Marcelo Lobosco

955 total citations
55 papers, 449 citations indexed

About

Marcelo Lobosco is a scholar working on Modeling and Simulation, Molecular Biology and Cardiology and Cardiovascular Medicine. According to data from OpenAlex, Marcelo Lobosco has authored 55 papers receiving a total of 449 indexed citations (citations by other indexed papers that have themselves been cited), including 15 papers in Modeling and Simulation, 12 papers in Molecular Biology and 11 papers in Cardiology and Cardiovascular Medicine. Recurrent topics in Marcelo Lobosco's work include COVID-19 epidemiological studies (10 papers), SARS-CoV-2 and COVID-19 Research (8 papers) and Cardiac electrophysiology and arrhythmias (7 papers). Marcelo Lobosco is often cited by papers focused on COVID-19 epidemiological studies (10 papers), SARS-CoV-2 and COVID-19 Research (8 papers) and Cardiac electrophysiology and arrhythmias (7 papers). Marcelo Lobosco collaborates with scholars based in Brazil, United States and Australia. Marcelo Lobosco's co-authors include Rodrigo Weber dos Santos, Bernardo Martins Rocha, Rafael Sachetto Oliveira, Gilson Costa Macedo, Orlando Loques, Sergio Alonso, Wagner Meira, Jessica M. Conway, Alan S. Perelson and Jérémie Guedj and has published in prestigious journals such as Frontiers in Microbiology, BMC Bioinformatics and BioMed Research International.

In The Last Decade

Marcelo Lobosco

48 papers receiving 444 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Marcelo Lobosco Brazil 12 152 107 82 70 66 55 449
Robert HC Chen United States 9 75 0.5× 349 3.3× 35 0.4× 159 2.3× 22 0.3× 25 779
Zhenfeng Shi China 11 123 0.8× 75 0.7× 155 1.9× 83 1.2× 12 0.2× 41 448
Maoxing Liu China 20 317 2.1× 51 0.5× 376 4.6× 90 1.3× 34 0.5× 83 918
Anmol Mohan Pakistan 11 53 0.3× 133 1.2× 126 1.5× 37 0.5× 9 0.1× 32 343
Liuyong Pang China 11 262 1.7× 67 0.6× 217 2.6× 40 0.6× 16 0.2× 28 385
Padmanabhan Seshaiyer United States 16 172 1.1× 90 0.8× 110 1.3× 33 0.5× 12 0.2× 80 642
Deepanjan Bhattacharya India 15 31 0.2× 24 0.2× 189 2.3× 160 2.3× 37 0.6× 99 633
Himanshu Chauhan India 16 23 0.2× 62 0.6× 23 0.3× 56 0.8× 21 0.3× 76 700
Mairaj Bibi Pakistan 14 161 1.1× 27 0.3× 165 2.0× 21 0.3× 17 0.3× 25 381
Joseph S. Lombardo United States 12 62 0.4× 43 0.4× 84 1.0× 90 1.3× 25 0.4× 35 507

