Abstract
Purpose
The paper aims to apply the probabilistic analysis of risks, improve the prediction and control of infections and optimise the use of resources and the knowledge available at all times.
Design/methodology/approach
First, a model based on Bayesian inference, which can be solved with the WinBUGS (Windows interface Bayesian inference Using Gibbs Sampling) simulation software, is described to reduce the uncertainty of the parameter that most influences air transmission: the rate of quanta emitted by the infected. Second, a method for predicting the expected number of infections and combining available resources to reduce parameter is described.
Findings
The results indicate that it is possible to initiate a powerful learning process when all available knowledge is integrated alongside the newly observed data and that it is possible to quantify the interaction between the environment and the spaces, improving the communication process by providing the values in a format that facilitates the objective perception of danger.
Research limitations/implications
The implementation of the inference model requires access to the spaces where there were infected.
Practical implications
The current study provides a model and a method to improve the probabilistic analysis of risks, which allows the systematisation of the risk-based management approach to control community transmission caused by infectious agents that use the airway.
Social implications
The application of the risk assessment and treatment method requires collaboration between the parties that will help the effective implementation of the improvements, such as to verify whether the available resources are sufficient to achieve control.
Originality/value
A hierarchical Bayesian inference model is presented to control the uncertainty in the quanta rate. Bayesian inference initiates a learning process to better understand random uncertainty. A method to quantify and communicate risk was also presented, which proposes to decompose the risk into four components to predict the expected number of infected individuals, helping to implement improvement measures, with the resources and knowledge available.