The characteristics of spatial-temporal distribution and cluster of tuberculosis in Yunnan Province, China, 2005-2018.

Abstract

Background Tuberculosis (TB) makes a big challenge to public health, especially in high TB burden counties of China and Greater Mekong Subregion (GMS). The aim of this study was to identify the spatial-temporal dynamic process and high-risk region of notified pulmonary tuberculosis (PTB), sputum smear-positive tuberculosis (SSP-TB) and sputum smear-negative tuberculosis (SSN-TB) cases in Yunnan, the south-western of China between years of 2005 to 2018. meanwhile, to evaluate the similarity of prevalence pattern for TB among GMS. Methods Data for notified PTB were extracted from the China Information System for Disease Control and Prevention (CISDCP) correspond to population information in 129 counties of Yunnan between 2005 to 2018. Seasonally adjusted time series defined the trend cycle and seasonality of PTB prevalence. Kulldorff’s space-time scan statistics was applied to identify temporal, spatial and spatial-temporal PTB prevalence clusters at county-level of Yunnan. Pearson correlation coefficient and hierarchical clustering were applied to define the similarity of TB prevalence among borders with GMS. Result There were a total of 381 855 notified PTB cases in Yunnan, and the average prevalence was 59.1 per 100 000 population between 2005 to 2018. A declined long-term trend with seasonality of a peak in spring and a trough in winter for PTB was observed. Spatial-temporal scan statistics detected the significant clusters of PTB prevalence, the most likely cluster concentrated in the northeastern angle of Yunnan between 2011 to 2015 ( RR =2.6, P <0.01), though the most recent cluster for PTB and spatial cluster for SSP-TB was in borders with GMS. There were six potential TB prevalence patterns among GMS. Conclusion This study detected aggregated time interval and regions for PTB, SSP-TB, and SSN-TB at county-level of Yunnan province. Similarity prevalence pattern was found in borders and GMS. The localized prevention strategy should focus on cross-boundary transmission and SSN-TB control.

A naturally isolated symbiotic bacterium suppresses flavivirus transmission by Aedes mosquitoes

“…We tested for the presence of Rosenbergiella_YN46 in field isolates of A. albopictus mosquitoes from four south-ern cities of Yunnan Province with similar vectorial and environmental conditions (Fig. 5A and table S4), including dengue-endemic areas (Xishuangbanna, Lincang) and low dengueincidence areas (Wenshan and Puer) (39). The prevalence of Rosenbergiella_YN46 was greater in the guts of mosquitoes in low dengue-incidence cities than in those found in dengue-endemic cities (Fig.…”

Research and Application of Intelligent Weather Push Model Based on Travel Forecast and 5G Message

“…Travel prediction, which utilizes historical or real-time data to forecast travelers' destinations, times, and other information, finds widespread applications in intelligent transportation, smart cities, the mobile internet [16,17], and personalized travel services [18]. Travel prediction methods can be broadly categorized into rule-based [19,20] and machine learning-based [21] approaches.…”