Learning from COVID-19 to design better infectious diseases control using mathematical modelling

Infectious diseases continue to pose significant threats to public health globally. The recent COVID-19 pandemic has highlighted the need for effective control strategies to mitigate the impact of such outbreaks. This project aims to leverage the lessons learned from the COVID-19 crisis and utilise mathematical modeling techniques to design improved infectious disease control measures. By applying mathematical modeling to analyse the spread and impact of infectious diseases, we can develop evidence-based interventions and strategies that can enhance public health preparedness and response. The project aims to achieve the following objectives: - Analyse and model the transmission dynamics of COVID-19 to identify key factors influencing disease spread. - Develop mathematical models that incorporate various control measures employed during the COVID-19 pandemic, such as social distancing, mask-wearing, vaccination, and testing. - Validate the developed models using real-world data from the COVID-19 pandemic and other infectious disease outbreaks. - Assess the effectiveness of different control strategies and interventions through simulation studies. - Identify knowledge gaps and areas for improvement in infectious disease control based on the lessons learned from COVID-19. - Provide modelling-based recommendations for policymakers and public health authorities to enhance infectious disease control measures. The student will be supported to use the state-of-the-art modelling tools developed by my team. A minimum knowledge of programming is required and experience with Python would be preferable.