Massimo Pacilli

3.1k citations
24 papers · 319 · h-index 8

Impact in

Papers in

    • SARS-CoV-2 detection and testing 5
    • SARS-CoV-2 and COVID-19 Research 4
    • Antifungal resistance and susceptibility 4
    • Viral gastroenteritis research and epidemiology 3
    • Infection Control in Healthcare 3

Massimo Pacilli

21 papers receiving 304 citations

Peers

Massimo Pacilli
Comparison fields: 5 of 76
  • Modeling and Simulation 67
  • Infectious Diseases 159
  • Microbiology 53
  • Applied Microbiology and Biotechnology 14
  • Molecular Medicine 24
Replace Franklin Asiedu‐Bekoe with:
Franklin Asiedu‐Bekoe Ghana
Laura Espenhain Denmark
Ndèye Aïssatou Lakhe Senegal
Wendy Chung United States
Chi Eun Oh South Korea
Xiang Ma China
I. Poujol France
Daniel A. Solomon United States
Patsy Kelso United States
Anja Takla Germany
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Citations per field
00.5×3.6×
Franklin Asiedu‐Bekoe · 1×
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 24 papers — load more, or switch the sort, to bring in the rest.

#Work
1 202096
2 201755
3 202150
4 201825
5 201520
6 202117
7 20189
8 20237
9 20196
10 20175
11 20185
12 20215
13 20244
14 20224
15 20163
16 20202
17 20202
18 20231
19 20231
20 20191

About Massimo Pacilli

Massimo Pacilli is a scholar working on Infectious Diseases, Epidemiology, Applied Microbiology and Biotechnology, General Health Professions and Molecular Medicine, having authored 24 papers that have together received 319 indexed citations. Recurring topics across this work include Antibiotic Use and Resistance (7 papers), SARS-CoV-2 detection and testing (5 papers), SARS-CoV-2 and COVID-19 Research (4 papers), Antifungal resistance and susceptibility (4 papers), Viral gastroenteritis research and epidemiology (3 papers), Antibiotic Resistance in Bacteria (3 papers), COVID-19 epidemiological studies (3 papers) and Infection Control in Healthcare (3 papers). The work is most often cited by research in Modeling and Simulation (67 citations), Infectious Diseases (159 citations), Microbiology (53 citations), Applied Microbiology and Biotechnology (14 citations) and Molecular Medicine (24 citations). Massimo Pacilli has collaborated with scholars based in United States, Uganda and United Kingdom. Frequent co-authors include Stéphanie Black, Tristan D. McPherson, Janna L. Kerins, Isaac Ghinai, M. Allison Arwady, Jennifer E. Layden, Peter Ruestow, Marielle Fricchione, Suzanne F. Beavers and Susan Woods. 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 Nature Communications.

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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