Markus Schwehm

1.1k total citations
36 papers, 708 citations indexed

About

Markus Schwehm is a scholar working on Epidemiology, Modeling and Simulation and Infectious Diseases. According to data from OpenAlex, Markus Schwehm has authored 36 papers receiving a total of 708 indexed citations (citations by other indexed papers that have themselves been cited), including 20 papers in Epidemiology, 18 papers in Modeling and Simulation and 6 papers in Infectious Diseases. Recurrent topics in Markus Schwehm's work include Influenza Virus Research Studies (19 papers), COVID-19 epidemiological studies (18 papers) and Respiratory viral infections research (8 papers). Markus Schwehm is often cited by papers focused on Influenza Virus Research Studies (19 papers), COVID-19 epidemiological studies (18 papers) and Respiratory viral infections research (8 papers). Markus Schwehm collaborates with scholars based in Germany, Belgium and United States. Markus Schwehm's co-authors include Martin Eichner, Hans-Peter Duerr, Stefan Brockmann, Laetitia Gerlier, Uwe Kubach, Alexander Leonhardi, Kurt Rothermel, Fritz Hohl, Ruprecht Schmidt‐Ott and Nick Wilson and has published in prestigious journals such as Vaccine, Epidemiology and Physica A Statistical Mechanics and its Applications.

In The Last Decade

Markus Schwehm

35 papers receiving 645 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Markus Schwehm Germany 15 322 264 150 125 115 36 708
Jiangzhuo Chen United States 18 321 1.0× 490 1.9× 111 0.7× 116 0.9× 62 0.5× 69 1.1k
Nita Bharti United States 12 490 1.5× 448 1.7× 66 0.4× 195 1.6× 136 1.2× 36 1.3k
George Milne Australia 20 431 1.3× 559 2.1× 85 0.6× 264 2.1× 85 0.7× 71 1.4k
Keith Bisset United States 19 224 0.7× 365 1.4× 214 1.4× 54 0.4× 20 0.2× 55 1.1k
Armin R. Mikler United States 12 180 0.6× 127 0.5× 109 0.7× 52 0.4× 49 0.4× 73 666
Duygu Balcan United States 13 589 1.8× 1.3k 4.8× 79 0.5× 203 1.6× 49 0.4× 21 2.1k
Maria Kazandjieva United States 8 112 0.3× 249 0.9× 465 3.1× 39 0.3× 13 0.1× 15 972
Marco Quaggiotto Italy 5 110 0.3× 222 0.8× 91 0.6× 46 0.4× 9 0.1× 5 718
Yuemei Hu China 21 418 1.3× 44 0.2× 141 0.9× 601 4.8× 56 0.5× 71 1.3k
A. G. McKendrick United Kingdom 6 132 0.4× 615 2.3× 39 0.3× 138 1.1× 33 0.3× 6 1.0k

