scholarly journals Space-time scan statistics of 2007-2013 dengue incidence in Cimahi city, Indonesia

2015 ◽  
Vol 10 (2) ◽  
Author(s):  
Pandji Wibawa Dhewantara ◽  
Andri Ruliansyah ◽  
M. Ezza Azmi Fuadiyah ◽  
Endang Puji Astuti ◽  
Mutiara Widawati
Keyword(s):  
Population Data ◽  
Northern Region ◽  
Space Time ◽  
Scan Statistics ◽  
Distribution Model ◽  
Monthly Data ◽  
Risk Areas ◽  
Surrounding Areas ◽  
The City ◽  
Time Scan

Four dengue serotypes threatened more than 200 million people and has spread to over 400 districts in Indonesia. Furthermore, 26 districts in most densely populated province, West Java, have been declared as hyperendemic areas. Cimahi is an endemic city with the highest population (14,969 people per square kilometer). Evidence on distribution pattern of dengue cases is required to discover the spread of dengue cases in Cimahi. A study has been conducted to detect clusters of dengue incidence during 2007-2013. A temporal spatial analysis was performed using SaTScan™ software incorporated confirmed dengue monthly data from the Municipality Health Office and population data from a local Bureau of Statistics. A retrospective space-time analysis with a Poisson distribution model and monthly precision was performed. Our results revealed a significant most likely cluster (p<0.001) throughout period of study. The most likely cluster was detected in the centre of the city and moved to the northern region of Cimahi. Cimahi, Karangmekar, and Cibabat village were most likely cluster in 2007-2010 (p <0.001; RR = 2.16-2.98; pop at risk 12% total population); Citeureup were detected as the most likely cluster in 2011-2013 (p <0.001; RR 5.77), respectively. Temporaly, clusters were detected in the first quarter of each year each. In conclusion, a dynamic spread of dengue initiated from the centre to its surrounding areas during the period 2007-2013. Our study suggests the use of GIS to strengthen case detection and surveillance. An in-depth investigation to relevant risk factors in high-risk areas in Cimahi city is encouraged.

scholarly journals Space–Time Clustering Characteristics of Malaria in Bhutan at the End Stages of Elimination

2021 ◽  
Vol 18 (11) ◽  
pp. 5553
Author(s):  
Kinley Wangdi ◽  
Kinley Penjor ◽  
Tobgyal ◽  
Saranath Lawpoolsri ◽  
Ric N. Price ◽  
...  
Keyword(s):  
Control Program ◽  
Space Time ◽  
Data Repository ◽  
Spatial Clusters ◽  
Scan Statistic ◽  
Disease Control Program ◽  
Risk Areas ◽  
Vector Borne ◽  
Program Data ◽  
Time Scan

Malaria in Bhutan has fallen significantly over the last decade. As Bhutan attempts to eliminate malaria in 2022, this study aimed to characterize the space–time clustering of malaria from 2010 to 2019. Malaria data were obtained from the Bhutan Vector-Borne Disease Control Program data repository. Spatial and space–time cluster analyses of Plasmodium falciparum and Plasmodium vivax cases were conducted at the sub-district level from 2010 to 2019 using Kulldorff’s space–time scan statistic. A total of 768 confirmed malaria cases, including 454 (59%) P. vivax cases, were reported in Bhutan during the study period. Significant temporal clusters of cases caused by both species were identified between April and September. The most likely spatial clusters were detected in the central part of Bhutan throughout the study period. The most likely space–time cluster was in Sarpang District and neighboring districts between January 2010 to June 2012 for cases of infection with both species. The most likely cluster for P. falciparum infection had a radius of 50.4 km and included 26 sub-districts with a relative risk (RR) of 32.7. The most likely cluster for P. vivax infection had a radius of 33.6 km with 11 sub-districts and RR of 27.7. Three secondary space–time clusters were detected in other parts of Bhutan. Spatial and space–time cluster analysis identified high-risk areas and periods for both P. vivax and P. falciparum malaria. Both malaria types showed significant spatial and spatiotemporal variations. Operational research to understand the drivers of residual transmission in hotspot sub-districts will help to overcome the final challenges of malaria elimination in Bhutan.

