Daniele Proverbio

20 papers receiving 207 citations

Peers

Daniele Proverbio
Comparison fields: 5 of 53
  • Modeling and Simulation 41
  • Infectious Diseases 51
  • Neurology 29
  • Statistical and Nonlinear Physics 18
  • Global and Planetary Change 30
Replace Lenka Přibylová with:
Lenka Přibylová Czechia
Valeria d’Andrea Italy
Davide Salzano Italy
Jichao Sun China
Sumali Bajaj United Kingdom
Laurent Mombaerts Luxembourg
Atte Aalto Luxembourg
Zaw Myo Tun Japan
Wesley Cota Brazil
Bastian Prasse Netherlands
Daniele Proverbio relative to Lenka Přibylová Czechia Lenka Přibylová's profile →
Citations per field
00.5×4.3×
Lenka Přibylová · 1×
Citations per year

Countries citing papers authored by Daniele Proverbio

Since Specialization
Citations

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

Fields of papers citing papers by Daniele Proverbio

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 23 papers — load more, or switch the sort, to bring in the rest.

#Work
1 202235
2 202032
3 202126
4 202222
5 202222
6 202118
7 202313
8 202210
9 202110
10 20217
11 20223
12 20203
13 20252
14 20241
15 20251
16 20261
17 20251
18 20221
19 20241
20 20251

About Daniele Proverbio

Daniele Proverbio is a scholar working on Biomedical Engineering, Global and Planetary Change, Modeling and Simulation, Infectious Diseases and Cognitive Neuroscience, having authored 23 papers that have together received 210 indexed citations. Recurring topics across this work include Ecosystem dynamics and resilience (5 papers), COVID-19 epidemiological studies (4 papers), Slime Mold and Myxomycetes Research (4 papers), SARS-CoV-2 and COVID-19 Research (3 papers), COVID-19 and Mental Health (2 papers), Nonlinear Dynamics and Pattern Formation (2 papers), SARS-CoV-2 detection and testing (2 papers) and Mental Health Research Topics (2 papers). The work is most often cited by research in Modeling and Simulation (41 citations), Infectious Diseases (51 citations), Neurology (29 citations), Statistical and Nonlinear Physics (18 citations) and Global and Planetary Change (30 citations). Daniele Proverbio has collaborated with scholars based in Luxembourg, Italy and United Kingdom. Frequent co-authors include Jorge Gonçalves, Stefano Magni, Alexander Skupin, Andreas Husch, Atte Aalto, Frank Hertel, Henry‐Michel Cauchie, Leslie Ogorzaly, Johan Markdahl and Ken Resnicow. Their work appears in journals such as Journal of Theoretical Biology, Physical review. E, iScience, The Science of The Total Environment and Physical Review Research.

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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