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

2019 ◽  
Author(s):  
Jinou Chen ◽  
Yubing Qiu ◽  
Rui Yang ◽  
Ling Li ◽  
Jinglong Hou ◽  
...  
Keyword(s):  
Yunnan Province ◽  
Pearson Correlation ◽  
Temporal Distribution ◽  
Scan Statistics ◽  
Sputum Smear ◽  
Time Interval ◽  
County Level ◽  
Temporal Cluster ◽  
Keywords Tuberculosis ◽  
Greater Mekong Subregion

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. Keywords: Tuberculosis, Spatial-temporal cluster, Scan statistics, Yunnan, Greater Mekong Subregion

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

2019 ◽  
Author(s):  
Jinou Chen ◽  
Yubing Qiu ◽  
Rui Yang ◽  
Ling Li ◽  
Jinglong Hou ◽  
...  
Keyword(s):  
Yunnan Province ◽  
Pearson Correlation ◽  
Temporal Distribution ◽  
Scan Statistics ◽  
Sputum Smear ◽  
Time Interval ◽  
County Level ◽  
Control And Prevention ◽  
Greater Mekong Subregion

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.

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

2019 ◽  
Vol 19 (1) ◽  
Author(s):  
Jinou Chen ◽  
Yubing Qiu ◽  
Rui Yang ◽  
Ling Li ◽  
Jinglong Hou ◽  
...  
Keyword(s):  
Yunnan Province ◽  
Pearson Correlation ◽  
Temporal Distribution ◽  
Scan Statistics ◽  
Sputum Smear ◽  
Time Interval ◽  
County Level ◽  
Control And Prevention ◽  
Greater Mekong Subregion

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.

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

2019 ◽  
Author(s):  
Jinou Chen ◽  
Yubing Qiu ◽  
Rui Yang ◽  
Ling Li ◽  
Jinglong Hou ◽  
...  
Keyword(s):  
Yunnan Province ◽  
Pearson Correlation ◽  
Temporal Distribution ◽  
Scan Statistics ◽  
Sputum Smear ◽  
Time Interval ◽  
County Level ◽  
Control And Prevention ◽  
Greater Mekong Subregion

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.

scholarly journals Covid-19 Space-time Cluster Detection Using Retrospective Analysis

2022 ◽  
Author(s):  
KALEAB TESFAYE TEGEGNE ◽  
ELENI TESFAYE TEGEGNE ◽  
MEKIBIB KASSA TESSEMA ◽  
GELETA ABERA ◽  
BERHANU BIFATO ◽  
...  
Keyword(s):  
Addis Ababa ◽  
Probability Model ◽  
Health Data ◽  
Space Time ◽  
Scan Statistics ◽  
County Level ◽  
Temporal Cluster ◽  
Preventative Measures ◽  
Spatio Temporal ◽  
Time Scan

Abstract Background: As of the 31st of January 2021, there had been 102,399,513 confirmed cases of COVID-19 worldwide, with 2,217,005 deaths reported to WHOThe goal of this study is to uncover the spatiotemporal patterns of COVID 19 in Ethiopia, which will aid in the planning and implementation of essential preventative measures. Methods We obtained data on COVID 19 cases reported in Ethiopia from November 23 to December 29, 2021, from an Ethiopian health data website that is open to the public.Kulldorff's retrospective space-time scan statistics were utilized to detect the temporal, geographical, and spatiotemporal clusters of COVID 19 at the county level in Ethiopia, using the discrete Poisson probability model. Results: In Ethiopia, between November 23 and December 29, 2021, a total of 22,199 COVID 19 cases were reported.The COVID 19 cases in Ethiopia were strongly clustered in spatial, temporal, and spatiotemporal distribution, according to the results of Kulldorff's scan. statisticsThe most likely Spatio-temporal cluster (LLR = 70369.783209, RR = 412.48, P 0.001) was mostly concentrated in Addis Ababa and clustered between 2021/11/1 and 2021/11/30.Conclusion: From November 23 to December 29, 2021, this study found three large COVID 19 space-time clusters in Ethiopia, which could aid in future resource allocation in high-risk locations for COVID 19 management and prevention.

