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

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.

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

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

2020 ◽  
Vol 118 ◽  
pp. 102202 ◽  
Author(s):  
M.R. Desjardins ◽  
A. Hohl ◽  
E.M. Delmelle
Keyword(s):  
United States ◽  
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 Applying a Prospective Space-Time Scan Statistic to Examine the Evolution of COVID-19 Clusters in the State of São Paulo, Brazil

2020 ◽  
Author(s):  
R.V. Ferreira ◽  
M.R. Martines ◽  
R.H. Toppa ◽  
L.M. Assunção ◽  
M.R. Desjardins ◽  
...  
Keyword(s):  
Public Management ◽  
Sao Paulo ◽  
Space Time ◽  
The State ◽  
List Type ◽  
São Paulo ◽  
Significant Cluster ◽  
Scan Statistic ◽  
First Case ◽  
Time Scan

AbstractWe present the first geographic study that uses space-time statistics to monitor COVID-19 in Brazil. The first cases of COVID-19 were confirmed in December 2019 in Wuhan, China, caused by the contamination of the SARS-CoV-2 virus, and quickly turned into a pandemic. In Brazil, the first case occurred on January 23rd, 2020 but was officially reported by the Brazilian Ministry of Health on February 25th. Since then, the number of deaths and people infected by COVID-19 in Brazil have been steadily increasing. Despite the underreporting of coronavirus cases by government agencies across the country, the State of São Paulo has the highest rate among all Brazilian States. Thus, it is essential to detect which areas contain the highest concentration of COVID-19 to implement public policies, to mitigate the spread of the epidemic. To identify these critical areas, we utilized daily confirmed case data from the Brasil.IO website between February 25th, 2020 to May 5th, 2020; which were aggregated to the municipality level. A prospective space-time scan statistic was applied to evaluate possible active clusters in three different time periods. The results visualize the space-time evolution and dynamics of COVID-19 clusters in the State of São Paulo. Since the first study period, the results highlight approximately 4.6 times the number of municipalities belonging to a significant cluster with a RR>1 on May 5th. These results can inform health authorities and public management to take the necessary measures to minimize the transmission of COVID-19 and track the evolution of significant space-time clusters.HIGHLIGHTSProspective space-time statistics can improve COVID-19 surveillance in BrazilAll statistically significant clusters are located near São Paulo MunicipalityThere are municipalities with relative risk highest than one in the countryside4.6 times the number of municipalities belong to a significant cluster on May 5th

scholarly journals Using space–time scan statistic for studying the effects of COVID-19 in Punjab, Pakistan: a guideline for policy measures in regional agriculture

2021 ◽  
Author(s):  
Sajjad Hussain ◽  
Muhammad Mubeen ◽  
Ashfaq Ahmad ◽  
Shah Fahad ◽  
Wajid Nasim ◽  
...  
Keyword(s):  
Space Time ◽  
Scan Statistic ◽  
Policy Measures ◽  
Time Scan

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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