scholarly journals Spatial and Temporal Variation of Japanese encephalitis Disease and Detection of Disease Hotspots: a Case Study of Gorakhpur District, Uttar Pradesh, India

2014 ◽  
Vol II-8 ◽  
pp. 1-7 ◽  
Author(s):  
S. Verma ◽  
R. D. Gupta
Keyword(s):  
Spatial Analysis ◽  
Japanese Encephalitis ◽  
Temporal Dynamics ◽  
Uttar Pradesh ◽  
Public Health Problem ◽  
Moran’S I ◽  
Spatial And Temporal Variation ◽  
Spatiotemporal Pattern ◽  
Moran's I ◽  
Incidence Data

In recent times, Japanese Encephalitis (JE) has emerged as a serious public health problem. In India, JE outbreaks were recently reported in Uttar Pradesh, Gorakhpur. The present study presents an approach to use GIS for analyzing the reported cases of JE in the Gorakhpur district based on spatial analysis to bring out the spatial and temporal dynamics of the JE epidemic. The study investigates spatiotemporal pattern of the occurrence of disease and detection of the JE hotspot. Spatial patterns of the JE disease can provide an understanding of geographical changes. Geospatial distribution of the JE disease outbreak is being investigated since 2005 in this study. The JE incidence data for the years 2005 to 2010 is used. The data is then geo-coded at block level. Spatial analysis is used to evaluate autocorrelation in JE distribution and to test the cases that are clustered or dispersed in space. The Inverse Distance Weighting interpolation technique is used to predict the pattern of JE incidence distribution prevalent across the study area. Moran's I Index (Moran's I) statistics is used to evaluate autocorrelation in spatial distribution. The Getis-Ord Gi*(d) is used to identify the disease areas. The results represent spatial disease patterns from 2005 to 2010, depicting spatially clustered patterns with significant differences between the blocks. It is observed that the blocks on the built up areas reported higher incidences.

scholarly journals Spatio-temporal dynamics of schistosomiasis in Rwanda between 2001 and 2012: impact of the national Neglected Tropical Disease control programme

2017 ◽  
Vol 12 (1) ◽  
Author(s):  
Elias Nyandwi ◽  
Tom Veldkamp ◽  
Frank Badu Osei ◽  
Sherif Amer
Keyword(s):  
Temporal Dynamics ◽  
Public Health Problem ◽  
Incidence Rates ◽  
Control Programme ◽  
Scan Statistics ◽  
Major Public Health Problem ◽  
Implementation Phase ◽  
Incidence Data ◽  
Spatio Temporal ◽  
The Impact

Schistosomiasis is recognised as a major public health problem in Rwanda. We aimed to identify the spatio-temporal dynamics of its distribution at a fine-scale spatial resolution and to explore the impact of control programme interventions. Incidence data of Schistosoma mansoni infection at 367 health facilities were obtained for the period 2001-2012. Disease cluster analyses were conducted using spatial scan statistics and geographic information systems. The impact of control interventions was assessed for three distinct sub-periods. Findings demonstrated persisting, emerging and re-emerging clusters of schistosomiasis infection across space and time. The control programme initially caused an abrupt increase in incidence rates during its implementation phase. However, this was followed by declining and disappearing clusters when the programme was fully in place. The findings presented should contribute to a better understanding of the dynamics of schistosomiasis distribution to be used when implementing future control activities, including prevention and elimination efforts.

scholarly journals Autokorelasi Spasial untuk Identifikasi Pola Hubungan Kemiskinan di Jawa Timur

2012 ◽  
Vol 3 (1) ◽  
pp. 217
Author(s):  
Rokhana Dwi Bekti
Keyword(s):  
Spatial Analysis ◽  
Spatial Autocorrelation ◽  
Moran’S I ◽  
Poor People ◽  
Spatial Autocorrelation Analysis ◽  
Autocorrelation Analysis ◽  
Moran's I ◽  
Relationship Of ◽  
The Relationship

