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Migrants are among the groups most vulnerable to infection with viruses due to the social and economic conditions in which they live. Therefore, spatial modeling of virus transmission among migrants is important for controlling and containing the COVID-19 pandemic. This research focused on modeling spatial associations between COVID-19 incidence rates and migrant workers. The aim was to understand the spatial relationships between COVID-19 infection rates of migrants and the type of workplace at the subnational level in Oman. Using empirical Bayes smoothing as well as local indicators of spatial associations, six work sectors (health, agriculture, retail and business, administrative, manufacturing, and mining) were investigated as risk factors for disease incidence. The results indicated that the six work sectors each had a significant spatial association with cases of COVID-19. High rates of COVID-19 cases in relation to the workplace were clustered in the densely populated areas of Muscat. Similarly, high rates of COVID-19 cases were located in the northern part of the country, along the Al-Batnah plain, where migrants are often employed in the agricultural sector. Further, the rate of COVID-19 in migrants employed in the health sector was higher than that for the other sectors. Therefore, working in the health sector can be considered a hot spot for the spread of COVID-19 infections. Due to a paucity of studies addressing the spatial analysis of COVID-19 associations with workplaces, the findings of this research are useful for decision makers to set the necessary policies and plans to control the outbreak of the virus not only in Oman or the Gulf Cooperation Council but also in other developing societies.

In recent years, socioeconomic transformation and social modernisation in the Gulf Cooperation Council (GCC) states have led to tremendous changes in lifestyle and, subsequently, expansion of urban settlements. This accelerated growth is pronounced not only across vegetated coasts, plains, and mountains, but also in desert cities. Nevertheless, spatial simulation and prediction of desert urban patterns has received little attention, including in Oman. While most urban settlements in Oman are located in desert environments, research exploring and monitoring this type of urban growth is rare in the scientific literature. This research focuses on analysing and predicting land use−land cover (LULC) changes across the desert city of Ibri in Oman. A methodology was employed involving integrating the multilayer perceptron (MLP) and Markov chain (MC) techniques to forecast spatiotemporal LULC dynamics and map urban growth patterns. The inputs were three Landsat images from 2010 and 2020, and a series of covariate layers based on transforms of elevation, slope, population settlements, urban centres, and points of interest that proxy the driving forces of change. The findings indicated that the observed LULC changes were predominantly rapid across the city during 2010 to 2020, transforming desert, bare land, and vegetation into built-up areas. The forecast showed that area of land conversion from desert to urban would be 5666 ha during the next two decades and 7751 ha by 2050. Similarly, vacant land is expected to contribute large areas to urban expansion (2370 ha by 2040, and 3266 ha by 2050), although desert cities confront numerous environmental challenges, including water scarcity, shrinking vegetation cover, and being converted into residential land. Massive urban expansion has consequences for biodiversity and natural ecosystems—particularly in green areas, which are expected to decline by approximately 107 ha by 2040 (i.e., 10%) and 166 ha by 2050. The outcomes of this

Migrants are among the groups most vulnerable to infection with viruses due to the social and economic conditions in which they live. Therefore, spatial modeling of virus transmission among migrants is important for controlling and containing the COVID-19 pandemic. This research focused on modeling spatial associations between COVID-19 incidence rates and migrant workers. The aim was to understand the spatial relationships between COVID-19 infection rates of migrants and the type of workplace at the subnational level in Oman. Using empirical Bayes smoothing as well as local indicators of spatial associations, six work sectors (health, agriculture, retail and business, administrative, manufacturing, and mining) were investigated as risk factors for disease incidence. The results indicated that the six work sectors each had a significant spatial association with cases of COVID-19. High rates of COVID-19 cases in relation to the workplace were clustered in the densely populated areas of Muscat. Similarly, high rates of COVID-19 cases were located in the northern part of the country, along the Al-Batnah plain, where migrants are often employed in the agricultural sector. Further, the rate of COVID-19 in migrants employed in the health sector was higher than that for the other sectors. Therefore, working in the health sector can be considered a hot spot for the spread of COVID-19 infections. Due to a paucity of studies addressing the spatial analysis of COVID-19 associations with workplaces, the findings of this research are useful for decision makers to set the necessary policies and plans to control the outbreak of the virus not only in Oman or the Gulf Cooperation Council but also in other developing societies.