Estimating droplet size and count distributions over a prolonged period of time following a cough in indoor environments

Abstract

An empirical correlation and a set of machine learning (ML) models were developed to estimate droplet size and count distributions over an extended duration after a cough at different relative humidities (RHs), air temperatures and locations within an indoor environment. Experiments covered RHs of 20%–80% and air temperatures of 21 °C–26 °C. Droplet count distributions for 4 size bins (0.3–0.5, 0.5–1, 1–3 and 3–5 μm) were recorded for 70 min within the distance of 2 m from the cough source. Different ML models, including decision tree, random forest and artificial neural network, were trained for each size bin to predict the associated count distribution. Amongst these models, random forest showed a slight superiority in performance. The coefficient of determination for the random forest models ranged from 0.912 to 0.989, indicating robust correlations between the features and the response variables. An empirical correlation was established linking the count distribution of 0.3–0.5 μm droplets to time, RH and distance along the cough direction. Both ML models and the correlation accurately predicted the trends and the distributions, providing valuable data for validating computational simulations and informing indoor environment control systems to reduce the risk of virus transmission.