Thomas Neyens

687 total citations
52 papers, 421 citations indexed

About

Thomas Neyens is a scholar working on Economics and Econometrics, Modeling and Simulation and Statistics and Probability. According to data from OpenAlex, Thomas Neyens has authored 52 papers receiving a total of 421 indexed citations (citations by other indexed papers that have themselves been cited), including 16 papers in Economics and Econometrics, 14 papers in Modeling and Simulation and 14 papers in Statistics and Probability. Recurrent topics in Thomas Neyens's work include COVID-19 epidemiological studies (14 papers), Statistical Methods and Bayesian Inference (13 papers) and Spatial and Panel Data Analysis (11 papers). Thomas Neyens is often cited by papers focused on COVID-19 epidemiological studies (14 papers), Statistical Methods and Bayesian Inference (13 papers) and Spatial and Panel Data Analysis (11 papers). Thomas Neyens collaborates with scholars based in Belgium, United States and Myanmar. Thomas Neyens's co-authors include Christel Faes, Geert Molenberghs, Tom Artois, Geert Verbeke, Natalie Beenaerts, Wondwosen Kassahun, Karen Smeets, Ruben Evens, Niel Hens and Philippe Beutels and has published in prestigious journals such as SHILAP Revista de lepidopterología, PLoS ONE and The Science of The Total Environment.

In The Last Decade

Thomas Neyens

42 papers receiving 412 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Thomas Neyens Belgium 13 75 68 68 66 64 52 421
Carolina Perez‐Heydrich United States 12 25 0.3× 90 1.3× 34 0.5× 80 1.2× 26 0.4× 21 381
Alastair Rushworth United Kingdom 8 61 0.8× 25 0.4× 48 0.7× 81 1.2× 88 1.4× 8 365
Fabio Divino Italy 11 64 0.9× 24 0.4× 16 0.2× 44 0.7× 37 0.6× 27 263
Manabi Paul India 13 105 1.4× 66 1.0× 71 1.0× 122 1.8× 35 0.5× 23 564
Mark J. Meyer United States 9 28 0.4× 43 0.6× 32 0.5× 22 0.3× 43 0.7× 29 458
Gianfranco Lovison Italy 12 62 0.8× 51 0.8× 64 0.9× 41 0.6× 26 0.4× 39 337
Owen Lyne United Kingdom 12 167 2.2× 109 1.6× 16 0.2× 107 1.6× 89 1.4× 14 652
Carmen L. Vidal Rodeiro United Kingdom 10 35 0.5× 10 0.1× 42 0.6× 103 1.6× 82 1.3× 18 416
Juxin Liu Canada 13 33 0.4× 25 0.4× 49 0.7× 41 0.6× 33 0.5× 43 478
Rachelle N. Binny New Zealand 17 204 2.7× 162 2.4× 9 0.1× 57 0.9× 48 0.8× 44 605

