Michael D. Welsh

1.7k total citations
38 papers, 1.3k citations indexed

About

Michael D. Welsh is a scholar working on Epidemiology, Infectious Diseases and Immunology. According to data from OpenAlex, Michael D. Welsh has authored 38 papers receiving a total of 1.3k indexed citations (citations by other indexed papers that have themselves been cited), including 18 papers in Epidemiology, 16 papers in Infectious Diseases and 12 papers in Immunology. Recurrent topics in Michael D. Welsh's work include Mycobacterium research and diagnosis (11 papers), Tuberculosis Research and Epidemiology (10 papers) and Microbial infections and disease research (8 papers). Michael D. Welsh is often cited by papers focused on Mycobacterium research and diagnosis (11 papers), Tuberculosis Research and Epidemiology (10 papers) and Microbial infections and disease research (8 papers). Michael D. Welsh collaborates with scholars based in United Kingdom, Ireland and United States. Michael D. Welsh's co-authors include J.M. Pollock, J. McNair, R. Martyn Girvin, D.G. Bryson, D. Todd, Marian McLoughlin, Robin Skuce, Joseph P. Cassidy, J.D. Rodgers and Daniel A. Todd and has published in prestigious journals such as PLoS ONE, Infection and Immunity and Journal of Dairy Science.

In The Last Decade

Michael D. Welsh

38 papers receiving 1.3k citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Michael D. Welsh United Kingdom 20 743 659 432 259 220 38 1.3k
Marco Pittau Italy 22 469 0.6× 298 0.5× 361 0.8× 312 1.2× 362 1.6× 53 1.3k
D.G. Bryson United Kingdom 23 700 0.9× 678 1.0× 231 0.5× 155 0.6× 401 1.8× 35 1.2k
Howard D. Lehmkuhl United States 24 442 0.6× 751 1.1× 291 0.7× 485 1.9× 350 1.6× 100 1.9k
Tomy Joseph Canada 17 471 0.6× 960 1.5× 383 0.9× 431 1.7× 315 1.4× 39 1.4k
María Á. Risalde Spain 23 988 1.3× 476 0.7× 236 0.5× 357 1.4× 186 0.8× 98 1.5k
J. McNair United Kingdom 27 1.4k 1.9× 1.2k 1.9× 225 0.5× 250 1.0× 490 2.2× 51 1.8k
A.C. Odeón Argentina 25 573 0.8× 516 0.8× 322 0.7× 765 3.0× 165 0.8× 114 2.0k
Reto Zanoni Switzerland 25 434 0.6× 889 1.3× 203 0.5× 796 3.1× 134 0.6× 63 1.8k
S. Perl Israel 20 366 0.5× 444 0.7× 136 0.3× 323 1.2× 179 0.8× 101 1.5k
Marta Alonso‐Hearn Spain 23 323 0.4× 631 1.0× 500 1.2× 62 0.2× 129 0.6× 59 1.2k

