Michael Lu

438 citations
7 papers · 308 · h-index 6

Impact in

  • Health top 10%
    • Vaccine Coverage and Hesitancy
  • Epidemiology top 10%
    • Influenza Virus Research Studies
    • Respiratory viral infections research
    • Pneumonia and Respiratory Infections

Papers in

    • Influenza Virus Research Studies 5
    • Respiratory viral infections research 3
    • Vaccine Coverage and Hesitancy 4

Michael Lu

7 papers receiving 289 citations

Peers

Michael Lu
Comparison fields: 5 of 42
  • Health 92
  • Epidemiology 282
  • Modeling and Simulation 28
  • Infectious Diseases 93
  • Agronomy and Crop Science 17
Replace Valentino Tisa with:
Valentino Tisa Italy
C. Hallie Phillips United States
Esther Albéniz Spain
Ushma Wadia Australia
Jamie Loehr United States
Rikard Rykkvin Norway
Jaume Giménez Durán Spain
P Sebastianpillai United Kingdom
Lingsheng Cao China
Elena Grigorieva Russia
Michael Lu relative to Valentino Tisa Italy Valentino Tisa's profile →
Citations per field
00.5×2.7×
Valentino Tisa · 1×
Citations per year

Countries citing papers authored by Michael Lu

Since Specialization
Citations

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

Fields of papers citing papers by Michael Lu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 23 scholars most cited alongside Michael Lu, 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 Michael Lu Line = papers co-authored together Michael Lu links everyone, so they are left out of the graph.

All Works

7 of 7 papers shown
#Work
1 2018125
2 202067
3 202065
4 201921
5 201816
6 20219
7 20215

About Michael Lu

Michael Lu is a scholar working on Epidemiology, Health, Infectious Diseases, Surgery and Pharmacology, having authored 7 papers that have together received 308 indexed citations. Recurring topics across this work include Influenza Virus Research Studies (5 papers), Vaccine Coverage and Hesitancy (4 papers), Respiratory viral infections research (3 papers), Lipoproteins and Cardiovascular Health (1 paper), Allergic Rhinitis and Sensitization (1 paper), Hepatitis C virus research (1 paper), Animal Disease Management and Epidemiology (1 paper) and SARS-CoV-2 and COVID-19 Research (1 paper). The work is most often cited by research in Health (92 citations), Epidemiology (282 citations), Modeling and Simulation (28 citations), Infectious Diseases (93 citations) and Agronomy and Crop Science (17 citations). Michael Lu has collaborated with scholars based in United States and Spain. Frequent co-authors include Yoganand Chillarige, Yun Lu, Richard A. Forshee, Thomas MaCurdy, Yuqin Wei, Michael Wernecke, Héctor S. Izurieta, Jeffrey A. Kelman, Douglas Pratt and Wenjie Xu. Their work appears in journals such as The Journal of Infectious Diseases, Vaccine 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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