Spatiotemporal Cluster Patterns of Hand, Foot, and Mouth Disease at the Province level in Mainland China, 2009-2018

BackgroundAlthough three monovalent EV-A71 vaccines have been launched in mainland China since 2016, hand, foot, and mouth disease (HFMD) still causes a considerable disease burden in China. Vaccines’ use may change the epidemiological characters of HFMD. This study aims to analyze the spatiotemporal cluster of HFMD at the province level in mainland China from 2009 to 2018 and compare the difference before and after the vaccines were launched. MethodsAll HFMD cases’ data from January 2009 to December 2018 were obtained from the public health science data center given by the Chinese Center for Diseases Control and Prevention. Spatial autocorrelation analysis and space-time scan statistics analysis were used to explore the spatiotemporal distribution pattern of this disease at the provincial level in mainland China. ResultsThe median annual incidence of HFMD was 143.22 per 100,000 (ranging from 87.01 to 205.06) in mainland China from 2009 to 2018. Two peaks of infections were observed per year. Children 5 years and under were the main morbid population. The global autocorrelation analysis showed that the spatial distribution of HFMD was presented a significant clustering pattern in each year (P<0.001), and the local autocorrelation analysis indicated that the high incidence areas were clustered in the southern and southeastern coastal provinces. The distribution of HFMD cases was clustered in time and space. The range of cluster time was between April and October. The most likely cluster appeared in the southern coastal provinces (Guangxi, Guangdong, Hainan) from 2010 to 2017 and in the southeastern coastal provinces (Shanghai, Jiangsu, Zhejiang) in 2018. ConclusionChanges in the spatiotemporal cluster of HFMD after the launch of EV-A71 vaccines were observed at the province level in mainland China in 2018. It is necessary to advance the EV-A71 vaccination plan, analyze the spatial-temporal distribution characteristics of different enterovirus pathogens of HFMD, and promote HFMD multivalent vaccines.

Washing and Chopping Pre-treatment Effect of Vetiver Roots on Vetiver Oil Yield and Distillation Time

Vetiver is a type of grass mainly used for its roots to be extracted into vetiver oil. Despite the increasing market demand, the productivity of vetiver oil in Indonesia still consider low. One of the determinant factors of the extraction yield is the pre-treatment before distillation. This study aimed to determine the best pre-treatment method to improve the vetiver oil extraction using water and steam distillation by looking at the yield, distillation rate, and forecasted distillation duration. The distillation process was using water and steam distillation method for 9 hours. The data analysis method used Durbin-Watson autocorrelation analysis. The feasibility test of polynomial regression was modelled with the F test and ANOVA variance test. The result showed that the combination of washing 2-3 times and chopping pre-treatments of vetiver roots with a size of ± 5 cm could significantly increase the extracted vetiver oil by producing the highest yield (0.36% (wet-based) and 0.47% (dry based)), the highest extraction rate (0.057%/h) and the fastest forecasted duration of the distillation (10.5 hours). The combination of washing and chopping pre-treatments of vetiver roots was the best method and could be an economical solution for low productivity problems of vetiver oil in Indonesia.

Spatial autocorrelation analysis of Covid-19 cases in South Kalimantan, Indonesia

Coronavirus Disease 2019 (COVID-19) is a new coronavirus that was discovered in Wuhan, China, at the end of 2019. In March 2020, the outbreak extended throughout the world, including Indonesia and one of its provinces, South Kalimantan. This rapid expansion should belinked to people’s mobility between regions, hence the linkage across regions must be examined. In South Kalimantan Province, the purpose of this research is to evaluate the distribution and relationship across regions in terms of the number of positive COVID-19 cases, the number of additional positive COVID-19 cases, and the number of COVID-19 patients under treatment. The spatial autocorrelation analysis with the Moran Index and Local Indicator of Spatial Autocorrelation (LISA) tests were used to determine the spatial autocorrelation between what and what using what data/where the data obtained? from March 22 to September 30, 2020. Based on the results of the Moran Index test, it is known that there is a spatial autocorrelation in the number of cases, the number of additional cases and the number of positive confirmed COVID-19 patients in treatment between one region and another neighboring area. While the results of the LISA Index test show that Balangan Regency, Hulu Sungai Tengah Regency, Hulu Sungai Utara Regency, Banjarmasin City, Tabalong Regency and Banjar Regency affect the level of COVID-19 cases in their respective neighboring areas. Therefore, there is a need for policies to control community mobility in those spatially correlated areas and increase testing and tracing to control the spread of COVID-19 cases in South Kalimantan Province.

