Eloisa Perez-Bennetts
Hi! I’m Eloisa, a mathematical modeller with a passion for coding.
I studied Physics at Sydney University, with a strong focus on Computer Science and Biotechnology. My research there was on nanobots for brain medicine, and involved developing ML algorithms to analyse brain maps, as well as making molecular logic gates in a lab.
I have been developing and using compartmental models since 2021 – first, in the field of mental health, and now in the context of epidemics. My current work focuses on evaluating the future impact of public health interventions and novel medications, and sometimes also translating reports into Spanish :)
Session
Infectious diseases are a major global threat, claiming millions of lives every year. While there are many interventions available for managing epidemics (including vaccines, tests and treatments) the best approach is not always clear, as the optimal response may vary significantly depending on outbreak severity, funding available, and even sociopolitical context.
Computational modelling is a highly effective tool to examine the potential impact of different public health interventions. However, the technical difficulty of building fit-for-purpose models that can simulate these impacts is a barrier to widespread use.
Atomica is an open-source Python-based simulation engine for compartmental modelling. It provides an easy-to-use yet highly configurable way to build disease models, with inbuilt support for public health interventions. By leveraging population, transmission, and public health intervention data, we can forecast the potential consequences of different public health strategies in specific countries or settings, helping governments to make the best possible decisions on what to prioritise.
Whether you’re a beginner or an expert Pythonista, this talk will equip you with the tools to simulate a real-world epidemic and project its future impact.