandemic risk perceptions. Scientific Reports. 12(1). 2014–2014. 18 indexed citations
13.
Daughton, Ashlynn R., Rumi Chunara, & Michael J. Paul. (2020). Comparison of Social Media, Syndromic Surveillance, and Microbiologic Acute Respiratory Infection Data: Observational Study. JMIR Public Health and Surveillance. 6(2). e14986–e14986. 9 indexed citations
14.
Chunara, Rumi, et al.. (2019). Deep Landscape Features for Improving Vector-borne Disease Prediction. arXiv (Cornell University). 44–51. 4 indexed citations
15.
Baltrusaitis, Kristin, Mauricio Santillana, Adam W. Crawley, et al.. (2017). Determinants of Participants’ Follow-Up and Characterization of Representativeness in Flu Near You, A Participatory Disease Surveillance System. JMIR Public Health and Surveillance. 3(2). e18–e18. 42 indexed citations
16.
Ray, Bisakha, Elodie Ghedin, & Rumi Chunara. (2016). Network inference from multimodal data: A review of approaches from infectious disease transmission. Journal of Biomedical Informatics. 64. 44–54. 10 indexed citations
18.
Wójcik, Oktawia, John S. Brownstein, Rumi Chunara, & Michael A. Johansson. (2014). Public health for the people: participatory infectious disease surveillance in the digital age. Emerging Themes in Epidemiology. 11(1). 7–7. 98 indexed citations
19.
Chunara, Rumi, et al.. (2013). Assessing the Online Social Environment for Surveillance of Obesity Prevalence. PLoS ONE. 8(4). e61373–e61373. 57 indexed citations
20.
Lee, Jungchul, Rumi Chunara, Kristofor R. Payer, et al.. (2010). Suspended microchannel resonators with piezoresistive sensors. Lab on a Chip. 11(4). 645–651. 57 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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