Analytical assessment of workers' safety concerning direct and indirect ways of getting infected by dangerous pathogen

TL;DR

This paper presents an analytical model for assessing infection risks among industrial workers from airborne pathogens, considering both direct and indirect transmission, enabling rapid safety policy development.

Contribution

It introduces a simple, adaptable analytical model based on COVID-19 observations that can be used for real-time risk assessment and prevention policy formulation in industrial environments.

Findings

Validated droplet spread model with simulations

Model enables rapid danger assessment in industrial settings

Proposed countermeasures for infection prevention

Abstract

Developing safety policies to protect large groups of individuals working in indoor environments from disease spread is an important and challenging task. To address this issue, we investigate the scenario of workers becoming infected by a dangerous airborne pathogen in a near-real-life industrial environment. We present a simple analytical model based on observations made during the recent COVID-19 pandemic and business expectations concerning worker protection. The model can be adapted to address other epidemic or non-epidemic threats, including hazardous vapors from industrial processes. In the presented model, we consider both direct and indirect modes of infection. Direct infection occurs through direct contact with an infected individual, while indirect infection results from contact with a contaminated environment, including airborne pathogens in enclosed spaces or contaminated surfaces. Our analysis utilizes a simplified droplet/aerosol diffusion model, validated by droplet spread simulations. This model can be easily applied to new scenarios and has modest computational requirements compared to full simulations. Thus, it can be implemented within an automated protection ecosystem in an industrial setting, where rapid assessment of potential danger is required, and calculations must be performed almost in real-time. We validate general research findings on disease spread using a simple agent-based model. Based on our results, we outline a set of countermeasures for infection prevention, which could serve as the foundation for a prevention policy suited to industrial scenarios.