Massimo Pacilli

3.2k citations
27 papers · 359 · h-index 9

Impact in

Papers in

    • Antifungal resistance and susceptibility 4
    • SARS-CoV-2 detection and testing 4
    • SARS-CoV-2 and COVID-19 Research 3
    • Viral gastroenteritis research and epidemiology 2

Massimo Pacilli

25 papers receiving 343 citations

Peers

Massimo Pacilli
Comparison fields: 5 of 81
  • Modeling and Simulation 55
  • Microbiology 51
  • Infectious Diseases 146
  • Molecular Medicine 17
  • Applied Microbiology and Biotechnology 5
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Citations per field
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Citations per year

Countries citing papers authored by Massimo Pacilli

Since Specialization
Citations

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

Fields of papers citing papers by Massimo Pacilli

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 27 papers — load more, or switch the sort, to bring in the rest.

#Work
1 202096
2 202155
3 201755
4 201826
5 201524
6 200724
7 202117
8 20189
9 20238
10 20196
11 20185
12 20175
13 20215
14 20224
15 20244
16 20163
17 20202
18 20202
19 20122
20 20112

About Massimo Pacilli

Massimo Pacilli is a scholar working on Infectious Diseases, Epidemiology, Applied Microbiology and Biotechnology, General Health Professions and Microbiology, having authored 27 papers that have together received 359 indexed citations. Recurring topics across this work include Antibiotic Use and Resistance (6 papers), Antifungal resistance and susceptibility (4 papers), SARS-CoV-2 detection and testing (4 papers), COVID-19 epidemiological studies (3 papers), SARS-CoV-2 and COVID-19 Research (3 papers), Patient Satisfaction in Healthcare (2 papers), Bacterial Infections and Vaccines (2 papers) and Viral gastroenteritis research and epidemiology (2 papers). The work is most often cited by research in Modeling and Simulation (55 citations), Microbiology (51 citations), Infectious Diseases (146 citations), Molecular Medicine (17 citations) and Applied Microbiology and Biotechnology (5 citations). Massimo Pacilli has collaborated with scholars based in United States, Uganda and Italy. Frequent co-authors include Stéphanie Black, Tristan D. McPherson, Janna L. Kerins, Isaac Ghinai, Marielle Fricchione, M. Allison Arwady, Jennifer E. Layden, Peter Ruestow, Kathleen A. Ritger and Suzanne F. Beavers. Their work appears in journals such as Open Forum Infectious Diseases, MMWR Morbidity and Mortality Weekly Report, Clinical Infectious Diseases, American Journal of Infection Control and Environment International.

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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