Prof. James McCaw

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Professor In Mathematical Biology, School of Mathematics and Statistics; and Head of the Infectious Disease Dynamics Unit, Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health

University of Melbourne

James McCaw’s primary area of expertise is infectious disease dynamics, with research programs at both the within-host and epidemiological scale. Since 2015 he has held a split appointment between the School of Mathematics and Statistics and Melbourne School of Population and Global Health. From January 2020 to June 2021, James was Associate Dean (Research) in the Faculty of Science.

His research in mathematical biology focuses on host-pathogen interactions and within-host dynamics, with research projects in influenza virus dynamics, the immune response to influenza, and malaria infection dynamics.

His research in infectious diseases epidemiology and modelling has been fundamental to Australia’s planning and response to pandemics, including the COVID-19 pandemic. Since 2005 he has worked closely with the Commonwealth government to apply techniques from applied mathematics and data science to pandemic planning and response. His research has informed development of multiple editions of the Australian Health Management Plan for Pandemic Influenza. Since early 2020, he has led a team of applied mathematicians, epidemiologists, statisticians and biologists providing national real-time situational-assessment advice, including epidemic case and clinical forecasts on COVID-19, to government. He sat on the Australian Health Protection Principal Committee as an invited expert in infectious diseases epidemiology from January 2020 to May 2022 and is an invited expert member of the Communicable Diseases Network of Australia (CDNA).

Related Projects

• Modelling Report for National Cabinet

Related publications

• Moss, R., Price, D.J., Golding, N., Dawson, P., McVernon, J., Hyndman, R.J., Shearer, F.M. and McCaw, J.M., 2023. Forecasting COVID-19 activity in Australia to support pandemic response: May to October 2020. Scientific Reports, 13(1), p.8763.
• Golding, N., Price, D.J., Ryan, G., McVernon, J., McCaw, J.M. and Shearer, F.M., 2023. A modelling approach to estimate the transmissibility of SARS-CoV-2 during periods of high, low, and zero case incidence. eLife, 12, p.e78089.
• Tobin, R.J., Wood, J.G., Jayasundara, D., Sara, G., Walker, C.R., Martin, G.E., McCaw, J.M., Shearer, F.M. and Price, D.J., 2023. Real-time analysis of hospital length of stay in a mixed SARS-CoV-2 Omicron and Delta epidemic in New South Wales, Australia. BMC Infectious Diseases, 23(1), p.e28
• Shearer, F.M., Walker, J., Tellioglu, N., McCaw, J.M., McVernon, J., Black, A. and Geard, N., 2022. Rapid assessment of the risk of SARS-CoV-2 importation: Case study and lessons learned. Epidemics, 38, p.100549.
• Zachreson, C., Shearer, F.M., Price, D.J., Lydeamore, M.J., McVernon, J., McCaw, J. and Geard, N., 2022. COVID-19 in low-tolerance border quarantine systems: Impact of the Delta variant of SARS-CoV-2. Science Advances, 8(14), p.eabm3624.
• Baker, C.M., Campbell, P.T., Chades, I., Dean, A.J., Hester, S.M., Holden, M.H., McCaw, J.M., McVernon, J., Moss, R., Shearer, F.M. and Possingham, H.P., 2022. From climate change to pandemics: decision science can help scientists have impact. Frontiers in Ecology and Evolution, 10.
• Doherty Institute, 2021. Doherty Modelling – Final report to National Cabinet, 5 November. Available at https://www.doherty.edu.au/our-work/institute-themes/viral-infectious-diseases/covid-19/covid-19-modelling/modelling (Accessed 24 April 2023).
• Doherty Institute, 2021. Doherty Modelling – Final report to National Cabinet, 5 November. Available at https://www.doherty.edu.au/our-work/institute-themes/viral-infectious-diseases/covid-19/covid-19-modelling/modelling (Accessed 24 April 2023).
• Fielding, J., Sullivan, S.G., Beard, F., Macartney, K., Williams, J., Dawson, A., Gilbert, G.L., Massey, P., Crooks, K., Moss, R. and McCaw, J.M., 2021. Constructing an ethical framework for priority allocation of pandemic vaccines. Vaccine, 39(5), pp.797-804.
• Moss, R., Wood, J., Brown, D., Shearer, F.M., Black, A.J., Glass, K., Cheng, A.C., McCaw, J.M. and McVernon, J., 2020. Coronavirus disease model to inform transmission-reducing measures and health system preparedness, Australia. Emerging Infectious Diseases, 26(12), p.2844.
• Alahmadi, A., Belet, S., Black, A., Cromer, D., Flegg, J.A., House, T., Jayasundara, P., Keith, J.M., McCaw, J.M., Moss, R. and Ross, J.V., 2020. Influencing public health policy with data-informed mathematical models of infectious diseases: Recent developments and new challenges. Epidemics, 32, p.100393.
• Price, D.J., Shearer, F.M., Meehan, M.T., McBryde, E., Moss, R., Golding, N., Conway, E.J., Dawson, P., Cromer, D., Wood, J. and Abbott, S., 2020. Early analysis of the Australian COVID-19 epidemic. eLife, 9, p.e58785.
• Biggerstaff, M., Dahlgren, F.S., Fitzner, J., George, D., Hammond, A., Hall, I., Haw, D., Imai, N., Johansson, M.A., Kramer, S. and McCaw, J.M., 2020. Coordinating the real‐time use of global influenza activity data for better public health planning. Influenza and Other Respiratory Viruses, 14(2), pp.105-110.