Michael T. Meehan

1.6k total citations · 1 hit paper
38 papers, 885 citations indexed

About

Michael T. Meehan is a scholar working on Modeling and Simulation, Infectious Diseases and Epidemiology. According to data from OpenAlex, Michael T. Meehan has authored 38 papers receiving a total of 885 indexed citations (citations by other indexed papers that have themselves been cited), including 18 papers in Modeling and Simulation, 16 papers in Infectious Diseases and 12 papers in Epidemiology. Recurrent topics in Michael T. Meehan's work include COVID-19 epidemiological studies (18 papers), Tuberculosis Research and Epidemiology (7 papers) and SARS-CoV-2 and COVID-19 Research (6 papers). Michael T. Meehan is often cited by papers focused on COVID-19 epidemiological studies (18 papers), Tuberculosis Research and Epidemiology (7 papers) and SARS-CoV-2 and COVID-19 Research (6 papers). Michael T. Meehan collaborates with scholars based in Australia, United States and Bangladesh. Michael T. Meehan's co-authors include Emma S. McBryde, Adeshina I. Adekunle, James M. Trauer, Denise L. Doolan, Yide Wong, John J. Miles, Scott R. Burrows, Diana P. Rojas, Romain Ragonnet and Oyelola A. Adegboye and has published in prestigious journals such as Nature Communications, PLoS ONE and Scientific Reports.

In The Last Decade

Michael T. Meehan

34 papers receiving 869 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.

Estimating the global burden of Epstein–Barr virus-related cancers

2021 163 citations Yide Wong, Michael T. Meehan et al. Journal of Cancer Research and Clinical Oncology profile →

Peers

Michael T. Meehan

PHEOH

EPIDE

ONCOL

Laura Skrip United States

Filippo Trentini Italy

Rodrigo Feliciano do Carmo Brazil

Oliver J. Watson United Kingdom

Steven Abrams Belgium

Michael V. Callahan United States

Ana Freitas Ribeiro Brazil

Julie Gutman United States

Megan O’Driscoll United Kingdom

Mircea T. Sofonea France

Laura Skrip United States

Michael T. Meehan

392 ×1.2

291 ×1.0

198 ×0.9

PHEOH

309 ×1.5

EPIDE

103 ×0.7

ONCOL

Citations per year, relative to Michael T. Meehan Michael T. Meehan (= 1×) peers Laura Skrip

Countries citing papers authored by Michael T. Meehan

Since Specialization

Citations

This map shows the geographic impact of Michael T. Meehan'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 Michael T. Meehan with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Michael T. Meehan more than expected).

Fields of papers citing papers by Michael T. Meehan

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Michael T. Meehan. 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 Michael T. Meehan. The network helps show where Michael T. Meehan may publish in the future.

Co-authorship network of co-authors of Michael T. Meehan

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

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

Ragonnet, Romain, Michael T. Meehan, Nouredine Melab, et al.. (2025). Estimating the impact of school closures on the COVID-19 dynamics in 74 countries: A modelling analysis. PLoS Medicine. 22(1). e1004512–e1004512.

Franklin, Richard C., Amy E. Peden, Blesson M. Varghese, et al.. (2025). Heatwaves and emergency department utilisation in Queensland: a 10-year retrospective study. BMC Health Services Research. 26(1). 7–7.

Hickson, Roslyn I., et al.. (2024). Reproducibility of COVID-era infectious disease models. Epidemics. 46. 100743–100743. 4 indexed citations

Trauer, James M., et al.. (2024). A data science pipeline applied to Australia's 2022 COVID-19 Omicron waves. Infectious Disease Modelling. 10(1). 99–109.

Bui, Viet L., et al.. (2024). Agent-based modelling of Mycobacterium tuberculosis transmission: a systematic review. BMC Infectious Diseases. 24(1). 1394–1394. 1 indexed citations

Adekunle, Adeshina I., et al.. (2023). Quantifying the impact of Wolbachia releases on dengue infection in Townsville, Australia. Scientific Reports. 13(1). 14932–14932. 14 indexed citations

Meehan, Michael T., et al.. (2023). Replicating superspreader dynamics with compartmental models. Scientific Reports. 13(1). 15319–15319. 3 indexed citations

Ragonnet, Romain, Kalaiarasu M. Peariasamy, Greg J. Fox, et al.. (2022). COVID-19 collaborative modelling for policy response in the Philippines, Malaysia and Vietnam. The Lancet Regional Health - Western Pacific. 29. 100563–100563. 3 indexed citations

Kuddus, Md Abdul, Emma S. McBryde, Adeshina I. Adekunle, Lisa J. White, & Michael T. Meehan. (2022). Mathematical analysis of a two-strain tuberculosis model in Bangladesh. Scientific Reports. 12(1). 3634–3634. 20 indexed citations

10.

Trauer, James M., David Pilcher, Michael T. Meehan, et al.. (2021). Understanding how Victoria, Australia gained control of its second COVID-19 wave. Nature Communications. 12(1). 6266–6266. 19 indexed citations

11.

Wong, Yide, Michael T. Meehan, Scott R. Burrows, Denise L. Doolan, & John J. Miles. (2021). Estimating the global burden of Epstein–Barr virus-related cancers. Journal of Cancer Research and Clinical Oncology. 148(1). 31–46. 163 indexed citations breakdown →

12.

Caldwell, Jamie M., Xuan Le, Michael T. Meehan, et al.. (2021). Vaccines and variants: Modelling insights into emerging issues in COVID-19 epidemiology. Paediatric Respiratory Reviews. 39. 32–39. 24 indexed citations

13.

Peariasamy, Kalaiarasu M., Linh‐Vi Le, Xuan Le, et al.. (2021). Sustaining effective COVID-19 control in Malaysia through large-scale vaccination. Epidemics. 37. 100517–100517. 9 indexed citations

14.

Kuddus, Md Abdul, Michael T. Meehan, Lisa J. White, Emma S. McBryde, & Adeshina I. Adekunle. (2020). Modeling drug-resistant tuberculosis amplification rates and intervention strategies in Bangladesh. PLoS ONE. 15(7). e0236112–e0236112. 28 indexed citations

15.

McBryde, Emma S., et al.. (2020). Mathematical analysis of a two-strain disease model with amplification. Oxford University Research Archive (ORA) (University of Oxford). 20 indexed citations

16.

Meehan, Michael T., Diana P. Rojas, Adeshina I. Adekunle, et al.. (2020). Modelling insights into the COVID-19 pandemic. Paediatric Respiratory Reviews. 35. 64–69. 40 indexed citations

17.

Adekunle, Adeshina I., et al.. (2020). Delaying the COVID‐19 epidemic in Australia: evaluating the effectiveness of international travel bans. Australian and New Zealand Journal of Public Health. 44(4). 257–259. 70 indexed citations

18.

Meehan, Michael T., Daniel Cocks, Johannes Müller, & Emma S. McBryde. (2019). Global stability properties of a class of renewal epidemic models. Journal of Mathematical Biology. 78(6). 1713–1725. 1 indexed citations

19.

Ragonnet, Romain, James M. Trauer, Nick Scott, et al.. (2017). Optimally capturing latency dynamics in models of tuberculosis transmission. Epidemics. 21. 39–47. 29 indexed citations

20.

Meehan, Michael T., Daniel Cocks, James M. Trauer, & Emma S. McBryde. (2017). Coupled, multi-strain epidemic models of mutating pathogens. Mathematical Biosciences. 296. 82–92. 20 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.

Explore authors with similar magnitude of impact