A Space-Time Permutation Scan Statistic for Evaluating County-Level Tickborne Disease Clusters in Indiana, 2009-2016

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.

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Spatio-Temporal Characteristics of the Bluetongue Epizooty in the Balkan Peninsula from 2014 to February 2015

Macedonian Veterinary Review ◽

10.1515/macvetrev-2017-0033 ◽

2018 ◽

Vol 41 (1) ◽

pp. 65-72 ◽

Cited By ~ 1

Author(s):

Zharko Stojmanovski ◽

Blagojcho Tabakovski

Keyword(s):

Balkan Peninsula ◽

Space Time ◽

Scan Statistics ◽

Security Measures ◽

Scan Statistic ◽

Disease Clusters ◽

Temporal Characteristics ◽

The Balkans ◽

Spatio Temporal ◽

Spatial And Temporal Characteristics

Abstract Starting in May 2014 an emerging Bluetongue (BT) serotype 4 (BTV-4) epizooty has affected the ruminant population of eleven countries from the Balkan Peninsula. Consequently, the veterinary services implemented various bio-security measures and a considerable discussion has been raised if future BTV surveillance and preventive measures should be taken in risk based zones and periods. Therefore, the objective of this work was to describe the spatial and temporal characteristics of the BTV-4 epizooty in the Balkan Peninsula from May 2014 to February 2015. We used the space-time permutation model of the scan statistic to identify the space-time disease clusters. The scan statistic was parameterized to a maximum temporal length of 150 days (duration of the epizooty in the Balkans in 2014) and a radius of 100 km as a maximum spatial cluster size (protection zone for BT). Results were significant (p < 0.05) to the maximum spatial size defined for the clusters. From the 6295 BT outbreaks the scan statistics identified 33 disease clusters in nine Balkan countries. The highest number of outbreaks occurred from September to November 2014.The earliest cluster was detected in Greece in July 2014 with a radius of 56 km. The latest cluster was detected in Croatia in February 2015 with a radius of 99,8 km. These results are a first description of the spatial and temporal characteristics of the 2014-February 2015 BT epizooty in the Balkans.

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Space-time permutation model applied to the past outbreak data of lumpy skin disease in the Balkan Peninsula from august 2015 to july 2017

Veterinarski glasnik ◽

10.2298/vetgl171027003s ◽

2018 ◽

Vol 72 (1) ◽

pp. 44-55

Author(s):

Zharko Stojmanovski

Keyword(s):

Skin Disease ◽

Balkan Peninsula ◽

Space Time ◽

Scan Statistic ◽

Lumpy Skin Disease ◽

Serbia And Montenegro ◽

Disease Clusters ◽

Risk Areas ◽

Permutation Model ◽

First Time

Introduction: In August 2015, lumpy skin disease (LSD) was notified for the first time in mainland European Union when it was observed in cattle in Greece. From August 2015 to July 2017, 1,757 outbreaks were reported in cattle in Greece, Bulgaria, Macedonia, Albania, Serbia, and Montenegro. Materials and Methods: The Kulldorff space-time permutation scan statistic contained in the software package SaTScan v 9.4.4 was used to analyse the epizootic past outbreak data and describe the spread of the disease in the 24 months after the first notification. Results and Conclusions:: Seventy-six space-time disease clusters (62 significant and 14 non-significant) were identified. In 2015, 10 clusters with a monthly peak in October (n=5, 50%) were identified, in 2016, the most (n=57) clusters were detected with monthly peak in July (n=15, 26.3%), and up to July 2017, nine clusters with a monthly peak in May (n=3, 3.3%) were determined. Possible high-risk areas were identified using the presented methodology, and so this technique could guide national veterinary authorities to formulate strategies for mitigating the spread of LSD, allocating resources and for formulating epidemiological preparedness plans in neighbouring, LSD-negative, countries.

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Fast Grid-Based Scan Statistic for Detection of Significant Spatial Disease Clusters

PsycEXTRA Dataset ◽

10.1037/e307182005-065 ◽

2004 ◽

Cited By ~ 7

Author(s):

Daniel B. Neill ◽

A. Moore

Keyword(s):

Scan Statistic ◽

Disease Clusters ◽

Grid Based

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Adaptive likelihood ratio methods for the detection of space-time disease clusters

Emerging Health Threats Journal ◽

10.3402/ehtj.v4i0.11150 ◽

2011 ◽

Vol 4 (0) ◽

Author(s):

Max Sousa de Lima ◽

Luiz Duczmal

Keyword(s):

Likelihood Ratio ◽

Space Time ◽

Disease Clusters

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

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph18115553 ◽

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.

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