How to Stop an Epidemic using the Atomica Python Tool

Infectious diseases are the third leading cause of death worldwide, claiming more than 13 million lives every year. While there are many interventions available for managing epidemics (like vaccination, testing and treatments), the best approach is not always clear. There are complex dynamics at play, and the optimal response may vary significantly depending on outbreak severity, funding available, and even sociopolitical context.

Computational modelling is a powerful tool for evaluating the potential impact of different public health approaches, but the difficulty of building fit-for-purpose epidemiological models from scratch is a barrier to widespread use.

Atomica, an open source Python-based simulation engine, aims to make this kind of modelling more accessible. It provides an easy-to-use yet highly configurable way to build disease models, with built-in support for public health interventions and optimisation with budget constraints. By leveraging population, transmission, and intervention data, as well as conceptual knowledge of how people progress through stages of disease, this package allows us to forecast the potential consequences of public health strategies in specific countries or settings. With this insight, we can help governments and health organisations to make the best possible decisions on what to prioritise, saving many lives in the process.

Whether you’re a beginner or an expert Pythonista, this talk will equip you with the tools to simulate a real-world typhoid epidemic and optimise funding distribution for maximum impact.