SPATIAL AUTOCORRELATION ANALYSIS OF HOUSING DISTRIBUTION IN JOHOR BAHRU

Geographic location naturally generates spatial patterns that are either clustered, dispersed, or random. Moreover, Tobler’s First Law of Geography is essentially a testable assumption in the concept where geographic location matters and one method for quantifying Tobler’s law of geography is through measures of spatial autocorrelation. Therefore, the purpose of this study is to identify the spatial patterns of housing distribution in Johor Bahru through the spatial autocorrelation method. The result of the global spatial autocorrelation analysis demonstrates a high degree of clustering within the housing distribution, as well as the identification of a clustered pattern with a highly positive Moran’s I value of 0.995207. Following that, the LISA cluster map successfully identified individual clusters of each housing unit with their neighbours through the red and blue colours displayed on the map, as well as revealing home buyers’ preferences for a property in each location.

Identification of Shrinking Cities on the Main Island of Taiwan Based on Census Data and Population Registers: A Spatial Analysis

At the end of the 20th century, the phenomenon of urban shrinkage received widespread attention, with population decline as its core characteristic. In 2020, the Taiwanese population had negative growth and faced a low fertility rate and an aging population. This study used exploratory spatial data analysis to identify shrinking cities in Taiwan based on census data and population registers. The results indicated that Taiwan has 11 shrinking counties and 202 shrinking towns. Urban shrinkage occurred in the 1980s and continued from the suburbanization stage to the re-urbanization stage. Five types of spatial patterns in the 11 shrinking counties were observed. In the majority of the shrinking counties, towns with high population densities were unable to avoid shrinkage. A global spatial autocorrelation analysis indicated that shrinkage and non-shrinkage have become increasingly apparent at the town level since 2005. A local spatial autocorrelation analysis indicates that the spatial clustering of towns with population growth or decline from 2000 to 2020 has changed. Based on each town's development, a two-step cluster analysis was conducted in which all towns were divided into four categories. Shrinking towns exist in each category, but with a different proportion. Based on the results of two-step cluster analysis combined with spatial analysis, this study discovered that both urbanization and suburbanization cause shrinkage in Taiwan, but the affected localities are distinct. For most shrinking counties, their spatial model indicates a relationship between shrinking and the urbanization of their towns. Keelung City and Chiayi City have the most potential to reverse the shrinkage. This study helps authorities better manage growth and implement regional revitalization.

Risk Assessment and Factors Associated with Lung Cancer using GIS in Mae Ka Subdistrict, Muang District, Phayao Province, Thailand

Cancer is a major health problem in the developing countries. Variations of its incidence rate among geographical areas are due to various contributing factors. This study was performed to assess the spatial patterns of lung cancer incidence in the Mae Ka subdistrict, Muang district, Phayao province, based on lung cancer registry data and to determine geographical clusters. In this cross-sectional study, the cases of lung cancer were recorded from 2015 to 2020. Crude incidence rate was estimated based on age groups and sex in the province of the Mae Ka subdistrict. It uses spatial autocorrelation analysis (SAA) techniques to provide insight into the patterns, in terms of their geographical distributions and hotspot identification. Spatial autocorrelation analysis was performed in measuring the geographic patterns and clusters using GIS. In addition, local indicators of spatial association (LISA) and kernel density (KD) estimation were used to detect lung cancer hotspots using data at village level. Factors associated with the incidence of lung cancer was analyzed for behavior risk factors. Analysis of the spatial distribution of lung cancer shows significant differences from year to year and between different areas. The hotspot maps showed spatial trend patterns of lung cancer diffusion. Villages in the northern part revealed higher incidence. Furthermore, the spatial patterns during the years 2015, 2017 and 2019 were found to represent spatially clustered patterns, both at global and local scales. However, a clear spatial autocorrelation is observed, which can be of grate interest and importance to researchers for future epidemiological studies, and to policymakers for applying preventive measures.