scholarly journals Spatial and Temporal Distribution of Biomass Open Burning Emissions in the Greater Mekong Subregion

Climate ◽  
2020 ◽  
Vol 8 (8) ◽  
pp. 90
Author(s):  
Agapol Junpen ◽  
Jirataya Roemmontri ◽  
Athipthep Boonman ◽  
Penwadee Cheewaphongphan ◽  
Pham Thi Bich Thao ◽  
...  
Keyword(s):  
Fine Particulate Matter ◽  
Temporal Distribution ◽  
Landsat 8 ◽  
Total Biomass ◽  
Burnt Area ◽  
Fire Emissions ◽  
Vegetation Fires ◽  
Agricultural Burning ◽  
Burnt Areas ◽  
Greater Mekong Subregion

Moderate Resolution Imaging Spectroradiometer (MODIS) burnt area products are widely used to assess the damaged area after wildfires and agricultural burning have occurred. This study improved the accuracy of the assessment of the burnt areas by using the MCD45A1 and MCD64A1 burnt area products with the finer spatial resolution product from the Landsat-8 Operational Land Imager/Thermal Infrared Sensor (OLI/TIRS) surface reflectance data. Thus, more accurate wildfires and agricultural burning areas in the Greater Mekong Subregion (GMS) for the year 2015 as well as the estimation of the fire emissions were reported. In addition, the results from this study were compared with the data derived from the fourth version of the Global Fire Emissions Database (GFED) that included small fires (GFED4.1s). Upon analysis of the data of the burnt areas, it was found that the burnt areas obtained from the MCD64A1 and MCD45A1 had lower values than the reference fires for all vegetation fires. These results suggested multiplying the MCD64A1 and MCD45A1 for the GMS by the correction factors of 2.11−21.08 depending on the MODIS burnt area product and vegetation fires. After adjusting the burnt areas by the correction factor, the total biomass burnt area in the GMS during the year 2015 was about 33.3 million hectares (Mha), which caused the burning of 109 ± 22 million tons (Mt) of biomass. This burning emitted 178 ± 42 Mt of CO2, 469 ± 351 kilotons (kt) of CH4, 18 ± 3 kt of N2O, 9.4 ± 4.9 Mt of CO, 345 ± 206 kt of NOX, 46 ± 25 kt of SO2, 147 ± 117 kt of NH3, 820 ± 489 kt of PM2.5, 60 ± 32 kt of BC, and 350 ± 205 kt of OC. Furthermore, the emission results of fine particulate matter (PM2.5) in all countries were slightly lower than GFED4.1s in the range between 0.3 and 0.6 times.

scholarly journals Influence of Topographic Characteristics on the Adaptive Time Interval for Diffusion Wave Simulation

Water ◽  
2019 ◽  
Vol 11 (3) ◽  
pp. 431
Author(s):  
Pin-Chun Huang ◽  
Kwan Lee ◽  
Boris Gartsman
Keyword(s):  
Flood Control ◽  
Temporal Distribution ◽  
Living Environment ◽  
Mitigation Measures ◽  
Computational Time ◽  
Time Interval ◽  
Flood Warning ◽  
Urgent Task ◽  
Research Findings ◽  
Flood Warning Systems

