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Computational simulation models have been widely used to study the dynamics of COVID-19. Among those, bottom-up approaches such as agent-based models (ABMs) can account for population heterogeneity. While many studies have addressed COVID-19 spread at various scales, insufficient studies have investigated the spread of COVID-19 within closed indoor settings. This study aims to develop an ABM to simulate the spread of COVID-19 in a closed indoor setting using three transmission sub-models. Moreover, a comprehensive sensitivity analysis encompassing 4374 scenarios is performed. The model is calibrated using data from Calabria, Italy. The results indicated a decent consistency between the observed and predicted number of infected people (MAPE = 27.94%, RMSE = 0.87 and χ 2 (1 , N = 34) = (44.11 , p = 0.11) ). Notably, the transmission distance was identified as the most influential parameter in this model. In nearly all scenarios, this parameter had a significant impact on the outbreak dynamics (total cases and epidemic peak). Also, the calibration process showed that the movement of agents and the number of initial asymptomatic agents are vital model parameters to simulate COVID-19 spread accurately. The developed model may provide useful insights to investigate different scenarios and dynamics of other similar infectious diseases in closed indoor settings. [ABSTRACT FROM AUTHOR]

Dust storms are a severe form of air pollution that poses significant threats to the environment and human health. To deal with this phenomenon, it is crucial to comprehend the mechanisms accountable for dust generation. This can be achieved by utilizing machine learning in dust source susceptibility mapping. Although dust activities vary spatiotemporally due to the constantly changing atmosphere, few papers have addressed dust source susceptibility mapping considering Earth system frameworks such as wet and dry periods. Also, while optimizing hyperparameters is crucial for improving machine learning performance, many studies have neglected this aspect in this particular application. To address this research gap, the objective of this study was to create a framework for mapping the susceptibility of hazardous hotspot dust sources (HDS) during wet and dry periods (based on the changes in water bodies) using a fine-tuned random forest model with teaching learning-based optimization (TLBO) and student psychology based optimization (SPBO) optimizers. To achieve this, the study analyzed 10,392 identified HDS, along with various environmental influential factors between 2000 and 2020 in the transnational shared Tigris-Euphrates Basin, which is a significant source of dust in the Middle East and globally. The results showed that RF-TLBO performed slightly better than RF-SPBO, with an average mean absolute error (MAE) of 0.146, average root mean squared error (RMSE) of 0.194, and average Willmott Index (WI) of 0.761, compared to RF-SPBO's average MAE of 0.148, average RMSE of 0.195, and average WI of 0.757. The TLBO tuned RF with a lower number of trees and a lower maximum depth value, making it a simpler model. We utilized RF-TLBO and observed more areas that are more susceptible to hazardous dust sources during dry periods, confirming the meaningful relationship between wet and dry periods and hazardous dust susceptibility. Higher susceptibilities were found near water bodies and marshlands, indicating the significant impact of fluctuating water bodies on the generation of hazardous dust sources. The Gini index results also show that vegetation cover, elevation, wind speed, and soil texture have a high impact on land susceptibility to be a hazardous dust source. [ABSTRACT FROM AUTHOR]