Spatial and space-time scan statistics to detect low rate clusters of sex ratio

2005 ◽  
Vol 12 (3) ◽  
pp. 289-299 ◽  
Author(s):  
Jean-Francois Viel ◽  
Nathalie Floret ◽  
Frederic Mauny
Keyword(s):  
Sex Ratio ◽  
Space Time ◽  
Scan Statistics ◽  
Time Scan ◽  
Low Rate

scholarly journals Trends and Spatial Patterns of Oral Cancer Mortality in Ecuador, 2001–2016

2018 ◽  
Vol 2018 ◽  
pp. 1-7 ◽  
Author(s):  
Solange Núñez-González ◽  
J. Andrés Delgado-Ron ◽  
Christopher Gault ◽  
Daniel Simancas-Racines
Keyword(s):  
Ecological Study ◽  
Temporal Trend ◽  
Model Space ◽  
Space Time ◽  
High Variability ◽  
Scan Statistics ◽  
Joinpoint Regression ◽  
Crude Mortality ◽  
Standardized Mortality Rate ◽  
Time Scan

The aims of this study were to describe the temporal trend of OC from 2001 to 2016 and to analyze the space and space-time clusters of high mortality due to OC in Ecuador from 2011 to 2016. Methods. The present study is a mixed ecological study; the time trends were obtained using a Joinpoint regression model, space-time scan statistics was used to identify high-risk clusters, and Global Moran I index was calculated. Results. In Ecuador, between 2001 and 2016, OC caused a total of 1,025 deaths. Crude mortality rates significantly increased, with an APC (annual percentage change) of 2.7% (p=0.009). The age-standardized mortality rate did not significantly increase (APC: 1.73%; p=0.08). The most likely cluster was detected in 2015, included 20 cantons. The second cluster included 38 cantons, in the years 2014 to 2016. The Global Moran I index for the study period showed a negative spatial autocorrelation (−0.067; p=0.37). Conclusion. Mortality due to OC in Ecuador significantly increased over the 16-year study period, the young groups being the most affected. Ecuadorian provinces present high variability in types of OC and cancer rates.

scholarly journals Space-time clusters of dengue, chikungunya, and Zika cases in the city of Rio de Janeiro

2019 ◽  
Author(s):  
Laís Picinini Freitas ◽  
Oswaldo Gonçalves Cruz ◽  
Rachel Lowe ◽  
Marilia Sá Carvalho
Keyword(s):  
Vector Control ◽  
Rio De Janeiro ◽  
Space Time ◽  
Scan Statistics ◽  
Scan Statistic ◽  
Timely Manner ◽  
The North ◽  
Spatio Temporal ◽  
The City ◽  
Janeiro City

AbstractBrazil is a dengue-endemic country where all four dengue virus serotypes circulate and cause seasonal epidemics. Recently, chikungunya and Zika viruses were also introduced. In Rio de Janeiro city, the three diseases co-circulated for the first time in 2015-2016, resulting in what is known as the ‘triple epidemic’. In this study, we identify space-time clusters of dengue, chikungunya, and Zika, to understand the dynamics and interaction between these simultaneously circulating arboviruses in a densely populated and heterogeneous city.We conducted a spatio-temporal analysis of weekly notified cases of the three diseases in Rio de Janeiro city (July 2015 – January 2017), georeferenced by 160 neighbourhoods, using Kulldorff’s scan statistic with discrete Poisson probability models.There were 26549, 13662, and 35905 notified cases of dengue, chikungunya, and Zika, respectively. The 17 dengue clusters and 15 Zika clusters were spread all over the city, while the 14 chikungunya clusters were more concentrated in the North and Downtown areas. Zika clusters persisted over a longer period of time. The multivariate scan statistic – used to analyse the three diseases simultaneously – detected 17 clusters, nine of which included all three diseases.This is the first study exploring space-time clustering of dengue, chikungunya, and Zika in an intraurban area. In general, the clusters did not coincide in time and space. This is probably the result of the competition between viruses for host resources, and of vector-control attitudes promoted by previous arbovirus outbreaks. The main affected area – the North region – is characterised by a combination of high population density and low human development index, highlighting the importance of targeting interventions in this area. Spatio-temporal scan statistics have the potential to direct interventions to high-risk locations in a timely manner and should be considered as part of the municipal surveillance routine as a tool to optimize prevention strategies.Author summaryDengue, an arboviral disease transmitted by Aedes mosquitoes, has been endemic in Brazil for decades, but vector-control strategies have not led to a significant reduction in the disease burden and were not sufficient to prevent chikungunya and Zika entry and establishment in the country. In Rio de Janeiro city, the first Zika and chikungunya epidemics were detected between 2015-2016, coinciding with a dengue epidemic. Understanding the behaviour of these diseases in a triple epidemic scenario is a necessary step for devising better interventions for prevention and outbreak response. We applied scan statistics analysis to detect spatio-temporal clustering for each disease separately and for all three simultaneously. In general, clusters were not detected in the same locations and time periods, possibly due to competition between viruses for host resources, and change in behaviour of the human population (e.g. intensified vector-control activities in response to increasing cases of a particular arbovirus). Neighbourhoods with high population density and social vulnerability should be considered as important targets for interventions. Particularly in the North region, where clusters of the three diseases exist and the first chikungunya cluster occurred. The use of space-time cluster detection can direct intensive interventions to high-risk locations in a timely manner.