Frequent flash floods in recent years have resulted in a major impact on the living environment, urban planning, economic system and flood control facilities of residents around the world; therefore, the establishment of disaster management and flood warning systems is an urgent task, required for government units to propose flood mitigation measures. To conserve the numerical accuracy and maintain stability for explicit scheme, the Courant–Friedrich–Lewy (CFL) condition is necessarily enforced, and it is conducted to regulate the relation between the numerical marching speed and wave celerity. On the other hand, to avoid the problem of flow reflux between adjacent grids in executing 2D floodplain simulation, another restriction on time intervals, known as the Hunter condition, was devised in an earlier study. The objective of this study was to analyze the spatial and temporal distribution of these two time-interval restrictions during runoff simulations. Via a case study of the Komarovsky River Basin in Russia, the results show that at the beginning of a storm, the computational time interval is restricted by the CFL condition along the upstream steep hillsides, and the time interval is subject to the Hunter condition in the mainstream during the occurrence of the main storm. The reason of a reduction in computational efficiency, which is a common problem in conducting distributed routing, was clearly explained. To relax the time-interval restrictions for efficient flood forecasting, the research findings also indicate the importance of integrating modified hydrological models proposed in recent studies.

Physical Properties of Nanohybrid and Microhybrid Resin Composites Subjected to an Acidic Environment: A Laboratory Study

2020 ◽  
Vol 45 (3) ◽  
pp. E105-E113
Author(s):  
M Ferooz ◽  
R Bagheri ◽  
D Jafarpour ◽  
MF Burrow
Keyword(s):  
Lactic Acid ◽  
Color Change ◽  
Pearson Correlation ◽  
Color Stability ◽  
Time Interval ◽  
Distilled Water ◽  
Resin Composites ◽  
Acidic Environment ◽  
Hardness Tester ◽  
Storage Media

SUMMARY Background: This study investigated the hardness and color stability of five resin composites subjected to different polishing methods following immersion in distilled water or lactic acid for up to three months. Methods and Materials: Three nanohybrid, Paradigm (3M ESPE), Estelite Sigma Quick (Tokuyama), Ice (SDI), and two microhybrid, Filtek P60 and Filtek Z250, composites were examined. Disc-shaped specimens (10×1.5 mm) were prepared and immersed in distilled water for 24 hours then polished using either silicon carbide paper, the Shofu polishing system or were left unpolished (control). The CIE values and microhardness were determined using a spectrophotometer and digital Vickers hardness tester, respectively (n=10) after one, 45, and 90 days of storage in distilled water or lactic acid. Data were analyzed using analysis of variance, Tukey test, and Pearson correlation coefficient. Results: Ice exhibited the greatest color change, yet Paradigm and Filtek P60 demonstrated the least. Overall, discoloration of tested materials was multifactorial and the effect of storage media depended on the material, polishing method and time interval. The greatest hardness was obtained for Paradigm and the lowest for Estelite. Hardness was found to be significantly higher in lactic acid after 45 days (p=0.014) and even higher after 90 days (p&lt;0.001) compared with distilled water. Conclusions: An acidic environment did not adversely affect color stability or microhardness of the resin composites. There was a significantly mild reverse correlation between hardness and color change in both storage media.

scholarly journals Analysis of initial laboratory diagnosis of malaria and its accuracy compared with re-testing from 2013 to 2018 in Yunnan Province, China

2020 ◽  
Vol 19 (1) ◽  
Author(s):  
Ying Dong ◽  
Yan Deng ◽  
Yanchun Xu ◽  
Mengni Chen ◽  
Chun Wei ◽  
...  
Keyword(s):  
Information System ◽  
Disease Prevention ◽  
Malaria Case ◽  
Nested Pcr ◽  
Prevention And Control ◽  
Yunnan Province ◽  
County Level ◽  
Control Information ◽  
Gene Testing ◽  
And Control

