Integrating data science with dynamic modelling of infectious diseases

The Epidemiological Modelling Unit (EMU) at the School of Public Health and Preventive Medicine is a multidisciplinary team that maintains advanced computational libraries to provide policy-relevant infectious disease modelling to policy makers across the Asia-Pacific Region. Prior to 2020, we worked closely with the Global Fund to Fight AIDS, TB and Malaria to model improved efficiency of tuberculosis (TB) programs in countries including Papua New Guinea, Uzbekistan, South Africa, India, China, Fiji, the Philippines, Bulgaria, Bhutan and Mongolia. Since the emergence of COVID-19, EMU has worked closely with the World Health Organization Western Pacific Regional Office and other public health agencies to support the COVID-19 response. EMU has played a key role supporting government health agencies in Malaysia and the Philippines, and has also provided modelling analysis to Victoria, the Northern Territory, Vietnam, Sri Lanka, Bhutan, Nepal, Indonesia, Bangladesh and Myanmar. As the basis of this high volume of modelling analyses, the EMU team has developed a range of sophisticated computation libraries to allow for analyses that are rapid, robust, transparent and integrated with the most commonly used computational libraries in the field of data science. In this project, the PhD candidate would take primary responsibility for the modelling analyses underpinning our analyses for topical public health questions being posed by our public health partners. The content of the PhD would be tailored to questions relating to the control of key infectious threats to Australia and our region, such as COVID-19, TB and others. The candidate would learn the interface for generating infectious disease dynamic models using our computational libraries, including “summer2” (available at https://github.com/monash-emu/summer2, including links to documentation and installation guidance). This project would suit applicants with an interest in developing advanced skills in applied mathematical modelling of infectious diseases. Key background would span fields including data science, complex systems analysis, software development and epidemiology, and confidence with the Python programming language is essential.