Python codes for deconstructing the effects of stochasticity on transmission of hospital-acquired infections in ICUs

The inherent stochasticity in transmission of hospital-acquired infections (HAIs) has complicated our understanding of transmission pathways. It is particularly difficult to detect the impact of changes in the environment on the acquisition rate due to stochasticity. In this study, we investigated the impact of uncertainty (epistemic and aleatory) on nosocomial transmission of HAIs by evaluating the effects of stochasticity on the detectability of seasonality on admission. For doing so, we developed an agent-based model of an ICU and simulated the acquisition of HAIs considering the uncertainties in the behavior of the healthcare workers (HCWs) and transmission of pathogens between patients, HCWs, and the environment. Our results show that stochasticity in HAI transmission weakens our ability to detect the effects of a change, such as seasonality, on the acquisition rate, particularly when transmission is a low-probability event. In addition, our findings demonstrate that data compilati..., No dataset was used in the paper. The Python code for the ABM is provided. For deatils of the code, please refer to the README document (README.md), and for the details of the methodology, please refer to the manuscript. [Manuscript currently under review by Royal Society Open Science], Any text-editor or MS Excel for input files and python (version 3.7 or higher) for the code files.