Thomas Neyens
Impact in
- Modeling and Simulation top 5%
- COVID-19 epidemiological studies
- Ecological Modeling top 10%
- Species Distribution and Climate Change
Papers in
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- Statistical Methods and Bayesian Inference 13
-
- Spatial and Panel Data Analysis 9
- Health Systems, Economic Evaluations, Quality of Life 4
- Co-authors
- Christel Faes (28 shared papers)Geert Molenberghs (20 shared papers)Tom Artois (6 shared papers)Geert Verbeke (11 shared papers)Natalie Beenaerts (9 shared papers)Wondwosen Kassahun (5 shared papers)Karen Smeets (3 shared papers)Ruben Evens (4 shared papers)
- Journals
- Spatial and Spatio-temporal Epidemiology (7 papers)Scientific Reports (4 papers)PLoS ONE (4 papers)Statistics in Medicine (3 papers)Statistical Modelling (2 papers)
- Partner nations
- BelgiumUnited StatesMyanmar
In The Last Decade
Thomas Neyens
45 papers receiving 441 citations
Peers
Comparison fields: 5 of 101
- Modeling and Simulation 62
- Ecological Modeling 44
- Statistics and Probability 68
- Health 37
- Ecology 74
Countries citing papers authored by Thomas Neyens
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
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-authors
The 25 scholars most cited alongside Thomas Neyens, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 55 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2018 | 36 | |
| 2 | 2019 | 36 | |
| 3 | 2021 | 32 | |
| 4 | 2014 | 30 | |
| 5 | 2014 | 27 | |
| 6 | 2011 | 23 | |
| 7 | 2020 | 21 | |
| 8 | 2022 | 19 | |
| 9 | 2020 | 18 | |
| 10 | 2014 | 18 | |
| 11 | 2020 | 18 | |
| 12 | 2016 | 16 | |
| 13 | 2023 | 14 | |
| 14 | 2012 | 14 | |
| 15 | 2013 | 13 | |
| 16 | 2020 | 10 | |
| 17 | 2021 | 10 | |
| 18 | 2019 | 9 | |
| 19 | 2021 | 8 | |
| 20 | 2022 | 8 |
About Thomas Neyens
Thomas Neyens is a scholar working on Statistics and Probability, Economics and Econometrics, Modeling and Simulation, Epidemiology and Ecology, having authored 55 papers that have together received 451 indexed citations. Recurring topics across this work include COVID-19 epidemiological studies (13 papers), Statistical Methods and Bayesian Inference (13 papers), Spatial and Panel Data Analysis (9 papers), Data-Driven Disease Surveillance (8 papers), Species Distribution and Climate Change (7 papers), Wildlife Ecology and Conservation (6 papers), Health Systems, Economic Evaluations, Quality of Life (4 papers) and Ecology and Vegetation Dynamics Studies (4 papers). The work is most often cited by research in Modeling and Simulation (62 citations), Ecological Modeling (44 citations), Statistics and Probability (68 citations), Health (37 citations) and Ecology (74 citations). Thomas Neyens has collaborated with scholars based in Belgium, United States and Myanmar. Frequent co-authors include Christel Faes, Geert Molenberghs, Tom Artois, Geert Verbeke, Natalie Beenaerts, Wondwosen Kassahun, Karen Smeets, Ruben Evens, Niel Hens and Koen Pepermans. Their work appears in journals such as Spatial and Spatio-temporal Epidemiology, Scientific Reports, PLoS ONE, Statistics in Medicine and Statistical Modelling.
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.