Countries citing papers authored by Markus Schwehm

Since Specialization
Citations

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

Fields of papers citing papers by Markus Schwehm

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Markus Schwehm

This figure shows the co-authorship network connecting the top 25 collaborators of Markus Schwehm. A scholar is included among the top collaborators of Markus Schwehm 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 Markus Schwehm. Markus Schwehm 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.
Nautrup, B Poulsen, Ruprecht Schmidt‐Ott, Martin Eichner, et al.. (2022). Cost-utility analysis of increasing uptake of universal seasonal quadrivalent influenza vaccine (QIV) in children aged 6 months and older in Germany. Human Vaccines & Immunotherapeutics. 18(5). 2058304–2058304. 5 indexed citations
2.
Schneider, Kristan A., et al.. (2020). The COVID-19 pandemic preparedness simulation tool: CovidSIM. BMC Infectious Diseases. 20(1). 859–859. 20 indexed citations
3.
Eichner, Martin, et al.. (2017). Direct and indirect effects of influenza vaccination. BMC Infectious Diseases. 17(1). 308–308. 63 indexed citations
4.
Dolk, F Christiaan K, Martin Eichner, R Welte, et al.. (2016). Cost-Utility of Quadrivalent Versus Trivalent Influenza Vaccine in Germany, Using an Individual-Based Dynamic Transmission Model. PharmacoEconomics. 34(12). 1299–1308. 36 indexed citations
5.
Schwehm, Markus. (2016). A Fast Algorithm for Creating Turing-McCabe Patterns. 431–434. 1 indexed citations
6.
Gerlier, Laetitia, Mark Lamotte, Xavier Lenne, et al.. (2016). Assessment of Public Health and Economic Impact of Intranasal Live-Attenuated Influenza Vaccination of Children in France Using a Dynamic Transmission Model. Applied Health Economics and Health Policy. 15(2). 261–276. 10 indexed citations
7.
Schmidt‐Ott, Ruprecht, Markus Schwehm, & Martin Eichner. (2016). Influence of social contact patterns and demographic factors on influenza simulation results. BMC Infectious Diseases. 16(1). 646–646. 12 indexed citations
8.
Rose, Markus A., Oliver Damm, Wolfgang Greiner, et al.. (2014). The epidemiological impact of childhood influenza vaccination using live-attenuated influenza vaccine (LAIV) in Germany: predictions of a simulation study. BMC Infectious Diseases. 14(1). 40–40. 35 indexed citations
9.
Vidondo, Beatriz, et al.. (2012). Finding and removing highly connected individuals using suboptimal vaccines. BMC Infectious Diseases. 12(1). 51–51. 13 indexed citations
10.
Eichner, Martin, Markus Schwehm, Nick Wilson, & Michael G. Baker. (2009). Small islands and pandemic influenza: Potential benefits and limitations of travel volume reduction as a border control measure. BMC Infectious Diseases. 9(1). 160–160. 33 indexed citations
11.
Mikolajczyk, Rafael, Ralf Krumkamp, Reinhard Bornemann, et al.. (2009). Influenza. Deutsches Ärzteblatt international. 106(47). 777–82. 7 indexed citations
12.
Eichner, Martin, Markus Schwehm, Hans-Peter Duerr, et al.. (2009). Antiviral prophylaxis during pandemic influenza may increase drug resistance. BMC Infectious Diseases. 9(1). 4–4. 13 indexed citations
13.
Brockmann, Stefan, Markus Schwehm, Hans-Peter Duerr, et al.. (2008). Modeling the effects of drug resistant influenza virus in a pandemic. Virology Journal. 5(1). 133–133. 5 indexed citations
14.
Duerr, Hans-Peter, Stefan Brockmann, Isolde Piechotowski, Markus Schwehm, & Martin Eichner. (2007). Influenza pandemic intervention planning using InfluSim: pharmaceutical and non- pharmaceutical interventions. BMC Infectious Diseases. 7(1). 76–76. 37 indexed citations
15.
Schwehm, Markus, et al.. (2007). Tuning degree distributions: Departing from scale-free networks. Physica A Statistical Mechanics and its Applications. 382(2). 731–738. 5 indexed citations
16.
Eichner, Martin & Markus Schwehm. (2004). Smallpox. Epidemiology. 15(3). 258–260. 9 indexed citations
17.
Schwehm, Markus. (2001). Fast Stochastic Simulation of Metabolic Networks.. 201(4353). 223–226. 6 indexed citations
18.
Schwehm, Markus, et al.. (1998). Scheduling of parallel programs on configurable multiprocessors by genetic algorithms. International Journal of Approximate Reasoning. 19(1-2). 23–38. 7 indexed citations
19.
Schwehm, Markus, et al.. (1997). A Performance Model for Mobile Agent Systems.. Fachbereich Informatik (University of Stuttgart). 1132–1140. 69 indexed citations
20.
Schwehm, Markus, et al.. (1995). Inference of Stochastic Regular Grammars by Massively Parallel Genetic Algorithms. international conference on Genetic algorithms. 520–527. 8 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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