Spatial change, time tendency and projection to HIV/AIDS incidence in an eastern province of China
Abstract Objectives As the second most populous province in China, it would have public implications to analyze spatial change, time tendency and prediction analysis of the acquired immunodeficiency syndrome (AIDS) epidemic in Shandong Province. Statistical projection study. Spatial autocorrelation...
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Nature Portfolio
2025-05-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-01755-z |
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| author | Yiwei Li Shijia Yuan Yang Zhao Xingguang Yang Hongyu Li Yanlin Lv Wenhui Gao Surong Zhao Ronghang Liu Chunlei Han |
| author_facet | Yiwei Li Shijia Yuan Yang Zhao Xingguang Yang Hongyu Li Yanlin Lv Wenhui Gao Surong Zhao Ronghang Liu Chunlei Han |
| author_sort | Yiwei Li |
| collection | DOAJ |
| description | Abstract Objectives As the second most populous province in China, it would have public implications to analyze spatial change, time tendency and prediction analysis of the acquired immunodeficiency syndrome (AIDS) epidemic in Shandong Province. Statistical projection study. Spatial autocorrelation model and spatiotemporal scanning analysis were used to describe the spatial and temporal distribution of HIV/AIDS incidence in Shandong Province from 2012 to 2022, and geographical weighted regression model and geographic detector model were used to analyze the influencing factors. The annual incidence rate of AIDS in Shandong Province from 2023 to 2030 was predicted by using the fitted Autoregressive Integrated Moving Average model (ARIMA). From 2012 to 2022, the incidence of AIDS in Shandong Province grows from 1/10 thousand to 2.48/10 thousand. Spatial regressions showed that the tertiary sector GDP ratio is the factor with the highest intensity of influence. AIDS incidence shows an overall decreasing trend from east to west across Shandong Province. It is predicted that the AIDS incidence rate in Shandong province will continue to rise from 2023 to 2030, with a projected rate of 6.33/10 thousand in 2030. From 2012 to 2022, the incidence of AIDS in Shandong Province increased and then decreased, and regional differences were revealed as well. It is predicted that the incidence of AIDS in Shandong Province would have an increasing trend from 2023 to 2030. |
| format | Article |
| id | doaj-art-e44cf5fb9ea0414cbb2613bfb3b7f994 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-e44cf5fb9ea0414cbb2613bfb3b7f9942025-08-20T03:48:15ZengNature PortfolioScientific Reports2045-23222025-05-0115111110.1038/s41598-025-01755-zSpatial change, time tendency and projection to HIV/AIDS incidence in an eastern province of ChinaYiwei Li0Shijia Yuan1Yang Zhao2Xingguang Yang3Hongyu Li4Yanlin Lv5Wenhui Gao6Surong Zhao7Ronghang Liu8Chunlei Han9Binzhou Medical University HospitalBinzhou Medical University HospitalSchool of Health Management, Binzhou Medical UniversityShandong Center for Disease Control and PreventionBinzhou Medical University HospitalSchool of Public Health, Ningxia Medical UniversityBinzhou Medical University HospitalBinzhou Medical University HospitalBinzhou Medical University HospitalBinzhou Medical University HospitalAbstract Objectives As the second most populous province in China, it would have public implications to analyze spatial change, time tendency and prediction analysis of the acquired immunodeficiency syndrome (AIDS) epidemic in Shandong Province. Statistical projection study. Spatial autocorrelation model and spatiotemporal scanning analysis were used to describe the spatial and temporal distribution of HIV/AIDS incidence in Shandong Province from 2012 to 2022, and geographical weighted regression model and geographic detector model were used to analyze the influencing factors. The annual incidence rate of AIDS in Shandong Province from 2023 to 2030 was predicted by using the fitted Autoregressive Integrated Moving Average model (ARIMA). From 2012 to 2022, the incidence of AIDS in Shandong Province grows from 1/10 thousand to 2.48/10 thousand. Spatial regressions showed that the tertiary sector GDP ratio is the factor with the highest intensity of influence. AIDS incidence shows an overall decreasing trend from east to west across Shandong Province. It is predicted that the AIDS incidence rate in Shandong province will continue to rise from 2023 to 2030, with a projected rate of 6.33/10 thousand in 2030. From 2012 to 2022, the incidence of AIDS in Shandong Province increased and then decreased, and regional differences were revealed as well. It is predicted that the incidence of AIDS in Shandong Province would have an increasing trend from 2023 to 2030.https://doi.org/10.1038/s41598-025-01755-zAIDS predictionSpatial correlation modelSpatiotemporal scanning analysisARIMA modelShandong provinceChina |
| spellingShingle | Yiwei Li Shijia Yuan Yang Zhao Xingguang Yang Hongyu Li Yanlin Lv Wenhui Gao Surong Zhao Ronghang Liu Chunlei Han Spatial change, time tendency and projection to HIV/AIDS incidence in an eastern province of China Scientific Reports AIDS prediction Spatial correlation model Spatiotemporal scanning analysis ARIMA model Shandong province China |
| title | Spatial change, time tendency and projection to HIV/AIDS incidence in an eastern province of China |
| title_full | Spatial change, time tendency and projection to HIV/AIDS incidence in an eastern province of China |
| title_fullStr | Spatial change, time tendency and projection to HIV/AIDS incidence in an eastern province of China |
| title_full_unstemmed | Spatial change, time tendency and projection to HIV/AIDS incidence in an eastern province of China |
| title_short | Spatial change, time tendency and projection to HIV/AIDS incidence in an eastern province of China |
| title_sort | spatial change time tendency and projection to hiv aids incidence in an eastern province of china |
| topic | AIDS prediction Spatial correlation model Spatiotemporal scanning analysis ARIMA model Shandong province China |
| url | https://doi.org/10.1038/s41598-025-01755-z |
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