Daisy Mugo

1.5k citations
10 papers · 316 · h-index 7

Impact in

  • Microbiology top 10%
    • Bacterial Infections and Vaccines
  • Epidemiology top 10%
    • Pneumonia and Respiratory Infections
    • Respiratory viral infections research
    • Mycobacterium research and diagnosis
    • Influenza Virus Research Studies

Papers in

    • SARS-CoV-2 and COVID-19 Research 3
    • Tuberculosis Research and Epidemiology 2
    • SARS-CoV-2 detection and testing 2
    • Pneumonia and Respiratory Infections 4
    • Mycobacterium research and diagnosis 2

Daisy Mugo

10 papers receiving 311 citations

Peers

Daisy Mugo
Comparison fields: 5 of 46
  • Microbiology 50
  • Epidemiology 235
  • Infectious Diseases 79
  • Microbiology 2
  • Modeling and Simulation 9
Replace Bassira Issaka with:
Bassira Issaka Niger
Hitt Sharma India
Erin A. McDonough United States
Robert Musyimi Kenya
Camilla Virta Finland
Lenesha Warrener United Kingdom
Lucky Sangal India
Marta Bertran United Kingdom
Amy Pinsent United Kingdom
Rita Côrte‐Real Portugal
Daisy Mugo relative to Bassira Issaka Niger Bassira Issaka's profile →
Citations per field
00.5×
Bassira Issaka · 1×
Citations per year

Countries citing papers authored by Daisy Mugo

Since Specialization
Citations

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

Fields of papers citing papers by Daisy Mugo

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

10 of 10 papers shown
#Work
1 201291
2 201274
3 201253
4 201141
5 202315
6 201714
7 202113
8 20225
9 20235
10 20215

About Daisy Mugo

Daisy Mugo is a scholar working on Infectious Diseases, Epidemiology, Molecular Biology, Surgery and Modeling and Simulation, having authored 10 papers that have together received 316 indexed citations. Recurring topics across this work include Pneumonia and Respiratory Infections (4 papers), SARS-CoV-2 and COVID-19 Research (3 papers), Tuberculosis Research and Epidemiology (2 papers), SARS-CoV-2 detection and testing (2 papers), Mycobacterium research and diagnosis (2 papers), Biosensors and Analytical Detection (1 paper), Diagnosis and treatment of tuberculosis (1 paper) and COVID-19 epidemiological studies (1 paper). The work is most often cited by research in Microbiology (50 citations), Epidemiology (235 citations), Infectious Diseases (79 citations), Microbiology (2 citations) and Modeling and Simulation (9 citations). Daisy Mugo has collaborated with scholars based in Kenya, United Kingdom and United States. Frequent co-authors include J. Anthony G. Scott, Robert Musyimi, Caroline Tigoi, Osman Abdullahi, Angela Karani, Marc Lipsitch, Andrew Brent, Susan C. Morpeth, Michael Levin and Hellen Gatakaa. Their work appears in journals such as PLoS ONE, Nature Communications, Scientific Reports, Journal of Clinical Microbiology and International Journal of 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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