Department of Ecology and Environmental Sciences, Faculty of Natural Sciences, Constantine the Philosopher University, 949 01 Nitra, Slovakia.
Department of Geography and Regional Development, Faculty of Natural Sciences, Constantine the Philosopher University, 949 01 Nitra, Slovakia.
Int J Environ Res Public Health. 2021 Aug 28;18(17):9082. doi: 10.3390/ijerph18179082.
The coronavirus became a phenomenon in 2020, which is making an unwanted but wide space for the study of various scientific disciplines. The COVID-19 pandemic situation which has reached almost the whole civilized world by its consequences thus offers a unique possibility to analyze the graphic space and the human activities inside it. The aim of this study is to predict and identify the potential rate of threat on the example of COVID-19 in Slovakia through an established model. This model consisted of an assessment of the partial phenomena of exposure, vulnerability, and overall risk. The statistical data used to evaluate these phenomena concerned individual cities in Slovakia. These represent the smallest administrative unit. Indirect methods based on the point method were applied in the paper. The spreading and transfer of the disease was influenced much more by the exposure presented by traffic availability, especially, but also the concentration of inhabitants in the selected locations (shops, cemeteries, and others). In the results, our modeling confirmed the regions with the highest intensity, especially in the districts (Bratislava, Košice, Prešov, and Nitra). The selection of the data and method used in this study together with the results reached and presented may serve as an appropriate tool for the support of decision-making of other measures for the future.
冠状病毒在 2020 年成为一种现象,为各个科学学科的研究提供了一个不必要但广泛的空间。COVID-19 疫情已经波及几乎整个文明世界,因此提供了一个独特的机会来分析图形空间和其中的人类活动。本研究的目的是通过建立模型,以 COVID-19 在斯洛伐克为例,预测和识别潜在的威胁率。该模型由暴露、脆弱性和总体风险的部分现象评估组成。用于评估这些现象的统计数据涉及斯洛伐克的各个城市。这些城市代表了最小的行政单位。本文应用了基于点法的间接方法。疾病的传播和转移更多地受到交通可用性所呈现的暴露的影响,尤其是但也受到所选地点(商店、墓地等)居民的集中影响。在结果中,我们的建模证实了疫情强度最高的地区,尤其是在布拉迪斯拉发、科希策、普雷绍夫和尼特拉地区。本研究中使用的数据和方法的选择以及得出和呈现的结果可以作为未来支持其他措施决策的适当工具。