scholarly journals Online platform for applying space–time scan statistics for prospectively detecting emerging hot spots of dengue fever

2016 ◽  
Vol 15 (1) ◽  
Author(s):  
Chien-Chou Chen ◽  
Yung-Chu Teng ◽  
Bo-Cheng Lin ◽  
I-Chun Fan ◽  
Ta-Chien Chan
Keyword(s):  
Dengue Fever ◽  
Hot Spots ◽  
Space Time ◽  
Scan Statistics ◽  
Online Platform ◽  
Time Scan

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 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.

scholarly journals Sociodemographic and spatiotemporal profiles of hepatitis-A in the state of Pará, Brazil, based on reported notified cases

2021 ◽  
Vol 16 (2) ◽  
Author(s):  
Philipe Riskalla Leal ◽  
Ricardo José de Paula e Sousa Guimarães ◽  
Milton Kampel
Keyword(s):  
Vaccination Programme ◽  
Hepatitis A ◽  
Hepatitis A Virus ◽  
Space Time ◽  
The State ◽  
Scan Statistics ◽  
Spatiotemporal Variability ◽  
Effective Control ◽  
Age Group ◽  
Time Scan

Hepatitis-A virus is a worldwide healthcare problem, mainly affecting countries with poor sanitary and socioeconomic conditions. This communication evaluates the spatiotemporal variability of the disease’s socioepidemiological profile in one of the endemic Brazilian regions (Pará State) prior to (2008-2013) and after (2014-2017) the launch of the national public vaccination programme. Hepatitis-A epidemiological reports concerning Pará State - Brazil - were used for this study including municipalitylevel data of the disease’s reported positive notification cases (PNCs). The analyses involved socioepidemiological profiling and space-time scan statistics. A total of 5500 PNCs were reported in the study period. On average, PNCs decreased over time throughout the state, with strongest drops after 2015. The PNCs were specific for gender, race/ethnic origin and age group. The predominant gender and race/ethnic groups was male and brown, respectively. While children were the most susceptible age group prior to 2015, there was a shift towards older ages (young and adults) in later years. Those found to be the most affected by the disease, as shown by space-time scan statistics, were people in densely populated municipalities with unsatisfactory sanitary conditions and also less well covered by the public vaccination programme. Despite drops in the number of hepatitis-A PNCs, thanks to the national vaccination programme, the disease still persists in Pará State and elsewhere in Brazil. The present study reinforces the need of continuous prevention and control strategies for effective control and erradication of hepatitis-A.

scholarly journals A comparison of prospective space-time scan statistics and spatiotemporal event sequence based clustering for COVID-19 surveillance

PLoS ONE ◽  
2021 ◽  
Vol 16 (6) ◽  
pp. e0252990
Author(s):  
Fuyu Xu ◽  
Kate Beard
Keyword(s):  
Sequence Similarity ◽  
The United States ◽  
Incidence Rates ◽  
Space Time ◽  
Scan Statistics ◽  
World Health ◽  
Disease Spread ◽  
Event Sequence ◽  
Scan Statistic ◽  
Time Scan

The outbreak of the COVID-19 disease was first reported in Wuhan, China, in December 2019. Cases in the United States began appearing in late January. On March 11, the World Health Organization (WHO) declared a pandemic. By mid-March COVID-19 cases were spreading across the US with several hotspots appearing by April. Health officials point to the importance of surveillance of COVID-19 to better inform decision makers at various levels and efficiently manage distribution of human and technical resources to areas of need. The prospective space-time scan statistic has been used to help identify emerging COVID-19 disease clusters, but results from this approach can encounter strategic limitations imposed by constraints of the scanning window. This paper presents a different approach to COVID-19 surveillance based on a spatiotemporal event sequence (STES) similarity. In this STES based approach, adapted for this pandemic context we compute the similarity of evolving daily COVID-19 incidence rates by county and then cluster these sequences to identify counties with similarly trending COVID-19 case loads. We analyze four study periods and compare the sequence similarity-based clusters to prospective space-time scan statistic-based clusters. The sequence similarity-based clusters provide an alternate surveillance perspective by identifying locations that may not be spatially proximate but share a similar disease progression pattern. Results of the two approaches taken together can aid in tracking the progression of the pandemic to aid local or regional public health responses and policy actions taken to control or moderate the disease spread.

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