Spatial Analysis of Abandoned Houses and Their Influencing Factors in South Korea

The number of abandoned houses is rapidly growing across South Korea. The increasing number of abandoned houses is directly linked to a wide range of problems in communities, such as apprehension about crimes. This study aimed to analyze the variables that affect housing abandonment empirically. First, we analyzed the status of housing abandonment in various regions based on the addresses of the abandoned houses. Second, we identified the spatial characteristics of abandoned houses through spatial autocorrelation analysis. Third, we selected variables based on the literature review and analyzed the factors affecting housing abandonment through spatial regression analysis. Lastly, we aimed to explore the correlation between regional characteristics and the occurrence of housing abandonment, and to derive the factors influencing housing abandonment. This study found that abandoned houses were more likely to occur mainly in areas with environmentally vulnerable features. In this study, neighborhood environmental factors that promoted the occurrence of abandoned houses were derived by considering the neighborhood-level unit of analysis.

Epidemiological characteristics and spatiotemporal analysis of hand-foot-mouth diseases from 2010 to 2019 in Zibo city, Shandong, China

Background Hand-foot-mouth disease (HFMD) is a global public health issues, especially in China. It has threat the health of children under 5 years old. The early recognition of high-risk districts and understanding of epidemic characteristics can facilitate health sectors to prevent the occurrence of HFMD effectively. Methods Descriptive analysis was used to summarize epidemic characteristics, and the spatial autocorrelation analysis and space-time scan analysis were utilized to explore distribution pattern of HFMD and identify hot spots with statistical significance. The result was presented in ArcMap. Results A total of 52,095 HFMD cases were collected in Zibo city from 1 Jan 2010 to 31 Dec 2019. The annual average incidence was 129.72/100,000. The distribution of HFMD was a unimodal trend, with peak from April to September. The most susceptible age group was children under 5 years old (92.46%), and the male-to-female ratio is 1.60: 1. The main clusters were identified in Zhangdian District from 12 April 2010 to 18 September 2012. Spatial autocorrelation analysis showed that the global spatial correlation in Zibo were no statistical significance, except in 2012, 2014, 2015, 2016 and 2018. Cold spots were gathered in Boshan county and Linzi district, while hot spots only in Zhangdian District in 2018, but other years were no significance. Conclusion Hot spots mainly concentrated in the central and surrounding city of Zibo city. We suggest that imminent public health planning and resource allocation should be focused within those areas.

Spatial-temporal heterogeneity and meteorological factors of hand-foot-and-mouth disease in Xinjiang, China from 2008 to 2016

The study aims to depict the temporal and spatial distributions of hand-foot-and-mouth disease (HFMD) in Xinjiang, China and reveal the relationships between the incidence of HFMD and meteorological factors in Xinjiang. With the national surveillance data of HFMD in Xinjiang and meteorological parameters in the study area from 2008 to 2016, in GeoDetector Model, we examined the effects of meteorological factors on the incidence of HFMD in Xinjiang, China, tested the spatial-temporal heterogeneity of HFMD risk, and explored the temporal-spatial patterns of HFMD through the spatial autocorrelation analysis. From 2008 to 2016, the HFMD distribution showed a distinct seasonal pattern and HFMD cases typically occurred from May to July and peaked in June in Xinjiang. Relative humidity, precipitation, barometric pressure and temperature had the more significant influences on the incidence of HFMD than other meteorological factors with the explanatory power of 0.30, 0.29, 0.29 and 0.21 (P<0.000). The interaction between any two meteorological factors had a nonlinear enhancement effect on the risk of HFMD. The relative risk in Northern Xinjiang was higher than that in Southern Xinjiang. Global spatial autocorrelation analysis results indicated a fluctuating trend over these years: the positive spatial dependency on the incidence of HFMD in 2008, 2010, 2012, 2014 and 2015, the negative spatial autocorrelation in 2009 and a random distribution pattern in 2011, 2013 and 2016. Our findings revealed the correlation between meteorological factors and the incidence of HFMD in Xinjiang. The correlation showed obvious spatiotemporal heterogeneity. The study provides the basis for the government to control HFMD based on meteorological information. The risk of HFMD can be predicted with appropriate meteorological factors for HFMD prevention and control.