scholarly journals A model-adjusted space–time scan statistic with an application to syndromic surveillance

2005 ◽  
Vol 133 (3) ◽  
pp. 409-419 ◽  
Author(s):  
K. P. KLEINMAN ◽  
A. M. ABRAMS ◽  
M. KULLDORFF ◽  
R. PLATT
Keyword(s):  
Temporal Trends ◽  
Space Time ◽  
Local History ◽  
Group Practice ◽  
Scan Statistic ◽  
Disease Clusters ◽  
Geographical Patterns ◽  
Naturally Occurring ◽  
History Of ◽  
Time Scan

The space–time scan statistic is often used to identify incident disease clusters. We introduce a method to adjust for naturally occurring temporal trends or geographical patterns in illness. The space–time scan statistic was applied to reports of lower respiratory complaints in a large group practice. We compared its performance with unadjusted populations from: (1) the census, (2) group-practice membership counts, and on adjustments incorporating (3) day of week, month, and holidays; and (4) additionally, local history of illness. Using a nominal false detection rate of 5%, incident clusters during 1 year were identified on 26, 22, 4 and 2% of days for the four populations respectively. We show that it is important to account for naturally occurring temporal and geographic trends when using the space–time scan statistic for surveillance. The large number of days with clusters renders the census and membership approaches impractical for public health surveillance. The proposed adjustment allows practical surveillance.

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.

scholarly journals An unconditional space–time scan statistic for ZIP‐distributed data

2018 ◽  
Vol 46 (1) ◽  
pp. 142-159 ◽  
Author(s):  
Benjamin Allévius ◽  
Michael Höhle
Keyword(s):  
Space Time ◽  
Distributed Data ◽  
Scan Statistic ◽  
Time Scan

Rapid detection of COVID-19 clusters in the United States using a prospective space-time scan statistic

2020 ◽  
Vol 12 (1) ◽  
pp. 27-33
Author(s):  
Alexander Hohl ◽  
Eric Delmelle ◽  
Michael Desjardins
Keyword(s):  
United States ◽  
Rapid Detection ◽  
The United States ◽  
Space Time ◽  
Scan Statistic ◽  
Time Scan

A Space-Time Scan Statistic for Detecting Emerging Outbreaks

Biometrics ◽  
2010 ◽  
Vol 67 (1) ◽  
pp. 106-115 ◽  
Author(s):  
Toshiro Tango ◽  
Kunihiko Takahashi ◽  
Kazuaki Kohriyama
Keyword(s):  
Space Time ◽  
Scan Statistic ◽  
Time Scan

scholarly journals Daily surveillance of COVID-19 using the prospective space-time scan statistic in the United States

2020 ◽  
Vol 34 ◽  
pp. 100354 ◽  
Author(s):  
Alexander Hohl ◽  
Eric M. Delmelle ◽  
Michael R. Desjardins ◽  
Yu Lan
Keyword(s):  
United States ◽  
The United States ◽  
Space Time ◽  
Scan Statistic ◽  
Time Scan

scholarly journals Evaluating cluster alarms: a space-time scan statistic and brain cancer in Los Alamos, New Mexico.

1998 ◽  
Vol 88 (9) ◽  
pp. 1377-1380 ◽  
Author(s):  
M Kulldorff ◽  
W F Athas ◽  
E J Feurer ◽  
B A Miller ◽  
C R Key
Keyword(s):  
New Mexico ◽  
Brain Cancer ◽  
Space Time ◽  
Scan Statistic ◽  
Los Alamos ◽  
Time Scan

scholarly journals Exploring spatial patterns of cardiovascular disease in Sweden between 2000 and 2010

2018 ◽  
Vol 46 (6) ◽  
pp. 647-658 ◽  
Author(s):  
Mohammadreza Rajabi ◽  
Ali Mansourian ◽  
Petter Pilesjö ◽  
Daniel Oudin Åström ◽  
Klas Cederin ◽  
...  
Keyword(s):  
Cardiovascular Disease ◽  
Spatial Patterns ◽  
Temporal Trends ◽  
Moran’S I ◽  
Scan Statistic ◽  
Moran's I ◽  
Mortality And Morbidity ◽  
Admission Rates ◽  
Geographical Patterns ◽  
Causes Of Mortality

Aims: Cardiovascular disease (CVD) is one of the leading causes of mortality and morbidity worldwide, including in Sweden. The main aim of this study was to explore the temporal trends and spatial patterns of CVD in Sweden using spatial autocorrelation analyses. Methods: The CVD admission rates between 2000 and 2010 throughout Sweden were entered as the input disease data for the analytic processes performed for the Swedish capital, Stockholm, and also for the whole of Sweden. Age-adjusted admission rates were calculated using a direct standardisation approach for men and women, and temporal trends analysis were performed on the standardised rates. Global Moran’s I was used to explore the structure of patterns and Anselin’s local Moran’s I, together with Kulldorff’s scan statistic were applied to explore the geographical patterns of admission rates. Results: The rates followed a spatially clustered pattern in Sweden with differences occurring between sexes. Accordingly, hot spots were identified in northern Sweden, with higher intensity identified for men, together with clusters in central Sweden. Cold spots were identified in the adjacency of the three major Swedish cities of Stockholm, Gothenburg and Malmö. Conclusions: The findings of this study can serve as a basis for distribution of health-care resources, preventive measures and exploration of aetiological factors.

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