Projects

Near-real-time epidemic forecasting

This project combines mathematical/computational models of infectious disease epidemics with near-real-time surveillance data and other supporting data streams, in order to predict future disease activity. A key challenge is deciding how to interpret incomplete data. Publications arising from this project include:

• Forecasting COVID-19 activity in Australia to support pandemic response: May to October 2020, Moss et al., 2023.

• Early analysis of the Australian COVID-19 epidemic, Price et al., 2020.

• Coordinating the real-time use of global influenza activity data for better public health planning, Biggerstaff^† et al., 2020.

• Anatomy of a seasonal influenza epidemic forecast", Moss et al., 2019.

• Accounting for healthcare-seeking behaviours and testing practices in real-time influenza forecasts, Moss et al., 2019.

• Epidemic forecasts as a tool for public health: interpretation and (re)calibration, Moss et al., 2018.

• Model selection for seasonal influenza forecasting, Zarebski et al., 2017.

• Retrospective forecasting of the 2010–2014 Melbourne influenza seasons using multiple surveillance systems, Moss et al., 2017.

• Forecasting influenza outbreak dynamics in Melbourne from Internet search query surveillance data, Moss et al., 2016.

Informing pandemic preparedness and response plans

This project uses mathematical/computational models of infectious disease epidemics to assess the impact of public health interventions and how available resources (such as antiviral drugs and vaccines) might best be used. A key challenge is defining what "best" actually means. Publications arising from this project include:

• Constructing an ethical framework for priority allocation of pandemic vaccines, Sullivan et al., 2021.

• Priority allocation of pandemic influenza vaccines in Australia - recommendations of 3 Community Juries, Degeling et al., 2021.

• Coronavirus Disease Model to Inform Transmission Reducing Measures and Health System Preparedness, Australia, Moss et al., 2020.

• Reducing disease burden in an influenza pandemic by targeted delivery of neuraminidase inhibitors: mathematical models in the Australian context, Moss et al., 2016.

• Model-informed risk assessment and decision making for an emerging infectious disease in the Asia-Pacific region, Moss et al., 2016.

• Diagnosis and Antiviral Intervention Strategies for Mitigating an Influenza Epidemic, Moss et al., 2011.

Informing malaria guidelines

This project uses within-host and population models of malaria infection to inform diagnosis and treatment guidelines to reduce the burden of disease in areas where malaria prevalence remains high.

• The role of the spleen in red blood cell loss caused by malaria: a mathematical model, Moss et al., 2026.

• Within-host modeling of primaquine-induced hemolysis in hemizygote glucose-6-phosphate dehydrogenase deficient healthy volunteers, Watson et al., 2025.

Supporting response policy decision-making

This project links interpretation of near-real-time surveillance data to actions and decisions that are articulated in pandemic preparedness and response plans. A key challenge is appropriately accounting for, and communicating, uncertainty without causing decision paralysis. Publications arising from this project include:

• Development of an influenza pandemic decision support tool linking situational analytics to national response policy, Shearer et al., 2021.

• Infectious disease pandemic planning and response: Incorporating decision analysis, Shearer et al., 2020.