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.

翻译：为保护室内工作环境中大规模人群免受疾病传播而制定安全政策，是一项重要且具有挑战性的任务。针对这一问题，我们研究了工人在接近真实工业环境中受危险空气传播病原体感染的场景。基于近期疫情期间的观察数据及企业对工人防护的预期，我们提出了一个简明的分析模型。该模型可通过参数调整处理其他流行病或非流行病威胁，包括工业过程中产生的有害蒸汽。在所提出的模型中，我们考虑了直接与间接两种感染途径：前者通过直接接触感染源，后者通过接触受污染环境（包括隔间空气或工作台面）。我们的分析基于经液滴扩散模拟验证的简化液滴/气溶胶扩散模型。该模型可轻松应用于新场景，且与仿真模拟相比计算需求较低。因此，该模型适用于工业环境中的自动化防护系统，此类场景中危险评估时间有限，需近乎实时完成计算。通过使用简单的基于智能体的模型，我们验证了关于疾病传播的总体研究结论。基于研究结果，我们拟定了一套适用于工业场景的感染传播应对措施，可作为预防政策制定的基础。