Mia Moore

1.3k citations
54 papers · 676 indexed · h-index 14

Mia Moore

50 papers receiving 640 citations

Peers

Mia Moore
Comparison fields: 5 of 139
  • Modeling and Simulation 65
  • Infectious Diseases 190
  • Virology 29
  • Organic Chemistry 163
  • Family Practice 12
Replace Michael V. Callahan with:
Michael V. Callahan United States
Jane Chen United States
Zezhou Wang China
Sarah A. Nowak United States
Liping Peng China
Sahar Saeed Canada
Nathan Green United States
Karen S. Moore United States
Mrigendra P. Singh India
Tiantian Wu China
Mia Moore relative to Michael V. Callahan United States Michael V. Callahan's profile →
Citations per field
00.5×12×
Michael V. Callahan · 1×
Citations per year

Countries citing papers authored by Mia Moore

Since Specialization
Citations

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

Fields of papers citing papers by Mia Moore

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network

The 25 scholars most cited alongside Mia Moore, 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 Mia Moore Line = papers co-authored together Mia Moore links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown
#Work
1 20260
2 20250
3 20251
4 20231
5 202313
6 20225
7 202112
8 20213
9 202169
10 202150
11 20201
12 202020
13 201921
14 20194
15 201732
16 20177
17 20164
18
TECHNOLOGY AND KINESIOLOGY EDUCATION: BYOD IN LARGE COURSES
20151
19 19924
20 196316

About Mia Moore

Mia Moore is a scholar working on Modeling and Simulation, Infectious Diseases, Family Practice, Virology and Epidemiology, having authored 54 papers that have together received 676 indexed citations. Recurring topics across this work include HIV/AIDS Research and Interventions (14 papers), HIV, Drug Use, Sexual Risk (11 papers), COVID-19 epidemiological studies (8 papers), HIV/AIDS drug development and treatment (6 papers), SARS-CoV-2 and COVID-19 Research (5 papers), Sex work and related issues (5 papers), Immune Cell Function and Interaction (5 papers) and Adolescent Sexual and Reproductive Health (4 papers). The work is most often cited by research in Modeling and Simulation (65 citations), Infectious Diseases (190 citations), Virology (29 citations), Organic Chemistry (163 citations) and Family Practice (12 citations). Mia Moore has collaborated with scholars based in United States, United Kingdom and South Africa. Frequent co-authors include W. A. Thompson, Dobromir Dimitrov, Young S. Lo, M. Jahi Chappell, Deborah Donnell, J. F. ARNETT, Lucius T. Rossano, Marie‐Claude Boily, David Meloni and Joshua T. Schiffer. Their work appears in journals such as Bulletin of Mathematical Biology, Journal of the International AIDS Society, Epidemics, Clinical Infectious Diseases and JAIDS Journal of Acquired Immune Deficiency Syndromes.

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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