Countries citing papers authored by Marcelo Lobosco

Since Specialization
Citations

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

Fields of papers citing papers by Marcelo Lobosco

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Marcelo Lobosco

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

All Works

20 of 20 papers shown
1.
Lobosco, Marcelo, et al.. (2025). Influence of blood-related parameters for hyperthermia-based treatments for cancer. Journal of Computational Science. 87. 102556–102556.
2.
Santos, Rodrigo Weber dos, et al.. (2025). A Coupled Model of the Cardiovascular and Immune Systems to Analyze the Effects of COVID-19 Infection. BioTech. 14(1). 19–19. 1 indexed citations
3.
4.
Lobosco, Marcelo, et al.. (2023). On the use of a coupled mathematical model for understanding the dynamics of multiple sclerosis. Journal of Computational and Applied Mathematics. 428. 115163–115163. 4 indexed citations
5.
Ruíz-Baier, Ricardo, et al.. (2022). A Poroelastic Approach for Modelling Myocardial Oedema in Acute Myocarditis. Frontiers in Physiology. 13. 888515–888515. 5 indexed citations
6.
Oliveira, Rafael Sachetto, et al.. (2022). Timing the race of vaccination, new variants, and relaxing restrictions during COVID-19 pandemic. Journal of Computational Science. 61. 101660–101660. 13 indexed citations
7.
Olufsen, Mette S., et al.. (2021). A Mathematical Model of the Dynamics of Cytokine Expression and Human Immune Cell Activation in Response to the Pathogen Staphylococcus aureus. Frontiers in Cellular and Infection Microbiology. 11. 711153–711153. 11 indexed citations
8.
Vieira, Ana Carolina, et al.. (2021). A Validated Mathematical Model of the Cytokine Release Syndrome in Severe COVID-19. Frontiers in Molecular Biosciences. 8. 639423–639423. 22 indexed citations
9.
Oliveira, Rafael Sachetto, et al.. (2021). The Quixotic Task of Forecasting Peaks of COVID-19: Rather Focus on Forward and Backward Projections. Frontiers in Public Health. 9. 623521–623521. 5 indexed citations
10.
Martins, Reinaldo de Menezes, Luiz Antônio Bastos Camacho, Andréa Teixeira‐Carvalho, et al.. (2020). Validation of a yellow fever vaccine model using data from primary vaccination in children and adults, re-vaccination and dose-response in adults and studies with immunocompromised individuals. BMC Bioinformatics. 21(S17). 551–551. 6 indexed citations
11.
Rocha, Bernardo Martins, et al.. (2020). Characterization of the COVID-19 pandemic and the impact of uncertainties, mitigation strategies, and underreporting of cases in South Korea, Italy, and Brazil. Chaos Solitons & Fractals. 136. 109888–109888. 91 indexed citations
12.
Lobosco, Marcelo, et al.. (2019). Delay differential equation-based models of cardiac tissue: Efficient implementation and effects on spiral-wave dynamics. Chaos An Interdisciplinary Journal of Nonlinear Science. 29(12). 123128–123128. 1 indexed citations
13.
Fernandes, Juliano Lara, et al.. (2019). A personalized computational model of edema formation in myocarditis based on long-axis biventricular MRI images. BMC Bioinformatics. 20(S6). 532–532. 5 indexed citations
14.
Santos, Rodrigo Weber dos, et al.. (2018). A qualitatively validated mathematical-computational model of the immune response to the yellow fever vaccine. BMC Immunology. 19(1). 15–15. 16 indexed citations
15.
Conway, Jessica M., James M. Hyman, Jérémie Guedj, et al.. (2018). A New Age-Structured Multiscale Model of the Hepatitis C Virus Life-Cycle During Infection and Therapy With Direct-Acting Antiviral Agents. Frontiers in Microbiology. 9. 601–601. 14 indexed citations
16.
Missiakas, Dominique, et al.. (2018). Development of a Computational Model of Abscess Formation. Frontiers in Microbiology. 9. 1355–1355. 2 indexed citations
17.
Santos, Rodrigo Weber dos, et al.. (2017). A simplified mathematical-computational model of the immune response to the yellow fever vaccine. 2016. 1425–1432. 7 indexed citations
18.
Santos, Rodrigo Weber dos, et al.. (2015). Computational modeling of the immune response to yellow fever. Journal of Computational and Applied Mathematics. 295. 127–138. 9 indexed citations
19.
Lobosco, Marcelo, et al.. (2015). 3D numerical simulations on GPUs of hyperthermia with nanoparticles by a nonlinear bioheat model. Journal of Computational and Applied Mathematics. 295. 35–47. 37 indexed citations
20.
Macedo, Gilson Costa, et al.. (2013). On the computational modeling of the innate immune system. BMC Bioinformatics. 14(S6). S7–S7. 30 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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