Spatial autocorrelation is a spatial analysis to determine the relationship pattern or correlation among some locations (observation). On the poverty case of East Java, this method will provide important information for analyze the relationship of poverty characteristics in each district or cities. Therefore, in this research performed spatial autocorrelation analysis on the data of East Java’s poverty. The method used is moran's I test and Local Indicator of Spatial Autocorrelation (LISA). The analysis showed that by the moran's I test, there is spatial autocorrelation found in the percentage of poor people amount in East Java, both in 2006 and 2007. While by LISA, obtained the conclusion that there is a significant grouping of district or cities.

scholarly journals Priestorová analýza dát z povrchového zberu na lokalite Vráble-Fidvár

2021 ◽  
Vol 6 (1-2) ◽  
pp. 35-50
Author(s):  
Dominik Drozd
Keyword(s):  
Spatial Analysis ◽  
Spatial Pattern ◽  
Spatial Patterns ◽  
Interpolation Method ◽  
Objective Evaluation ◽  
Moran’S I ◽  
Sampled Data ◽  
Moran's I ◽  
Geostatistical Interpolation ◽  
Global And Local

The goal of this study is to introduce selected methods of spatial analysis and their contribution to evaluation of fieldwalking data. Spatial analysis encompasses various methods suitable for identification, objective evaluation and visualization of spatial patterns which are present in obtained data. This article primarily deals with sampled data, collected during a 2007 fieldwalking campaign. The dataset consisting of potsherds was spatially autocorrelated, using the global and local Moran’s I coefficient, which was used to identify clusters of finds. Spatial pattern of the settlement was visualised by geostatistical interpolation method – kriging.

scholarly journals A retrospective study on the seasonal occurrence of Japanese Encephalitis cases in Uttar Pradesh

2021 ◽  
Vol 8 (4) ◽  
pp. 1762
Author(s):  
Jaideep K. Chaubey ◽  
Vinod K. Srivastava ◽  
Virendra Kumar ◽  
Arslan Neyaz
Keyword(s):  
Retrospective Study ◽  
Japanese Encephalitis ◽  
Communicable Disease ◽  
Monsoon Season ◽  
Uttar Pradesh ◽  
Public Health Problem ◽  
Morbidity And Mortality ◽  
Seasonal Occurrence ◽  
High Morbidity ◽  
Major Public Health Problem

Background: Japanese encephalitis (JE) is a dreaded mosquito-borne viral disease, especially in Asian, Western Pacific, and Northern Australia region and a major public health problem in India. In India, State of Uttar Pradesh contributed a large portion of JE cases to the country. Because of its high morbidity and mortality, JE is of particular interest. With the help of specific intervention, we can prevent the morbidity and mortality of JE cases. The objective of the study was to know the seasonal occurrence of JE cases in Uttar Pradesh.Methods: This was a retrospective study based on secondary data, shared by Communicable Disease wing of Swasthya Bhawan, Lucknow, Uttar Pradesh (U.P) for the analysis. We used data for the study during the past 7 years (2010–2016). Analysis has been done using Microsoft Excel.Results: In Uttar Pradesh, there were 1322 cases of JE during the year 2010-2016. Peak incidence of JE cases were seen in months of September. An overall decreasing trend with some fluctuation was seen in the occurrence of cases from 2010 to 2016. It was also observed that maximum cases were occurred in monsoon season. Majority of the cases were seen in Gorakhpur district which is located in eastern part of Uttar Pradesh.Conclusions: Majority of the cases of JE were seen in rainy months. Gorakhpur district of Uttar Pradesh has the highest load of JE cases. IEC activities should be carried out to disseminate the information regarding JE among the people for prevention.

scholarly journals Detecting spatiotemporal pattern of tuberculosis and the relationship between ecological environment and tuberculosis, a spatial panel data analysis in Guangxi, China

2018 ◽  
Author(s):  
Zhezhe Cui ◽  
Dingwen Lin ◽  
Virasakdi Chongsuvivatwong ◽  
Jinming Zhao ◽  
Mei Lin ◽  
...  
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Scan Statistics ◽  
Moran’S I ◽  
Ecological Environment ◽  
Spatiotemporal Pattern ◽  
Moran's I ◽  
Spatial Panel ◽  
Series Analysis ◽  
Spatial Panel Data

