Zhao Fei, Zhang Sujin, Zhang Degang, Peng Zhiyan, Zeng Hongyun, Zhao Zhifang, Jin Wei, Shen Wenyu, Liu Wei
School of Earth Sciences, Yunnan University, Kunming 650500, China.
Engineering Research Center of Domestic High-resolution Satellite Remote Sensing Geology for Universities of Yunnan province, Kunming 650500, China.
Int J Appl Earth Obs Geoinf. 2022 May;109:102774. doi: 10.1016/j.jag.2022.102774. Epub 2022 Apr 26.
The emergence of mutant strains such as Omicron has increased the uncertainty of COVID-19, and all countries have taken strict measures to prevent the spread of the disease. The spread of the disease between countries is of particular concern. However, most COVID-19 research focuses mainly on the country or community, and there is less research on the border areas between two countries. In this study, we analyzed changes in the total nighttime light intensity (TNLI) and total nighttime lit area (TNLA) along the Sino-Burma border and used the data to construct an epidemic pressure input index (PII) model in reference to the Shen potential model. The results show that, as the epidemic became more severe, TNLI on both sides of the border at the Ruili border port increased, while that in areas far from the port decreased. At the same time, increases and decreases in TNLA occurred in areas far from the port, and PII can indicate the areas where imported cases are likely to occur. Along the Sino-Burma border, the PII model showed low PII in the north and south and high PII in the central region. The areas between Dehong and Lincang, especially the Ruili, Wanding, Nansan, and Qingshuihe border ports, had high PII. The results of this study offer a reference for public health officials and decision makers when determining resource allocation and the implementation of stricter quarantine rules. With updated epidemic statistics, PII can be recalculated to support timely monitoring of COVID-19 in border areas.
奥密克戎等变异毒株的出现增加了新冠疫情的不确定性,各国纷纷采取严格措施防止疾病传播。各国之间疾病的传播尤其令人担忧。然而,大多数新冠疫情研究主要集中在国家或社区层面,对两国边境地区的研究较少。在本研究中,我们分析了中缅边境沿线夜间总光强(TNLI)和夜间总照明面积(TNLA)的变化,并参考沈氏潜力模型,利用这些数据构建了疫情压力输入指数(PII)模型。结果表明,随着疫情加剧,瑞丽边境口岸两侧边境地区的TNLI增加,而远离口岸地区的TNLI下降。与此同时,远离口岸的地区TNLA出现增减变化,且PII能够指示可能出现输入病例的区域。沿中缅边境,PII模型显示北部和南部的PII较低,中部地区的PII较高。德宏和临沧之间的地区,特别是瑞丽、畹町、南伞和清水河边境口岸,PII较高。本研究结果为公共卫生官员和决策者在确定资源分配以及实施更严格检疫规则时提供了参考。随着疫情统计数据的更新,可以重新计算PII,以支持对边境地区新冠疫情的及时监测。