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

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

Scandinavian Journal of Statistics ◽

10.1111/sjos.12341 ◽

2018 ◽

Vol 46 (1) ◽

pp. 142-159 ◽

Cited By ~ 1

Author(s):

Benjamin Allévius ◽

Michael Höhle

Keyword(s):

Space Time ◽

Distributed Data ◽

Scan Statistic ◽

Time Scan

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

SIGSPATIAL Special ◽

10.1145/3404111.3404116 ◽

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

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Application of GIS and Space-Time Scan Statistic for Vector Born Disease Clustering

Proceedings of the 10th International Conference on Theory and Practice of Electronic Governance - ICEGOV '17 ◽

10.1145/3047273.3047361 ◽

2017 ◽

Cited By ~ 1

Author(s):

Mahesh Kumar Jat ◽

Shuchi Mala

Keyword(s):

Space Time ◽

Scan Statistic ◽

Disease Clustering ◽

Time Scan ◽

Application Of Gis

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Identification of TB space-time clusters and hotspots in Ouest département, Haiti, 2011–2016

Public Health Action ◽

10.5588/pha.20.0085 ◽

2021 ◽

Vol 11 (2) ◽

pp. 101-107

Author(s):

A. M. Dismer ◽

M. Charles ◽

N. Dear ◽

J. M. Louis-Jean ◽

N. Barthelemy ◽

...

Keyword(s):

Metropolitan Area ◽

Urban Areas ◽

Control Program ◽

High Volume ◽

Space Time ◽

Case Finding ◽

Western Hemisphere ◽

Spatial Clusters ◽

Tb Control ◽

Tb Control Program

BACKGROUND: Haiti has the highest incidence rate of TB in the Western Hemisphere, with an estimated 170 cases per 100,000 in 2019. Since 2010, control efforts have focused on targeted case-finding activities in urban areas, implementation of rapid molecular diagnostics at high-volume TB centers, and improved reporting. TB analyses are rarely focused on lower geographic units; thus, the major goal was to determine if there were focal areas of TB transmission from 2011 to 2016 at operational geographic levels useful for the National TB Control Program (PNLT).METHODS: We created a geocoder to locate TB cases at the smallest geographic level. Kulldorff’s space-time permutation scan, Anselin Moran’s I, and Getis-Ord Gi* statistics were used to determine the spatial distribution and clusters of TB.RESULTS: With 91% of cases linked using the geocoder, TB clusters were identified each year. Getis-Ord Gi* analysis revealed 14 distinct spatial clusters of high incidences in the Port-au-Prince metropolitan area. One hundred retrospective space-time clusters were detected.CONCLUSION: Our study confirms the presence of TB hotspots in the Ouest département, with most clusters in the Port-au-Prince metropolitan area. Results will help the PNLT and its partners better design case-finding strategies for these areas.

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Rapid surveillance of COVID-19 in the United States using a prospective space-time scan statistic: Detecting and evaluating emerging clusters

Applied Geography ◽

10.1016/j.apgeog.2020.102202 ◽

2020 ◽

Vol 118 ◽

pp. 102202 ◽

Cited By ~ 60

Author(s):

M.R. Desjardins ◽

A. Hohl ◽

E.M. Delmelle

Keyword(s):

United States ◽

The United States ◽

Space Time ◽

Scan Statistic ◽

Time Scan

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A Space-Time Scan Statistic for Detecting Emerging Outbreaks

Biometrics ◽

10.1111/j.1541-0420.2010.01412.x ◽

2010 ◽

Vol 67 (1) ◽

pp. 106-115 ◽

Cited By ~ 21

Author(s):

Toshiro Tango ◽

Kunihiko Takahashi ◽

Kazuaki Kohriyama

Keyword(s):

Space Time ◽

Scan Statistic ◽

Time Scan

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

Spatial and Spatio-temporal Epidemiology ◽

10.1016/j.sste.2020.100354 ◽

2020 ◽

Vol 34 ◽

pp. 100354 ◽

Cited By ~ 8

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

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Searching for space-time clusters: The CutL method compared to Kulldorff’s scan statistic

Geospatial health ◽

10.4081/gh.2019.791 ◽

2019 ◽

Vol 14 (2) ◽

Author(s):

Barbara Więckowska ◽

Ilona Górna ◽

Maciej Trojanowski ◽

Agata Pruciak ◽

Barbara Stawińska-Witoszyńska

Keyword(s):

Disease Incidence ◽

Incidence Rates ◽

Space Time ◽

Care Planning ◽

Spatial Clusters ◽

Health Care Planning ◽

Scan Statistic ◽

Software Company ◽

The One ◽

Analytical Tools

Both epidemiology and health care planning require analytical tools, especially for cluster detection in cases with unusually high rates of disease incidence. The aim of this work was to extend the application of the CutL method, which is used for detecting spatial clusters of any shape, to detecting space-time clusters, and to show how the method works compared to Kulldorff’s scan statistic. In the CutL method, clusters with disease incidence rates higher than the one entered by the researcher are searched for. The way in which the space-time version of that method works is illustrated with the example of data simulating the distribution of people affected by health problems in Polish counties in the period 2013- 2017. With respect to detection of irregularly shaped space-time clusters, the CutL method turned out to be more effective than Kulldorff’s scan statistic; for cylinder-shaped space-time clusters, the two methods produced similar results. The CutL method has also the important advantage of being widely accessible through the PQScut and PQStat programmes (PQStat Software Company, Poznan, Poland).

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

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph17041413 ◽

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.

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