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 Space-Time Clustering Characteristics of Tuberculosis in Khyber Pakhtunkhwa Province, Pakistan, 2015–2019

2020 ◽  
Vol 17 (4) ◽  
pp. 1413
Author(s):  
Sami Ullah ◽  
Hanita Daud ◽  
Sarat C. Dass ◽  
Hadi Fanaee-T ◽  
Husnul Kausarian ◽  
...  
Keyword(s):  
Control Program ◽  
Space Time ◽  
Scan Statistics ◽  
Khyber Pakhtunkhwa ◽  
Effective Interventions ◽  
Khyber Pakhtunkhwa Province ◽  
Rural Part ◽  
Tb Control Program ◽  
Time Scan ◽  
Cool Climate

The number of tuberculosis (TB) cases in Pakistan ranks fifth in the world. The National TB Control Program (NTP) has recently reported more than 462,920 TB patients in Khyber Pakhtunkhwa province, Pakistan from 2002 to 2017. This study aims to identify spatial and space-time clusters of TB cases in Khyber Pakhtunkhwa province Pakistan during 2015–2019 to design effective interventions. The spatial and space-time cluster analyses were conducted at the district-level based on the reported TB cases from January 2015 to April 2019 using space-time scan statistics (SaTScan). The most likely spatial and space-time clusters were detected in the northern rural part of the province. Additionally, two districts in the west were detected as the secondary space-time clusters. The most likely space-time cluster shows a tendency of spread toward the neighboring districts in the central part, and the most likely spatial cluster shows a tendency of spread toward the neighboring districts in the south. Most of the space-time clusters were detected at the start of the study period 2015–2016. The potential TB clusters in the remote rural part might be associated to the dry–cool climate and lack of access to the healthcare centers in the remote areas.

scholarly journals Spatiotemporal Dynamics of Scrub Typhus in Jiangxi Province, China, from 2006 to 2018

2021 ◽  
Vol 18 (9) ◽  
pp. 4599
Author(s):  
Shu Yang ◽  
Xiaobo Liu ◽  
Yuan Gao ◽  
Baizhou Chen ◽  
Liang Lu ◽  
...  
Keyword(s):  
Spatial Distribution ◽  
High Risk ◽  
Scrub Typhus ◽  
Space Time ◽  
Spatiotemporal Dynamics ◽  
Scan Statistics ◽  
Significant Heterogeneity ◽  
Targeted Interventions ◽  
Hilly Region ◽  
Time Scan

Background: Scrub typhus (ST) has become a significant potential threat to public health in Jiangxi. Further investigation is essential for the control and management of the spatiotemporal patterns of the disease. Methods: Time-series analyses, spatial distribution analyses, spatial autocorrelation analysis, and space-time scan statistics were performed to detect spatiotemporal dynamics distribution of the incidence of ST. Results: From 2006 to 2018, a total of 5508 ST cases occurred in Jiangxi, covering 79 counties. The number of ST cases increased continuously from 2006 to 2018, and there was obvious seasonality during the variation process in each year, with a primary peak in autumn (September to October) and a smaller peak in summer (June to August). From 2007 to 2018, the spatial distribution of the ST epidemic was significant heterogeneity, and Nanfeng, Huichang, Xunwu, Anyuan, Longnan, and Xinfeng were hotspots. Seven spatiotemporal clusters were observed using Kulldorff’s space-time scan statistic, and the most likely cluster only included one county, Nanfeng county. The high-risk areas of the disease were in the mountainous, hilly region of Wuyi and the southern mountainous region of Jiangxi. Conclusions: Targeted interventions should be executed in high-risk regions for the precise prevention and control of ST.

Sign in / Sign up

Export Citation Format

Share Document