Maya Petersen

5.1k citations
76 papers · 2.8k · h-index 28

Impact in

    • HIV/AIDS Research and Interventions
    • HIV/AIDS drug development and treatment
    • Advanced Causal Inference Techniques
    • Statistical Methods and Inference
    • Statistical Methods and Bayesian Inference

Papers in

    • HIV/AIDS Research and Interventions 22
    • SARS-CoV-2 and COVID-19 Research 5
    • SARS-CoV-2 detection and testing 5
    • Advanced Causal Inference Techniques 14
    • Statistical Methods and Inference 9
    • Statistical Methods and Bayesian Inference 7

Maya Petersen

70 papers receiving 2.7k citations

Peers

Maya Petersen
Comparison fields: 5 of 164
  • Infectious Diseases 927
  • Statistics and Probability 405
  • Virology 151
  • Modeling and Simulation 104
  • Health Informatics 29
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Maya L. Petersen United States
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Citations per field
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Citations per year

Countries citing papers authored by Maya Petersen

Since Specialization
Citations

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

Fields of papers citing papers by Maya Petersen

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Maya Petersen, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Maya Petersen Line = papers co-authored together Maya Petersen links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 76 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2014263
2 2010231
3 2015221
4 2015170
5 2014150
6 2014127
7 2020125
8 2007116
9 2008111
10 2014106
11 200386
12 202078
13 201476
14 202057
15 200751
16 201750
17 201648
18 202147
19 201545
20 202043

About Maya Petersen

Maya Petersen is a scholar working on Infectious Diseases, Statistics and Probability, Epidemiology, Economics and Econometrics and Modeling and Simulation, having authored 76 papers that have together received 2.8k indexed citations. Recurring topics across this work include HIV/AIDS Research and Interventions (22 papers), Advanced Causal Inference Techniques (14 papers), Statistical Methods and Inference (9 papers), Statistical Methods and Bayesian Inference (7 papers), COVID-19 epidemiological studies (5 papers), SARS-CoV-2 and COVID-19 Research (5 papers), SARS-CoV-2 detection and testing (5 papers) and Health Systems, Economic Evaluations, Quality of Life (4 papers). The work is most often cited by research in Infectious Diseases (927 citations), Statistics and Probability (405 citations), Virology (151 citations), Modeling and Simulation (104 citations) and Health Informatics (29 citations). Maya Petersen has collaborated with scholars based in United States, Kenya and Uganda. Frequent co-authors include Mark J. van der Laan, Mark van der Laan, Romain Pirracchio, Erin LeDell, Diane V. Havlir, Marco Carone, Sylvie Chevret, Matthieu Resche‐Rigon, Elvin Geng and Steven G. Deeks. Their work appears in journals such as AIDS, Journal of the International AIDS Society, JAIDS Journal of Acquired Immune Deficiency Syndromes, American Journal of Epidemiology and Clinical Infectious Diseases.

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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