Analytical assessment of workers' safety concerning direct and indirect ways of getting infected by dangerous pathogen
<|reference_start|>Analytical assessment of workers' safety concerning direct and indirect ways of getting infected by dangerous pathogen: The development of safety policies for protecting large groups of individuals working in indoor environments against disease spreading provides an important and challenging task. To address this issue, we investigate the scenario of workers getting infected by the dangerous airborne pathogen in a close to real-life industrial environment. We present the simple analytical model based on the observations made during the recent pandemic, and business expectations concerning the protection of workers. The model can be tuned to handle other epidemic or non-epidemic threads, including dangerous vapors from industrial processes. In the presented model, we consider direct and indirect ways of getting infected, the first by direct contact with an infected agent, and the second by contact with a contaminated environment, including air in compartments or working surfaces. Our analysis is based on the simplified droplet/aerosol spreading diffusion model, validated by droplets' spreading simulations. The model can be easily applied to new scenarios and has modest computational requirements compared with the simulations. Hence, the model can be applied in an automated protection ecosystem in the industrial environment, where the time for assessing danger is limited, and computation has to be performed almost in real time. Using a simple agent-based model, we confirm the general research conclusion on disease spreading. From our results, we draft a set of countermeasures for infection spreading, which could be used as the basis of the prevention policy, suitable for use in industrial scenarios.<|reference_end|>
@article{domino2024analytical,
    title={Analytical assessment of workers' safety concerning direct and indirect
  ways of getting infected by dangerous pathogen},
    author={Krzysztof Domino, Arkadiusz Sochan, Jaros{l}aw Adam Miszczak},
    journal={arXiv preprint arXiv:2409.16809},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.16809},
    primaryClass={cs.CE}
}