Maximilian Marshall

1.6k total citations · 1 hit paper
6 papers, 641 citations indexed

About

Maximilian Marshall is a scholar working on Modeling and Simulation, Epidemiology and Infectious Diseases. According to data from OpenAlex, Maximilian Marshall has authored 6 papers receiving a total of 641 indexed citations (citations by other indexed papers that have themselves been cited), including 4 papers in Modeling and Simulation, 3 papers in Epidemiology and 1 paper in Infectious Diseases. Recurrent topics in Maximilian Marshall's work include COVID-19 epidemiological studies (4 papers), Data-Driven Disease Surveillance (3 papers) and SARS-CoV-2 and COVID-19 Research (1 paper). Maximilian Marshall is often cited by papers focused on COVID-19 epidemiological studies (4 papers), Data-Driven Disease Surveillance (3 papers) and SARS-CoV-2 and COVID-19 Research (1 paper). Maximilian Marshall collaborates with scholars based in United States and Taiwan. Maximilian Marshall's co-authors include Lauren Gardner, Ensheng Dong, Hongru Du, Hamada S. Badr, Marietta M. Squire, Kristen Nixon, Arash Mohegh, Margaret Kosek, Nicholas G Reich and Daniel L. Goldberg and has published in prestigious journals such as SHILAP Revista de lepidopterología, The Journal of Urology and The Lancet Infectious Diseases.

In The Last Decade

Maximilian Marshall

6 papers receiving 629 citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Association between mobility patterns and COVID-19 transmission in the USA: a mathematical modelling study

2020 577 citations Hamada S. Badr, Hongru Du et al. The Lancet Infectious Diseases profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Maximilian Marshall United States	4	445	191	163	156	86	6	641
Marietta M. Squire United States	4	424 1.0×	188 1.0×	144 0.9×	154 1.0×	76 0.9×	5	597
Ensheng Dong United States	7	501 1.1×	224 1.2×	215 1.3×	158 1.0×	152 1.8×	12	841
Liangcai Zhou China	5	618 1.4×	274 1.4×	186 1.1×	101 0.6×	220 2.6×	12	906
David Martín-Corral Spain	3	448 1.0×	116 0.6×	155 1.0×	89 0.6×	140 1.6×	4	593
Behzad Vahedi United States	6	409 0.9×	252 1.3×	177 1.1×	76 0.5×	61 0.7×	9	698
Weiyu Luo United States	6	286 0.6×	106 0.6×	124 0.8×	246 1.6×	26 0.3×	12	519
Alessandra Carioli United Kingdom	11	213 0.5×	94 0.5×	67 0.4×	80 0.5×	93 1.1×	16	495
Michael R. Desjardins United States	13	440 1.0×	148 0.8×	368 2.3×	75 0.5×	68 0.8×	40	767
Alexander Hohl United States	11	347 0.8×	131 0.7×	301 1.8×	94 0.6×	37 0.4×	30	670

Countries citing papers authored by Maximilian Marshall

Since Specialization

Citations

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

Fields of papers citing papers by Maximilian Marshall

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Maximilian Marshall

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

All Works

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6 of 6 papers shown

1.

Marshall, Maximilian, et al.. (2024). When are predictions useful? A new method for evaluating epidemic forecasts. SHILAP Revista de lepidopterología. 2(1). 67–67. 1 indexed citations

2.

Badr, Hamada S., Benjamin F. Zaitchik, Gaige Hunter Kerr, et al.. (2023). Unified real-time environmental-epidemiological data for multiscale modeling of the COVID-19 pandemic. Scientific Data. 10(1). 367–367. 15 indexed citations

3.

Nixon, Kristen, Maximilian Marshall, Nicholas G Reich, et al.. (2022). Real-time COVID-19 forecasting: challenges and opportunities of model performance and translation. The Lancet Digital Health. 4(10). e699–e701. 13 indexed citations

4.

Badr, Hamada S., Hongru Du, Maximilian Marshall, et al.. (2020). Association between mobility patterns and COVID-19 transmission in the USA: a mathematical modelling study. The Lancet Infectious Diseases. 20(11). 1247–1254. 577 indexed citations breakdown →

5.

Marshall, Maximilian. (1986). Ultrastructural findings on platelet depositions in initial atherogenesis.. PubMed. 98(7). 212–4. 2 indexed citations

6.

Marshall, Maximilian, et al.. (1972). Plasma Cell Granuloma of the Renal Pelvis. The Journal of Urology. 107(6). 938–939. 33 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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