Countries citing papers authored by Thomas Neyens

Since Specialization
Citations

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

Fields of papers citing papers by Thomas Neyens

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Thomas Neyens

This figure shows the co-authorship network connecting the top 25 collaborators of Thomas Neyens. A scholar is included among the top collaborators of Thomas Neyens 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 Thomas Neyens. Thomas Neyens 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.
Knijf, Geert De, Robby Stoks, Pieter Lemmens, et al.. (2025). Leveraging Massive Opportunistically Collected Datasets to Study Species Communities in Space and Time. Ecology Letters. 28(3). e70094–e70094. 2 indexed citations
2.
Faes, Christel, Philippe Beutels, Koen Pepermans, et al.. (2024). The effect of spatio-temporal sample imbalance in epidemiologic surveillance using opportunistic samples: An ecological study using real and simulated self-reported COVID-19 symptom data. Spatial and Spatio-temporal Epidemiology. 50. 100676–100676. 1 indexed citations
3.
Molenberghs, Geert, Johan Verbeeck, Lander Willem, et al.. (2024). Evaluating the direct effect of vaccination and non-pharmaceutical interventions during the COVID-19 pandemic in Europe. SHILAP Revista de lepidopterología. 4(1). 178–178. 1 indexed citations
4.
Verbeeck, Johan, et al.. (2024). Unraveling the impact of the COVID-19 pandemic on the mortality trends in Belgium between 2020–2022. BMC Public Health. 24(1). 2916–2916.
5.
Vaes, Bert, et al.. (2024). Model-based disease mapping using primary care registry data. Spatial and Spatio-temporal Epidemiology. 49. 100654–100654.
6.
Alonso, Ariel, et al.. (2024). A joint penalized spline smoothing model for the number of positive and negative COVID-19 tests. PLoS ONE. 19(5). e0303254–e0303254.
7.
Alonso, Ariel, et al.. (2023). A multivariate spatio-temporal model for the incidence of imported COVID-19 cases and COVID-19 deaths in Cuba. Spatial and Spatio-temporal Epidemiology. 45. 100588–100588.
8.
Neyens, Thomas, et al.. (2023). Impacts of zoning and landscape structure on the relative abundance of wild boar assessed through a Bayesian N-mixture model. The Science of The Total Environment. 911. 168546–168546. 1 indexed citations
9.
10.
Neyens, Thomas, et al.. (2023). Winter agri-environment schemes and local landscape composition influence the distribution of wintering farmland birds. Global Ecology and Conservation. 45. e02533–e02533. 2 indexed citations
11.
Aertgeerts, Bert, Nicolas Delvaux, Gijs Van Pottelbergh, et al.. (2023). The Impact of the COVID-19 Pandemic on the Registration and Care Provision of Mental Health Problems in General Practice: Registry-Based Study. JMIR Public Health and Surveillance. 9. e43049–e43049. 4 indexed citations
12.
Faes, Christel, et al.. (2023). Fractal dimension based geographical clustering of COVID-19 time series data. Scientific Reports. 13(1). 4322–4322. 2 indexed citations
13.
Geeraerts, Annelies, Livia Guadagnoli, Ans Pauwels, et al.. (2023). Psychological symptoms do not discriminate between reflux phenotypes along the organic-functional refractory GERD spectrum. Gut. 72(10). 1819–1827. 3 indexed citations
14.
Verbeeck, Johan, Christel Faes, Thomas Neyens, et al.. (2021). A linear mixed model to estimate COVID‐19‐induced excess mortality. Biometrics. 79(1). 417–425. 10 indexed citations
15.
16.
Valckx, Sara, Frederik Verelst, Greet Hendrickx, et al.. (2021). Individual factors influencing COVID-19 vaccine acceptance in between and during pandemic waves (July–December 2020). Vaccine. 40(1). 151–161. 30 indexed citations
17.
Molenberghs, Geert, Marc Buyse, Steven Abrams, et al.. (2020). Infectious diseases epidemiology, quantitative methodology, and clinical research in the midst of the COVID-19 pandemic: Perspective from a European country. Contemporary Clinical Trials. 99. 106189–106189. 18 indexed citations
18.
Neyens, Thomas, Peter J. Diggle, Christel Faes, et al.. (2019). Mapping species richness using opportunistic samples: a case study on ground-floor bryophyte species richness in the Belgian province of Limburg. Scientific Reports. 9(1). 19122–19122. 36 indexed citations
19.
Neyens, Thomas, Andrew Lawson, Russell S. Kirby, et al.. (2016). Disease mapping of zero-excessive mesothelioma data in Flanders. Annals of Epidemiology. 27(1). 59–66.e3. 16 indexed citations
20.
Kassahun, Wondwosen, Thomas Neyens, Geert Molenberghs, Christel Faes, & Geert Verbeke. (2012). Modeling overdispersed longitudinal binary data from the Jimma longitudinal studies using a combined beta and normal random-effects model. Lirias (KU Leuven). 2 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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