Qinghai-Tibet Plateau is one of the major plague foci in China. Himalayan marmot (hereinafter referred to as the marmot) is the main host animals of the foci. Effective identification and monitoring of its habitat in Qinghai-Tibet Plateau is important for active prevention and effective control of the marmot plague epidemic. Spatial information technology is an important means for rapid identification and effective monitoring of marmot. This review analyzed the factors that affect the distribution patterns of marmot habitat, including topographical features, soil properties, vegetation types and cover, climatic conditions and human activities, and reviewed and summarized the progress and shortcomings of relevant research on identifying and monitoring marmot habitat based on spatial information technologies such as remote sensing and geographic information system. Meanwhile, combined with the emerging low-altitude remote sensing, crowdsource data acquisition, machine learning and other big data mining methods, from the perspective of multi-source data, integration of emerging technologies and comprehensive evaluation system, the new ideas of using spatial information technology to quickly identify and effectively monitor the habitat of marmot, was discussed. It is expected to provide technical support for prediction and effective control of marmot plague in Qinghai-Tibet Plateau under the background of changing global climate and increasing human activities.

Background Precise prevention and control refers to the means of dividing a large area into several small areas, clarifying the disease information in each small area, and carrying out prevention and control program in the small areas accordingly. It is an important starting point for a comprehensive prevention and control program targeting liver cancer, which can make the prevention and control of liver cancer more accurate and efficient. At present, most of the mainstream research on liver cancer prevention and control at home and abroad is only on the provincial and municipal level, which is difficult to meet the requirements of precise prevention and control. Objective Taking S County, a typical county in Central China as an example, to explore the temporal and spatial distribution of liver cancer and environmental health risks at the township scale, so as to provide scientific reference for developing precise prevention and control program. Methods Based on the liver cancer data in the tumor registry data and related environmental variables of S County from 2009 to 2018, a spatiotemporal analysis was carried out by using geographic information system mapping, global Moran index analysis, and cold and hot spot detection. The correlations between liver cancer and various environmental factors [fine particulate matter (PM_2.5), aerosol optical depth (AOD), temperature, precipitation, proportion of cultivated land, normalized difference vegetation index (NDVI), population density, and per capita gross domestic product (GDP)] in S County were preliminarily evaluated by using geodetectors. The environmental factors and their cumulative exposure years that were closely related to the liver cancer in S County were investigated by random forest model. On this basis, the towns were categorized based on total, age-specified, and gender-specified incidences of liver cancer through quartile ranking, and precise prevention and control suggestions were proposed. Results ZD, HD and BYJ had the highest incidence rates of liver cancer, and the average annual incidence rate was 628/10⁵, 58.28/10⁵, and 40.21/ 10⁵, respectively. In addition to the above three townships, LW was a high incidence area of liver cancer in male population and people under the age of 60 years, whose average annual incidence rate was 50.47/10⁵ and 10.59/10⁵, respectively, while LianC was a high incidence area of liver cancer in female population and people aged 60 years and above, whose average annual incidence was 23.39/10⁵ and 131.10/10⁵ respectively. The incidence of liver cancer was closely related to population density, GDP per capita, NDVI, and AOD, and their importance indicators were 0.92, 0.50, 0.43, and 0.36, respectively. The average time interval between continuous exposure to dangerous environmental factors and the diagnosis of liver cancer was 10 years. Conclusion HD, ZD, and BYJ of S County should vigorously carry out liver cancer screening, diagnosis, and treatment in the follow-up prevention and control, while HD and LW should continue to implement environmental protection. LW needs to strengthen the prevention and treatment of liver cancer in male population and population under 60 years old. LianC needs to strengthen the prevention and control of liver cancer in female population and people of and over 60 years of age. The towns around HD need to prevent the sudden outbreak of liver cancer. In addition, it is also necessary to strengthen the willingness of people in the county, especially those of and over 60 years old, to participate in liver cancer screening. This study provides important reference for the analysis of environmental health effects at a fine scale and for the prevention and control of liver cancer and environmental protection in different populations.