AbstractGuangxi is one of the provinces having the highest reported incidence of tuberculosis (TB) in China. However, spatial and temporal pattern and causation of the situation are still unclear. In order to detect the spatiotemporal pattern of TB and the association with ecological environment factors in Guangxi Zhuang autonomous region, China, We performed a spatiotemporal analysis with prediction using time series analysis, Moran’s I global and local spatial autocorrelation statistics, and space-time scan statistics, to detect temporal and spatial clusters. Spatial panel models were employed to identify the influence factors. The time series analysis shows that the number of reported cases peaked in spring and summer and decreased in autumn and winter with the annual reported incidence of 113.1/100,000 population. Moran’s I global statistics were greater than 0 (0.363 – 0.536) during the study period. The most significant hot spots were mainly located in the central part. The east part exhibited a low-low relation. By spacetime scanning, the clusters identified were similar to that of the local autocorrelation statistics, and were clustered toward the early of 2016. Duration of sunshine, per capita gross domestic product (PGDP), the recovery rate of TB and participation rate of new cooperative medical care insurance in rural areas had a significant negative association with TB. In conclusion, the reported incidence of TB in Guangxi remains high. The main cluster was located in the central part of Guangxi, a region where promoting the productivity, improving TB treatment pathway and strengthening environmental protective measures (increasing sunshine exposure) are urgently needed.

scholarly journals Harmonic Analysis of the Spatiotemporal Pattern of Thunderstorms in Iran (1961–2010)

2019 ◽  
Vol 2019 ◽  
pp. 1-14 ◽  
Author(s):  
Ali Akbar Sabziparvar ◽  
Seyed Hossein Mir Mousavi ◽  
Mostafa Karampour ◽  
Mehdi Doostkamian ◽  
Esmaeil Haghighi ◽  
...  
Keyword(s):  
Spectral Analysis ◽  
Spatial Autocorrelation ◽  
Harmonic Analysis ◽  
The Other ◽  
Moran’S I ◽  
Spatiotemporal Pattern ◽  
Short Term ◽  
Moran's I ◽  
Central Regions ◽  
To Come

The current study aimed at investigating cycles and the spatial autocorrelation pattern of anomalies of thunderstorms in Iran during different periods from 1961 to 2010. In this analysis, 50-year periods (1961–2010) of thunderstorm codes have been collected from 283 synoptic stations of Meteorological Organization of Iran. The study period has been divided into five different decades (1961–1970, 1971–1980, 1981–1990, 1991–2000, and 2001–2010). Spectral analysis and Moran’s I were used to analyze cycles and the spatial autocorrelation pattern, respectively. Furthermore, in order to conduct the calculations, programming facilities of MATLAB have been explored. Finally, Surfer and GIS were employed to come up with the graphical depiction of the maps. The results showed that the maximum of positive anomalies mainly occurred in the northwestern and western parts of Iran due to their special topography, during all the five studied periods. On the other hand, the minimum of negative anomalies took place in central regions of the country because of lack of appropriate conditions (e.g., enough humidity). Moran’s I spatial analysis further confirmed these findings as Moran’s I depicts the positive and negative spatial autocorrelation patterns in line with negative and positive anomalies, respectively. However, in recent decades, this pattern has experienced a declining trend, especially in southern areas of Iran. The results of harmonic analysis indicated that mainly short-term and midterm cycles dominated Iran’s thunderstorms.

