Mohammed Alahmadi

The COVID-19 pandemic greatly impacted socioeconomic life globally. Nighttime-lights (NTLs) data are mainly related to anthropogenic phenomena and thus have the ability to monitor changes in socioeconomic activity. However, the overglow effect is a source of uncertainty and affects the applicability of NTL data for accurately monitoring socioeconomic changes. This research integrates the NTL and fine bare-land-cover data to construct a novel index named the Bare Adjusted NTL Index (BANTLI) to lessen the overglow uncertainty. BANTLI was used to measure the post-pandemic resumption of religious rituals and socioeconomic activity in Makkah and Madinah at different spatial levels. The results demonstrate that BANTLI significantly eliminates the overglow effect. In addition, BANTLI brightness recovered during the post-pandemic periods, but it has remained below the level of the pre-pandemic period. Moreover, not all wards and rings are affected equally: wards and rings that are near the city center experienced the most explicit reduction of BANTLI brightness compared with the suburbs. The Hajj pilgrimage period witnessed a larger decrease in BANTLI brightness than the pandemic period in Makkah. The findings indicate that (i) BANTLI successfully mitigates the overglow effect in the NTL data, and (ii) the cultural context is important to understand the impact of COVID-19.

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 research provide fundamental guidance for decision-makers and planners in Oman and elsewhere to effectively monitor and manage desert urban dynamics and sustainable desert cities.

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.

The coronavirus disease (COVID-19) that appeared in 2019 gave rise to a major global health crisis that is still topping global health, socioeconomic, and intervention program agendas. Although the outbreak of COVID-19 has had substantial and devastating impacts on developed countries, the countries of the Global South share a higher proportion of the epidemic’s effects as shown particularly in morbidity and mortality rates in low-income countries. Modeling the effects of underlying factors and disease mortality is essential to plan effective control strategies for disease transmission and risks. The relationship between COVID-19 mortality rates and sociodemographic and health determinants can highlight various epidemic fatality risks. In this research, geographic information systems (GIS) and a multilayer perceptron (MLP) artificial neural network (ANN) were adopted to model and examine variations in COVID-19 mortality rates in the Global South. The model’s performance was tested using statistical measures of mean square error (MSE), root mean square error (RMSE), mean bias error (MBE), and the coefficient of determination (R²). The findings indicated that the most important variables in explaining spatial mortality rate variations were the size of the elderly (sixty-five and older) population, accessibility to handwashing facilities, and hospital beds per 1,000 population. Mapping the explanatory variables and estimated mortality rates and determining the importance of each variable in explaining the spatial variation of COVID-19 death rates across countries of the Global South can shed light on how public health care and demographic structures can offer policymakers invaluable guidelines to planning effective intervention strategies.

A novel coronavirus, COVID-19, appeared at the beginning of 2020 and within a few months spread worldwide. The COVID-19 pandemic had some of its greatest impacts on social, economic and religious activities. This study focused on the application of daily nighttime light (NTL) data (VNP46A2) to measure the spatiotemporal impact of the COVID-19 pandemic on the human lifestyle in Saudi Arabia at the national, province and governorate levels as well as on selected cities and sites. The results show that NTL brightness was reduced in all the pandemic periods in 2020 compared with a pre-pandemic period in 2019, and this was consistent with the socioeconomic results. An early pandemic period showed the greatest effects on the human lifestyle due to the closure of mosques and the implementation of a curfew. A slight improvement in the NTL intensity was observed in later pandemic periods, which represented Ramadan and Eid Alfiter days when Muslims usually increase the light of their houses. Closures of the two holy mosques in Makkah and Madinah affected the human lifestyle in these holy cities as well as that of Umrah pilgrims inside Saudi Arabia and abroad. The findings of this study confirm that the social and cultural context of each country must be taken into account when interpreting COVID-19 impacts, and that analysis of difference in nighttime lights is sensitive to these factors. In Saudi Arabia, the origin of Islam and one of the main sources of global energy, the preventive measures taken not only affected Saudi society; impacts spread further and reached the entire Islamic society and other societies, too.

Knowledge of the spatial pattern of the population is important. Census population data provide insufficient spatial information because they are released only for large geographic areas. Nighttime light (NTL) data have been utilized widely as an effective proxy for population mapping. However, the well-reported challenges of pixel overglow and saturation influence the applicability of the Defense Meteorological Program Operational Line-Scan System (DMSP-OLS) for accurate population mapping. This paper integrates three remotely sensed information sources, DMSP-OLS, vegetation, and bare land areas, to develop a novel index called the Vegetation-Bare Adjusted NTL Index (VBANTLI) to overcome the uncertainties in the DMSP-OLS data. The VBANTLI was applied to Riyadh province to downscale governorate-level census population for 2004 and 2010 to a gridded surface of 1 km resolution. The experimental results confirmed that the VBANTLI significantly reduced the overglow and saturation effects compared to widely applied indices such as the Human Settlement Index (HSI), Vegetation Adjusted Normalized Urban Index (VANUI), and radiance-calibrated NTL (RCNTL). The correlation coefficient between the census population and the RCNTL (R = 0.99) and VBANTLI (R = 0.98) was larger than for the HSI (R = 0.14) and VANUI (R = 0.81) products. In addition, Model 5 (VBANTLI) was the most accurate model with R² and mean relative error (MRE) values of 0.95% and 37%, respectively.

About 83% of laboratory-confirmed Middle East respiratory syndrome coronavirus (MERS-CoV) cases have emerged from Saudi Arabia, which has the highest overall mortality rate worldwide. This retrospective study assesses the impact of spatial/patient characteristics for 14-and 45-day MERS-CoV mortality using 2012–2019 data reported across Saudi regions and provinces. The Kaplan–Meier estimator was employed to estimate MERS-CoV survival rates, Cox proportional-hazards (CPH) models were applied to estimate hazard ratios (HRs) for 14-and 45-day mortality predictors, and univariate local spatial autocorrelation and multivariate spatial clustering analyses were used to assess the spatial correlation. The 14-day, 45-day and overall mortality rates (with estimated survival rates) were 25.52% (70.20%), 32.35% (57.70%) and 37.30% (56.50%), respectively, with no significant rate variations between Saudi regions and provinces. Nationally, the CPH multivariate model identified that being elderly (age ≥ 61), being a non-healthcare worker (non-HCW), and having an underlying comorbidity were significantly related to 14-day mortality (HR = 2.10, 10.12 and 4.11, respectively; p < 0.0001). The 45-day mortality model identified similar risk factors but with an additional factor: patients aged 41–60 (HR = 1.44; p < 0.0001). Risk factors similar to those in the national model were observed in the Central, East and West regions and Riyadh, Makkah, Eastern, Madinah and Qassim provinces but with varying HRs. Spatial clusters of MERS-CoV mortality in the provinces were identified based on the risk factors (r² = 0.85–0.97): Riyadh (Cluster 1), Eastern, Makkah and Qassim (Cluster 2), and other provinces in the north and south of the country (Cluster 3). The estimated HRs for the 14-and 45-day mortality varied spatially by province. For 45-day mortality, the highest HRs were found in Makkah (age ≥ 61 and non-HCWs), Riyadh (comorbidity) and Madinah (age 41–60). Coming from Makkah (HR = 1.30 and 1.27) or Qassim province (HR = 1.77 and 1.70) was independently related to higher 14-and 45-day mortality, respectively. MERS-CoV patient survival could be improved by implementing appropriate interventions for the elderly, those with comorbidities and non-HCW patients.