Countries citing papers authored by Michael D. Welsh

Since Specialization
Citations

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

Fields of papers citing papers by Michael D. Welsh

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Michael D. Welsh

This figure shows the co-authorship network connecting the top 25 collaborators of Michael D. Welsh. A scholar is included among the top collaborators of Michael D. Welsh based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Michael D. Welsh. Michael D. Welsh is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Welsh, Michael D., et al.. (2021). In vitro evaluation of novel (nanoparticle) oral delivery systems allow selection of gut immunomodulatory formulations. Fish & Shellfish Immunology. 113. 125–138. 4 indexed citations
2.
Welsh, Michael D., et al.. (2018). DIVA metabolomics: Differentiating vaccination status following viral challenge using metabolomic profiles. PLoS ONE. 13(4). e0194488–e0194488. 3 indexed citations
3.
McMenamy, Michael, Bernd Hoffmann, Bernadette Earley, et al.. (2017). The development of a real-time reverse transcription-polymerase chain reaction (rRT-PCR) assay using TaqMan technology for the pan detection of bluetongue virus (BTV). Journal of Virological Methods. 245. 35–39. 6 indexed citations
4.
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6.
Earley, Bernadette, et al.. (2015). Comparing the immune response to a novel intranasal nanoparticle PLGA vaccine and a commercial BPI3V vaccine in dairy calves. BMC Veterinary Research. 11(1). 220–220. 38 indexed citations
7.
Hoffmann, Bernd, Michael McMenamy, Bernadette Earley, et al.. (2014). The development of an accelerated reverse-transcription loop mediated isothermal amplification for the serotype specific detection of bluetongue virus 8 in clinical samples. Journal of Virological Methods. 202. 95–100. 9 indexed citations
8.
Smyth, V. J., et al.. (2013). Chicken astrovirus capsid proteins produced by recombinant baculoviruses: potential use for diagnosis and vaccination. Avian Pathology. 42(5). 434–442. 11 indexed citations
9.
Smyth, V. J., et al.. (2012). Capsid protein sequence diversity of chicken astrovirus. Avian Pathology. 41(2). 151–159. 44 indexed citations
10.
Todd, Daniel A., et al.. (2011). Capsid protein sequence diversity of avian nephritis virus. Avian Pathology. 40(3). 249–259. 31 indexed citations
11.
McNair, J., Michael D. Welsh, & J.M. Pollock. (2007). The immunology of bovine tuberculosis and progression toward improved disease control strategies. Vaccine. 25(30). 5504–5511. 36 indexed citations
12.
Rodgers, J.D., J. McNair, Michael D. Welsh, et al.. (2007). Experimental exposure of cattle to a precise aerosolised challenge of Mycobacterium bovis: A novel model to study bovine tuberculosis. Tuberculosis. 87(5). 405–414. 24 indexed citations
13.
Pollock, J.M., J.D. Rodgers, Michael D. Welsh, & J. McNair. (2005). Pathogenesis of bovine tuberculosis: The role of experimental models of infection. Veterinary Microbiology. 112(2-4). 141–150. 67 indexed citations
14.
Pollock, J.M., Michael D. Welsh, & J. McNair. (2005). Immune responses in bovine tuberculosis: Towards new strategies for the diagnosis and control of disease. Veterinary Immunology and Immunopathology. 108(1-2). 37–43. 114 indexed citations
15.
Welsh, Michael D., Rodat T. Cunningham, David Corbett, et al.. (2004). Influence of pathological progression on the balance between cellular and humoral immune responses in bovine tuberculosis. Immunology. 114(1). 101–111. 148 indexed citations
16.
Todd, D., et al.. (2003). Immunopathologic Investigations with an Attenuated Chicken Anemia Virus in Day-Old Chickens. Avian Diseases. 47(4). 1339–1345. 9 indexed citations
17.
Welsh, Michael D., Joseph P. Cassidy, D.G. Bryson, et al.. (2003). The role of WC1+ γδ T-cells in the delayed-type hypersensitivity (DTH) skin-test reaction of Mycobacterium bovis-infected cattle. Veterinary Immunology and Immunopathology. 93(3-4). 169–176. 9 indexed citations
18.
Pollock, J.M. & Michael D. Welsh. (2002). The WC1+ γδ T-cell population in cattle: a possible role in resistance to intracellular infection. Veterinary Immunology and Immunopathology. 89(3-4). 105–114. 52 indexed citations
19.
Pollock, J.M., J. McNair, Michael D. Welsh, et al.. (2001). Immune responses in bovine tuberculosis. Tuberculosis. 81(1-2). 103–107. 80 indexed citations
20.
Welsh, Michael D., et al.. (1999). Salmon Pancreas Disease Virus, an Alphavirus Infecting Farmed Atlantic Salmon, Salmo salar L.. Virology. 256(2). 188–195. 116 indexed citations

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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