Leonardo Clemente

405 total citations
9 papers, 138 citations indexed

About

Leonardo Clemente is a scholar working on Epidemiology, Modeling and Simulation and Public Health, Environmental and Occupational Health. According to data from OpenAlex, Leonardo Clemente has authored 9 papers receiving a total of 138 indexed citations (citations by other indexed papers that have themselves been cited), including 6 papers in Epidemiology, 6 papers in Modeling and Simulation and 3 papers in Public Health, Environmental and Occupational Health. Recurrent topics in Leonardo Clemente's work include COVID-19 epidemiological studies (6 papers), Data-Driven Disease Surveillance (6 papers) and Influenza Virus Research Studies (3 papers). Leonardo Clemente is often cited by papers focused on COVID-19 epidemiological studies (6 papers), Data-Driven Disease Surveillance (6 papers) and Influenza Virus Research Studies (3 papers). Leonardo Clemente collaborates with scholars based in United States, Mexico and Italy. Leonardo Clemente's co-authors include Mauricio Santillana, Fred Lu, Canelle Poirier, Matteo Chinazzi, Dianbo Liu, Alessandro Vespignani, Jessica T. Davis, J. Nathan Kutz, Sarah F. McGough and Caroline O. Buckee and has published in prestigious journals such as Proceedings of the National Academy of Sciences, SHILAP Revista de lepidopterología and Journal of Medical Internet Research.

In The Last Decade

Leonardo Clemente

9 papers receiving 133 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Leonardo Clemente United States	5	77	59	32	26	26	9	138
Nina Gorišek Miksić Slovenia	6	110 1.4×	46 0.8×	27 0.8×	9 0.3×	18 0.7×	9	221
Alan Siniscalchi United States	6	69 0.9×	86 1.5×	16 0.5×	7 0.3×	12 0.5×	12	143
Didier Darcet Switzerland	4	139 1.8×	24 0.4×	27 0.8×	12 0.5×	14 0.5×	5	187
Jamie A. Cohen United States	6	65 0.8×	42 0.7×	32 1.0×	9 0.3×	9 0.3×	13	175
David Farrow United States	4	155 2.0×	168 2.8×	24 0.8×	11 0.4×	26 1.0×	9	226
Corentin Cot France	5	116 1.5×	36 0.6×	27 0.8×	20 0.8×	5 0.2×	6	186
Asami Anzai Japan	6	161 2.1×	32 0.5×	19 0.6×	28 1.1×	12 0.5×	12	244
Manu Saraswat Canada	5	137 1.8×	33 0.6×	15 0.5×	8 0.3×	11 0.4×	5	256
Anoshé Aslam United States	8	47 0.6×	121 2.1×	11 0.3×	59 2.3×	25 1.0×	10	299
Tamás Tekeli Hungary	4	165 2.1×	28 0.5×	46 1.4×	12 0.5×	15 0.6×	4	257

Countries citing papers authored by Leonardo Clemente

Since Specialization

Citations

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

Fields of papers citing papers by Leonardo Clemente

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Leonardo Clemente

This figure shows the co-authorship network connecting the top 25 collaborators of Leonardo Clemente. A scholar is included among the top collaborators of Leonardo Clemente 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 Leonardo Clemente. Leonardo Clemente is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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9 of 9 papers shown

Meyer, Austin G., et al.. (2025). Ensemble approaches for short-term dengue fever forecasts: A global evaluation study. Proceedings of the National Academy of Sciences. 122(33). e2422335122–e2422335122. 1 indexed citations

Meyer, Austin G., Fred Lu, Leonardo Clemente, & Mauricio Santillana. (2025). A prospective real-time transfer learning approach to estimate influenza hospitalizations with limited data. Epidemics. 50. 100816–100816. 2 indexed citations

Clemente, Leonardo, et al.. (2025). Fine-grained forecasting of COVID-19 trends at the county level in the United States. npj Digital Medicine. 8(1). 204–204. 1 indexed citations

Lu, Fred, et al.. (2022). Predicting dengue incidence leveraging internet-based data sources. A case study in 20 cities in Brazil. PLoS neglected tropical diseases. 16(1). e0010071–e0010071. 9 indexed citations

McGough, Sarah F., Leonardo Clemente, J. Nathan Kutz, & Mauricio Santillana. (2021). A dynamic, ensemble learning approach to forecast dengue fever epidemic years in Brazil using weather and population susceptibility cycles. Journal of The Royal Society Interface. 18(179). 20201006–20201006. 31 indexed citations

Clemente, Leonardo, et al.. (2020). Adaptive-network-based Fuzzy Inference (anfis) Modelling of Particle Image Velocimetry (piv) Measurements in Stirred Tank Reactors. SHILAP Revista de lepidopterología. 79. 1–6. 2 indexed citations

Liu, Dianbo, Leonardo Clemente, Canelle Poirier, et al.. (2020). Correction: Real-Time Forecasting of the COVID-19 Outbreak in Chinese Provinces: Machine Learning Approach Using Novel Digital Data and Estimates From Mechanistic Models. Journal of Medical Internet Research. 22(9). e23996–e23996. 28 indexed citations

Liu, Dianbo, Leonardo Clemente, Canelle Poirier, et al.. (2020). Real-Time Forecasting of the COVID-19 Outbreak in Chinese Provinces: Machine Learning Approach Using Novel Digital Data and Estimates From Mechanistic Models. Journal of Medical Internet Research. 22(8). e20285–e20285. 44 indexed citations

Clemente, Leonardo, Fred Lu, & Mauricio Santillana. (2019). Improved Real-Time Influenza Surveillance: Using Internet Search Data in Eight Latin American Countries. JMIR Public Health and Surveillance. 5(2). e12214–e12214. 20 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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