Abstract Background According to China’s Malaria Eradication Action Plan, malaria cases diagnosed and reported by health authorities at the county level must be further re-confirmed by provincial laboratories. The Yunnan Province Malaria Diagnostic Reference Laboratory (YPMDRL) began the synchronous implementation of microscopic examinations and nested polymerase chain reaction (nested-PCR) testing to re-test the malaria cases initially diagnosed by county-level laboratories and to evaluate the consistency of Plasmodium species identified between by YPMDRL and by the county-level laboratories from 2013 to 2018 in Yunnan Province. Methods Data on malaria initial diagnosis completed by county-level laboratories in Yunnan Province were collected weekly from the “China Disease Prevention and Control Information System” from 2013 to 2018. The YPMDRL performed Plasmodium microscopic examination and 18S rRNA gene nested-PCR testing on every malaria case managed by the China Disease Prevention and Control Information System. The re-testing detection results were fed back to the initial diagnosis and reporting unit for revision of malaria case types. Results A total of 2,869 malaria cases were diagnosed and reported by county-level laboratories in Yunnan Province from 2013 to 2018. The re-testing rate was 95.6% (2,742/2,869), and the re-testing rate increased from 2013 to 2018. Among the re-tested 2,742 cases, 96.7% (2651/2742), 2.2% (59/2742), and 1.1% (32/2742) were doubly examined by microscopy and by nested-PCR, only by microscopy, and only by nested-PCR, respectively. The total Plasmodium species accuracy rate at county-level laboratories was 92.6% (2,543/2,742) reference to the diagnosis by YPMDRL. Among the inconsistent 199 cases, they were identified as including 103 negative cases, 45 falciparum malaria cases, 30 vivax malaria cases, 11 ovale malaria cases, and 10 malariae malaria cases by YPMDRL. From 2013 to 2018, the revised and registered malaria cases by the China Disease Prevention and Control Information System in Yunnan Province was 2,747 cases, including 2,305 vivax malaria cases, 421 falciparum malaria cases, 11 ovale malaria cases, and 10 malariae malaria cases. Conclusions The double re-testing strategy by microscopy and by gene testing increases the accuracy of diagnoses malaria in Yunnan Province, and gene testing can reliably differentiate Plasmodium species. The re-testing results provided by YPMDRL are the authoritative basis for revising malaria kind in Yunnan Province.

scholarly journals Spatiotemporal Clustering of Middle East Respiratory Syndrome Coronavirus (MERS-CoV) Incidence in Saudi Arabia, 2012–2019

2019 ◽  
Vol 16 (14) ◽  
pp. 2520 ◽  
Author(s):  
Khalid Al-Ahmadi ◽  
Sabah Alahmadi ◽  
Ali Al-Zahrani
Keyword(s):  
Saudi Arabia ◽  
High Risk ◽  
Middle East ◽  
Infection Risk ◽  
Middle East Respiratory Syndrome ◽  
Scan Statistics ◽  
Health Concern ◽  
Temporal Cluster ◽  
Time Period ◽  
Spatiotemporal Clustering

Middle East respiratory syndrome coronavirus (MERS-CoV) is a great public health concern globally. Although 83% of the globally confirmed cases have emerged in Saudi Arabia, the spatiotemporal clustering of MERS-CoV incidence has not been investigated. This study analysed the spatiotemporal patterns and clusters of laboratory-confirmed MERS-CoV cases reported in Saudi Arabia between June 2012 and March 2019. Temporal, seasonal, spatial and spatiotemporal cluster analyses were performed using Kulldorff’s spatial scan statistics to determine the time period and geographical areas with the highest MERS-CoV infection risk. A strongly significant temporal cluster for MERS-CoV infection risk was identified between April 5 and May 24, 2014. Most MERS-CoV infections occurred during the spring season (41.88%), with April and May showing significant seasonal clusters. Wadi Addawasir showed a high-risk spatial cluster for MERS-CoV infection. The most likely high-risk MERS-CoV annual spatiotemporal clusters were identified for a group of cities (n = 10) in Riyadh province between 2014 and 2016. A monthly spatiotemporal cluster included Jeddah, Makkah and Taif cities, with the most likely high-risk MERS-CoV infection cluster occurring between April and May 2014. Significant spatiotemporal clusters of MERS-CoV incidence were identified in Saudi Arabia. The findings are relevant to control the spread of the disease. This study provides preliminary risk assessments for the further investigation of the environmental risk factors associated with MERS-CoV clusters.

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