Infectious diseases are a major global health challenge, claiming over 13 million lives every year. Despite the availability of interventions – such as vaccination, testing and treatment – determining the most effective strategy can be challenging, as this depends on complex factors like outbreak severity, funding limitations and sociopolitical influences. Computational modelling offers a powerful tool to assess the potential outcomes of different public health responses, but the difficulty of building epidemiological models from the ground up is a barrier to widespread use.

Atomica, a Python-based open source simulation package, addresses this challenge by simplifying the development of epidemiological models. It allows users to create highly configurable data-driven simulations that incorporate disease transmission, intervention strategies and budget constraints. With its user-friendly design and robust capabilities, Atomica allows us to forecast the potential consequences of public health strategies in specific settings. This insight enables policymakers and health organisations to make evidence-based choices, ultimately improving intervention effectiveness and saving lives.