James M. McCaw

4.8k total citations
143 papers, 2.9k citations indexed

About

James M. McCaw is a scholar working on Modeling and Simulation, Epidemiology and Public Health, Environmental and Occupational Health. According to data from OpenAlex, James M. McCaw has authored 143 papers receiving a total of 2.9k indexed citations (citations by other indexed papers that have themselves been cited), including 82 papers in Modeling and Simulation, 81 papers in Epidemiology and 42 papers in Public Health, Environmental and Occupational Health. Recurrent topics in James M. McCaw's work include COVID-19 epidemiological studies (82 papers), Influenza Virus Research Studies (72 papers) and Malaria Research and Control (23 papers). James M. McCaw is often cited by papers focused on COVID-19 epidemiological studies (82 papers), Influenza Virus Research Studies (72 papers) and Malaria Research and Control (23 papers). James M. McCaw collaborates with scholars based in Australia, United Kingdom and Canada. James M. McCaw's co-authors include Jodie McVernon, J. A. Simpson, Pengxing Cao, John D. Mathews, Robert Moss, Sophie Zaloumis, Ian Barr, Nectarios Klonis, Leann Tilley and Emma S. McBryde and has published in prestigious journals such as Proceedings of the National Academy of Sciences, SHILAP Revista de lepidopterología and PLoS ONE.

In The Last Decade

James M. McCaw

136 papers receiving 2.8k citations

Peers

James M. McCaw

EPIDE

PHEOH

IMMUN

Lisa J. White United Kingdom

Ranjit Sah Nepal

Mélanie Saville France

Marta Giovanetti Brazil

Maria D. Van Kerkhove United Kingdom

D. Katterine Bonilla‐Aldana Colombia

Catharine I. Paules United States

KH Chan China

Philip H. Li China

Neeltje van Doremalen United States

Lisa J. White United Kingdom

James M. McCaw

1.1k ×0.8

EPIDE

504 ×0.5

2.6k ×2.9

PHEOH

859 ×1.2

235 ×0.8

IMMUN

Citations per year, relative to James M. McCaw James M. McCaw (= 1×) peers Lisa J. White

Countries citing papers authored by James M. McCaw

Since Specialization

Citations

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

Fields of papers citing papers by James M. McCaw

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by James M. McCaw. 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 James M. McCaw. The network helps show where James M. McCaw may publish in the future.

Co-authorship network of co-authors of James M. McCaw

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

All Works

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20 of 20 papers shown

Hao, Tianxiao, Gerard E. Ryan, Deborah Cromer, et al.. (2025). Predicting immune protection against outcomes of infectious disease from population-level effectiveness data with application to COVID-19. Vaccine. 55. 126987–126987. 1 indexed citations

Walker, James R., et al.. (2025). A modular approach to forecasting COVID-19 hospital bed occupancy. Communications Medicine. 5(1). 349–349. 1 indexed citations

McCaw, James M., et al.. (2024). Investigation of P. vivax elimination via mass drug administration: A simulation study. Epidemics. 48. 100789–100789.

Shearer, Freya M., James M. McCaw, Gerard E. Ryan, et al.. (2024). Estimating the impact of test–trace–isolate–quarantine systems on SARS-CoV-2 transmission in Australia. Epidemics. 47. 100764–100764. 3 indexed citations

Smith, Lauren M., Angela Devine, Somya Mehra, et al.. (2024). Mathematical models of Plasmodium vivax transmission: A scoping review. PLoS Computational Biology. 20(3). e1011931–e1011931. 4 indexed citations

Hickson, Roslyn I., Somya Mehra, David J. Price, et al.. (2023). Optimal Interruption of P. vivax Malaria Transmission Using Mass Drug Administration. Bulletin of Mathematical Biology. 85(6). 43–43. 6 indexed citations

Walker, James R., Roslyn I. Hickson, Elizabeth Chang, et al.. (2023). A model for malaria treatment evaluation in the presence of multiple species. Epidemics. 44. 100687–100687. 3 indexed citations

McCaw, James M., et al.. (2023). Enhanced viral infectivity and reduced interferon production are associated with high pathogenicity for influenza viruses. PLoS Computational Biology. 19(2). e1010886–e1010886. 2 indexed citations

Zachreson, Cameron, Freya M. Shearer, David J. Price, et al.. (2022). COVID-19 in low-tolerance border quarantine systems: Impact of the Delta variant of SARS-CoV-2. Science Advances. 8(14). eabm3624–eabm3624. 16 indexed citations

10.

Hickson, Roslyn I., et al.. (2022). A Multiscale Mathematical Model of Plasmodium Vivax Transmission. Bulletin of Mathematical Biology. 84(8). 81–81. 6 indexed citations

11.

Shearer, Freya M., James R. Walker, James M. McCaw, et al.. (2022). Rapid assessment of the risk of SARS-CoV-2 importation: case study and lessons learned. Epidemics. 38. 100549–100549. 5 indexed citations

12.

McCaw, James M., et al.. (2022). Stochastic Modeling of Within-Host Dynamics of Plasmodium Falciparum. Mathematics. 10(21). 4057–4057. 1 indexed citations

13.

Campbell, Patricia T., David J. Price, Yue Wu, et al.. (2020). Estimation of the force of infection and infectious period of skin sores in remote Australian communities using interval-censored data. PLoS Computational Biology. 16(10). e1007838–e1007838. 7 indexed citations

14.

Dini, S, Sophie Zaloumis, Pengxing Cao, et al.. (2018). Investigating the Efficacy of Triple Artemisinin-Based Combination Therapies for Treating Plasmodium falciparum Malaria Patients Using Mathematical Modeling. Antimicrobial Agents and Chemotherapy. 62(11). 59 indexed citations

15.

Campbell, Patricia T., Ross Andrews, Thérèse Kearns, et al.. (2018). Calculation of the age of the first infection for skin sores and scabies in five remote communities in northern Australia. Epidemiology and Infection. 146(9). 1194–1201. 9 indexed citations

16.

Campbell, Patricia T., David G. Regan, Steven Y. C. Tong, et al.. (2018). A biological model of scabies infection dynamics and treatment informs mass drug administration strategies to increase the likelihood of elimination. Mathematical Biosciences. 309. 163–173. 19 indexed citations

17.

Yan, Ada W. C., et al.. (2018). The distribution of the time taken for an epidemic to spread between two communities. Mathematical Biosciences. 303. 139–147. 6 indexed citations

18.

Yang, Tuo, Stanley C. Xie, Pengxing Cao, et al.. (2016). Comparison of the Exposure Time Dependence of the Activities of Synthetic Ozonide Antimalarials and Dihydroartemisinin against K13 Wild-Type and Mutant Plasmodium falciparum Strains. Antimicrobial Agents and Chemotherapy. 60(8). 4501–4510. 42 indexed citations

19.

Laurie, Karen, Teagan Guarnaccia, Louise Carolan, et al.. (2016). The time-interval between infections and viral hierarchies are determinants of viral interference following influenza virus infection in a ferret model. European Journal of Immunology. 46. 3 indexed citations

20.

Mathews, John D., et al.. (2011). Proof of principle for an immunological model to explain mortality variations over the three waves of the 1918-1919 pandemic. Influenza and Other Respiratory Viruses. 5. 1 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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