Analisis Moran’s I, Geary’s C, dan Getis-Ord G pada Penerapan Jumlah Penderita Kusta di Kabupaten Gowa

2020 ◽  
Vol 2 (2) ◽  
pp. 151
Author(s):  
S. Sukarna ◽  
Wahidah Sanusi ◽  
Hafilah Hardiono
Keyword(s):  
Infectious Disease ◽  
Spatial Analysis ◽  
Spatial Autocorrelation ◽  
Spatial Patterns ◽  
Moran’S I ◽  
Analysis Method ◽  
Moran's I ◽  
Surrounding Areas ◽  
Geary’S C ◽  
Quadrat Analysis

Analisis spasial merupakan salah satu metode yang sering digunakan dalam melihat pola penyebaran penyakit menular. Penyakit Kusta atau lepra merupakan penyakit menular kronis yang disebabkan oleh bakteri Mycrobacterium Leprae yang penyebarannya melalui droplet. Penelitian ini bertujuan untuk mengetahui pola spasial pada Kusta dengan menggunakan metode Quadrat Analysis, untuk mengetahui ada atau tidaknya autokorelasi spasial antar daerah dengan menggunakan Moran’s I, Geary’s C, Getis-Ord G, dan pemetaan penyebaran penyakit Kusta di Kabupaten Gowa. Pada penelitian ini diperoleh bahwa pola spasial penyebaran penyakit Kusta pada Tahun 2016 dan 2017 di Kabupaten Gowa bersifat mengelompok (clustered). Pada Tahun 2016 terdapat autokorelasi spasial dengan pengujian Moran’s I  dan Geary’s C, sedangkan pengujian Getis-Ord G tidak terdapat autokorelasi spasial antar daerah. Pada Tahun 2017 tidak terdapat autokorelasi spasial antar daerah dengan menggunakan ke tiga pengujian tersebut. Pada Tahun 2016 daerah yang rawan adalah Barombong, daerah yang harus berhati-hati dengan daerah sekitarnya adalah Bontonompo dan daerah yang termasuk kategori aman adalah Tompobulu. Sedangkan pada tahun 2017 daerah yang rawan terhadap penyakit Kusta adalah Bajeng dan Manuju.Kata kunci : Moran’s I, Geary’s C, Getis-Ord G, Moran Scatterplot, Kusta Spatial analysis is one of the methods that is often used to observe spreading pattern of infectious diseases. Leprosy is a chronic infectious disease caused by bacterium Mycrobacterium Leprae which spreads through droplets. This study aims to determine the spatial pattern of leprosy using the Quadrat Analysis method, to determine whether there is spatial autocorrelation between regions using Moran's I, Geary’s C, Getis-Ord G, and mapping the spread of leprosy in Gowa Regency. In this study it was found that the spatial patterns of the spread of leprosy in 2016 and 2017 in Gowa Regency was clustered. In 2016 there were spatial autocorrelations with the tests of Moran's I and Geary's C, while the testing of Getis-Ord G did not have spatial autocorrelation between regions. In 2017 there is no spatial autocorrelation between regions using the three tests. In 2016 the vulnerable areas was Barombong, the area that had to be careful with the surrounding areas was Bontonompo and the area included in the safe category was Tompobulu. Whereas in 2017 areas prone to leprosy were Bajeng and Manuju.Keywords : Moran's I, Geary's C, Getis-Ord G, Moran Scatterplot, Leprosy

Japanese Encephalitis (JE); The Fatal Public Health Issue In Uttar Pradesh, India : A Review

2015 ◽  
Vol 7 (1) ◽  
Author(s):  
Purnima Srivastava ◽  
Manindra Kumar Srivastava
Keyword(s):  
Public Health ◽  
Japanese Encephalitis ◽  
Health Problem ◽  
East Asian ◽  
Uttar Pradesh ◽  
Public Health Problem ◽  
Public Health Issue ◽  
Major Public Health Problem ◽  
Viral Origin ◽  
Important Disease

Japanese Encephalitis (JE) an important disease of viral origin has attracted the attention of public health specialists in South East Asian Regions especially in the BBIN (Bangladesh, Bhutan, India & Nepal) countries due to its endemicity, high CFR and residual problems among survivors. JE has been occurring in the endemic form since long back particularly in northern states of India. Eastern parts of U.P. particularly Gorakhpur division is the worst hit division of Uttar Pradesh (UP) in India. U.P. alone is reporting nearly half of the cases found in whole India. Innocent children are the most common victims. This paper attempts to review the problem & emphasizes the need of identifying auxiliary feasible factors rather than concentrating on unfeasible, as despite of best efforts of state the disease is still a major public health problem.

