Conor Tweed

1.2k citations
19 papers · 298 · h-index 9

Impact in

Papers in

Conor Tweed

18 papers receiving 291 citations

Peers

Conor Tweed
Comparison fields: 5 of 71
  • Infectious Diseases 139
  • Pharmacology 42
  • Statistics and Probability 25
  • Epidemiology 91
  • General Health Professions 52
Replace Silvia Cohn with:
Silvia Cohn United States
Johannes P. Mouton South Africa
Victoria Pilkington United Kingdom
Hadija Semvua Tanzania
Luana Seles Alves Brazil
Grace L. Barnes United States
Ali Nikfarjam Iran
D. F. Wares India
François Houÿez France
Meredith B. Brooks United States
Conor Tweed relative to Silvia Cohn United States Silvia Cohn's profile →
Citations per field
00.5×3.8×
Silvia Cohn · 1×
Citations per year

Countries citing papers authored by Conor Tweed

Since Specialization
Citations

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

Fields of papers citing papers by Conor Tweed

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

19 of 19 papers shown
#Work
1 201992
2 201850
3 201650
4 201827
5 202022
6 202111
7 20209
8 20229
9 20248
10 20206
11 20225
12 20243
13 20231
14 20251
15 20211
16 20241
17 20231
18 20171
19 20190

About Conor Tweed

Conor Tweed is a scholar working on Infectious Diseases, Epidemiology, General Health Professions, Statistics and Probability and Surgery, having authored 19 papers that have together received 298 indexed citations. Recurring topics across this work include Tuberculosis Research and Epidemiology (7 papers), Statistical Methods in Clinical Trials (5 papers), Pneumocystis jirovecii pneumonia detection and treatment (3 papers), Advanced Causal Inference Techniques (3 papers), Patient-Provider Communication in Healthcare (2 papers), Mental Health and Patient Involvement (2 papers), Infectious Diseases and Tuberculosis (2 papers) and Health Systems, Economic Evaluations, Quality of Life (2 papers). The work is most often cited by research in Infectious Diseases (139 citations), Pharmacology (42 citations), Statistics and Probability (25 citations), Epidemiology (91 citations) and General Health Professions (52 citations). Conor Tweed has collaborated with scholars based in United Kingdom, United States and South Africa. Frequent co-authors include Angela M. Crook, Rodney Dawson, Carl M. Mendel, Melvin Spigelman, Andrew Nunn, Bec Hanley, Mohammed Rassool, Richard Stephens, Alphonse Okwera and Suzanne Staples. Their work appears in journals such as Research Involvement and Engagement, BMC Medicine, PLoS Medicine, The Lancet Respiratory Medicine and BMC Pulmonary Medicine.

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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