I am preparing capstone projects for the Master of Data Science that build on data, methods, and outcomes from the research projects described below.
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:
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.
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.
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: