https://orcid.org/0000-0001-6969-9188
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Abstract
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.
由于生活的社会经济条件，移民是最容易感染病毒的群体之一。因此，对移民群体之内病毒传播的空间建模，对COVID-19疾病控制具有重要意义。本研究对COVID-19发病率和移民务工人员之间的空间关系进行建模，旨在了解阿曼移民的COVID-19感染率及其工作类型在次国家尺度上的空间关系。利用经验贝叶斯平滑法和空间关联局部指标，研究了做为发病风险因素的六种工作（卫生、农业、零售和商业、行政、制造业、采矿业）。结果显示，每种工作都与COVID-19病例有显著的空间关联。与工作相关的COVID-19高发病率，在空间上集中在马斯喀特人口密集地区。类似的，COVID-19高发病率位于阿曼北部的阿尔-巴特纳（Al-Batnah）平原，那里的移民通常工作于农业部门。此外，就职于卫生部门的移民COVID-19发病率高于其它部门。因此，卫生工作是COVID-19感染传播的热点。目前缺乏COVID-19和工作场所的空间关系研究，本文不仅能帮助阿曼和海湾合作委员会的决策者制定必要的疾病控制政策和计划，而且对其它国家也有益。
Los migrantes figuran entre los grupos humanos más vulnerables a la infección con virus debido a las condiciones sociales y económicas en las que viven. En consecuencia, tiene mucha importancia la modelización espacial de la transmisión de virus entre migrantes, para controlar y contener la pandemia de COVID-19. Esta investigación se enfocó en la modelización de las asociaciones espaciales de las tasas de incidencia del COVID-19 y los trabajadores migrantes. El objetivo era entender las relaciones espaciales entre las tasas de infección de los migrantes con COVID-19 y el tipo de lugar de trabajo, a nivel subnacional, en Omán. Usando el alisamiento empírico de Bayes lo mismo que los indicadores locales de las asociaciones espaciales, se investigaron seis sectores de trabajo (sanidad, agricultura, comercio al menudeo y empresarial, administración, industria y minería) como factores de riesgo para la incidencia de la enfermedad. Los resultados indican que cada uno de los seis sectores laborales tenían una asociación espacial significativa con los casos registrados de COVID-19. Altas tasas de casos de COVID-19 en relación con el lugar de trabajo se agrupaban en las áreas densamente pobladas de Muscat. Similarmente, las tasas altas de casos de COVID-19 estaban localizadas en la parte norte del país, a lo largo de la planicie de Al-Batnah, donde a menudo los migrantes consiguen empleo en el sector agrícola. Además, la tasa de COVID-19 entre los migrantes empleados en el sector de la salud era más alta que la encontrada en los demás sectores. Por eso, trabajar en el sector de la salud puede considerarse como un punto caliente para la propagación de las infecciones con COVID-19. A causa de la escasez de estudios que aboquen el análisis espacial de las asociaciones del COVID-19 con los lugares de trabajo, los resultados de esta investigación son útiles para que los responsables de tomar decisiones establezcan las políticas necesarias y los planes para controlar el brote del virus, no solo en Omán o en el Consejo de Cooperación del Golfo, sino también en otros entornos en desarrollo.
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Acknowledgment
Authors’ Contributions: Conceptualization, methodology, data curation, geospatial analysis, investigation, writing—original draft, Mansour, S.; writing the introduction section, writing—review and editing Abulibdeh, A.; data curation, writing—review and editing, Alahmadi, M.; Al-Said, A.; Al-Said, Al; Watmough, G.; Atkinson, P. All authors reviewed the results and approved the final version of the article.
Additional information
Notes on contributors
Shawky Mansour
SHAWKY MANSOUR is an Associate Professor of GIS in the Department of Geography and GIS, Faculty of Arts, Alexandria University, Egypt, and in the Geography Department, College of Arts and Social Sciences, Sultan Qaboos University, Muscat 123, Al Khoud, Oman. E-mail: [email protected]. He is a specialist in GIS with particular interests in GIScience and spatial modeling. His research focuses on developing and utilizing advanced geospatial techniques to model and analyze the interrelationships between socioeconomic, demographic, and environmental phenomena.
Ammar Abulibdeh
AMMAR ABULIBDEH is an Assistant Professor in the Department of Humanities, College of Arts and Science, Qatar University, Doha, Qatar. E-mail: [email protected]. His research focuses on smart urban planning and design, sustainable built environment, sustainable transportation, and the water–energy–food nexus.
Mohammed Alahmadi
MOHAMMED ALAHMADI is an Associate Professor in the National Center for Remote Sensing Technology at King Abdulaziz City for Science and Technology, Riyadh 11442, Saudi Arabia. E-mail: [email protected]. He is an expert in modeling small area population data from satellite data. His research focuses on the application of machine learning, space–time modeling, and global environmental change.
Adham Al-Said
ADHAM AL-SAID is an Assistant Professor in the Department of Economics and Finance at Sultan Qaboos University, Muscat 123, Al Khoud, Oman. E-mail: [email protected]. His research interests include macroeconomic policy, economic development, and economic diversification.
Alkhattab Al-Said
ALKHATTAB AL-SAID is a part-time faculty member at the College of Economics and Political Sciences, Sultan Qaboos University, Muscat 123, Al Khoud, Oman. E-mail: [email protected]. His research interests include the political economy of the Middle East and Gulf studies.
Gary Watmough
GARY WATMOUGH is an Interdisciplinary Lecturer in land use and socioecological systems in the School of Geosciences, University of Edinburgh, Edinburgh, Scotland EH8 9XP, UK. E-mail: [email protected]. His main research focus is in the development of approaches for geographical targeting of resources particularly in low- and middle-income countries. His research into Earth Observations for Sustainable Development Goals (EO4SDGs) supports the UN’s call for a data revolution to help monitoring progress toward socioeconomic indicators.
Peter M. Atkinson
PETER M. ATKINSON is a Distinguished Professor of Spatial Data Science and Executive Dean of the Faculty of Science and Technology at Lancaster University, Bailrigg, Lancaster LA1 4YW, UK. E-mail: [email protected]. He is currently a Visiting Professor at the University of Southampton, Southampton and the Chinese Academy of Sciences. His research agenda focuses on the development and application of spatial statistical and data science methods, coupled with remote sensing, to tackle some of the most important challenges in environment and epidemiology facing humankind.