Filippo Trentini

3.8k citations
37 papers · 1.6k indexed · 2 hit papers · h-index 17

Filippo Trentini

33 papers receiving 1.5k citations

Hit Papers

Evaluation of Wani...1372013202620172021200400600

Peers

Filippo Trentini
Comparison fields: 5 of 122
  • Modeling and Simulation 499
  • Infectious Diseases 504
  • Health 223
  • Immunology 535
  • Oncology 232
Replace Neta S. Zuckerman with:
Neta S. Zuckerman Israel
Aaron Sutherland United States
Sydney I. Ramirez United States
Carolyn Rydyznski Moderbacher United States
Daniel Marrama United States
Siaw Shi Boon Hong Kong
Oliver J. Watson United Kingdom
Jayachandran Radhakrishnan United Kingdom
Mark Frank Germany
Alexandra B. Hogan United Kingdom
Filippo Trentini relative to Neta S. Zuckerman Israel Neta S. Zuckerman's profile →
Citations per field
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Neta S. Zuckerman · 1×
Citations per year

Countries citing papers authored by Filippo Trentini

Since Specialization
Citations

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

Fields of papers citing papers by Filippo Trentini

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network

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

All Works

20 of 20 papers shown
#Work
1 20250
2 20240
3 20240
4 202315
5 20231
6 20234
7 20221
8 202229
9 20226
10 202215
11 20215
12 202141
13 202162
14 202146
15
Effectiveness of regional restrictions in reducing SARS-CoV-2 transmission during the second wave of COVID-19, Italy
20215
16 202165
17 20216
18 20199
19 201810
20 201752

About Filippo Trentini

Filippo Trentini is a scholar working on Modeling and Simulation, Infectious Diseases and Health, having authored 37 papers that have together received 1.6k indexed citations. Recurring topics across this work include COVID-19 epidemiological studies (25 papers), SARS-CoV-2 and COVID-19 Research (11 papers), Vaccine Coverage and Hesitancy (9 papers), COVID-19 Clinical Research Studies (8 papers), COVID-19 Pandemic Impacts (6 papers), Virology and Viral Diseases (4 papers), SARS-CoV-2 detection and testing (4 papers) and COVID-19 and healthcare impacts (4 papers). The work is most often cited by research in Modeling and Simulation (499 citations), Infectious Diseases (504 citations) and Health (223 citations). Filippo Trentini has collaborated with scholars based in Italy, United States and China. Frequent co-authors include Stefano Merler, Piero Poletti, Marco Ajelli, Giorgio Guzzetta, Valentina Marziano, Alessia Melegaro, Clelia Di Serio, Silvia Gregori, De’Broski R. Herbert and Monica E. Gianolini. Their work appears in journals such as Proceedings of the National Academy of Sciences, Nature Medicine 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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