Spatial Distribution and Predictors of Domestic Violence Among Women Aged 15-49 in Ethiopia: Analysis of EDHS 2016 Dataset.

2020 ◽  
Author(s):  
Elias Ali Seid ◽  
Tesfahun Melese ◽  
Kassahun Alemu
Keyword(s):  
Domestic Violence ◽  
Spatial Distribution ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Spatial Clustering ◽  
Public Health Problem ◽  
Wife Beating ◽  
Moran’S I ◽  
P Value ◽  
Moran's I

Abstract Introduction: Violence against women particularly that is commited by an intimate partner is becoming a social and public health problem across the world. Studies from different countries shows that the spatial variation in distribution of domestic violence was commonly attributed by neighborhood level predictors. Despite the importance of spatial techniques, studies that employ it in Ethiopia are limited. Therefore, the aim of this study is to determine the spatial distribution and predictors of domestic violence among women aged 15-49 in Ethiopia by using EDHS 2016 dataset. Methods: Secondary data from EDHS 2016 was used to determine the spatial distribution of domestic violence in Ethiopia. Spatial auto-correlation statistics (both Global and Local Moran’s I) was used to assess the spatial distribution of domestic violence cases in Ethiopia. Spatial locations of significant clusters were identified by using Kuldorff’s Sat Scan version 9.4 software. Finally, binary logistic regression and generalized linear mixed model were fitted to identify predictors of domestic violence. Result: The study found that spatial clustering of domestic violence cases in Ethiopia with Moran’s I value of 0.26, Z score of 8.26, and P-value < 0.01. The Sat Scan analysis find out 24 significant locations of domestic violence clusters. Among this, 10 are primary clusters with RR 2.18, LLR of 39.55, and P-value < 0.01. The output from regression analysis identifies low economic status, husband/partner alcohol use, witnessing family violence as a child, marital controlling behaviors, and community acceptance of wife-beating as significant predictors of domestic violence.Conclusion and Recommendation: There is spatial clustering of d domestic violence cases in Ethiopia. Areas with a high burden of the problem should get priority for intervention. Comprehensive and collaborative action should be taken by involving stakeholders at different levels. Specific activities may include Organizing media on awareness creation and continuous education on how to maintain a stable relationship between couples and employing long term and intensive effort for transforming culture and social norms that encourage violence against woman are among the major ones.

scholarly journals A Protocol for Pollution Index, Source Identification, and Spatial Analysis of Heavy Metals in Top Soil

2020 ◽  
Author(s):  
Amir Mohammadi ◽  
Sepideh Nemati Mansour ◽  
Maryam Faraji ◽  
Ali Abdolahnejad ◽  
Ali Toolabi ◽  
...  
Keyword(s):  
Spatial Analysis ◽  
Toxic Metals ◽  
Source Identification ◽  
Contamination Factor ◽  
Pollution Index ◽  
Principal Component ◽  
Moran’S I ◽  
Moran's I ◽  
Top Soil ◽  
Z Scores

Introduction: This study aimed to assess a good protocol for the contamination indexes, concentration, spatial analysis, and source identification of toxic metals in top soils. Materials and Methods: In the first step, samples were taken from top soil (30 cm) and the metals were extracted and detected with ICP-AES. In the second step, Enrichment Factor, Geoaccumulation Index, and Contamination Factor of metals were calculated to determine soil contamination degree. Furthermore, the principal component analysis and correlation between metals were conducted for source identification. Results: Spatial analysis, as an important section of the present protocol, was performed using Arc GIS, kriging, and Moran's I models. As results of Moran's I model showed, distribution pattern for Fe, As, Cd, Cu, Ni, Pb, and Zn were random (z-scores ranged from -1.17 to 1.09), indicatingthat these elements could be emitted from different potential sources. In Moran's model, spatial autocorrelation of each pollutant could be measured based on its value and location. Conclusion: The finding of this protocol can be used for extraction of contamination indexes, concentration, spatial analysis, and source identification of toxic metals in top soils.

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