Approximating the Distribution of the Multiple Scan Statistic

AbstractThe contribution of the theory of scan statistics to the study of many real-life applications has been rapidly expanding during the last decades. The multiple scan statistic, defined on a sequence of n Bernoulli trials, enumerates the number of occurrences of k consecutive trials which contain at least r successes among them (r≤k≤n). In this paper we establish some asymptotic results for the distribution of the multiple scan statistic, as n,k,r→∞ and illustrate their accuracy through a simulation study. Our approach is based on an appropriate combination of compound Poisson approximation and random walk theory.

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Asymptotic results for jump probabilities associated to the multiple scan statistic

Annals of the Institute of Statistical Mathematics ◽

10.1007/s10463-017-0621-1 ◽

2017 ◽

Vol 70 (5) ◽

pp. 951-968 ◽

Cited By ~ 3

Author(s):

Markos V. Koutras ◽

Demetrios P. Lyberopoulos

Keyword(s):

Scan Statistic ◽

Asymptotic Results ◽

Multiple Scan

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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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Optimizing Thresholds of the Scan Statistic to Improve its Worst Case Detection Performance in Sensor Detection Systems

IEEE Transactions on Signal and Information Processing over Networks ◽

10.1109/tsipn.2021.3069300 ◽

2021 ◽

pp. 1-1

Author(s):

Benedito Fonseca

Keyword(s):

Detection Performance ◽

Case Detection ◽

Scan Statistic ◽

Worst Case ◽

Detection Systems ◽

Sensor Detection

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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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Real time surveillance of COVID-19 space and time clusters during the summer 2020 in Spain

BMC Public Health ◽

10.1186/s12889-021-10961-z ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Nicolás Rosillo ◽

Javier Del-Águila-Mejía ◽

Ayelén Rojas-Benedicto ◽

María Guerrero-Vadillo ◽

Marina Peñuelas ◽

...

Keyword(s):

Real Time ◽

Scan Statistic ◽

Cluster Aggregation ◽

Space And Time ◽

Spatial Trends ◽

Community Transmission ◽

Low Incidence ◽

Set Up ◽

And Control ◽

Time Scan

Abstract Background On June 21st de-escalation measures and state-of-alarm ended in Spain after the COVID-19 first wave. New surveillance and control strategy was set up to detect emerging outbreaks. Aim To detect and describe the evolution of COVID-19 clusters and cases during the 2020 summer in Spain. Methods A near-real time surveillance system to detect active clusters of COVID-19 was developed based on Kulldorf’s prospective space-time scan statistic (STSS) to detect daily emerging active clusters. Results Analyses were performed daily during the summer 2020 (June 21st – August 31st) in Spain, showing an increase of active clusters and municipalities affected. Spread happened in the study period from a few, low-cases, regional-located clusters in June to a nationwide distribution of bigger clusters encompassing a higher average number of municipalities and total cases by end-August. Conclusion STSS-based surveillance of COVID-19 can be of utility in a low-incidence scenario to help tackle emerging outbreaks that could potentially drive a widespread transmission. If that happens, spatial trends and disease distribution can be followed with this method. Finally, cluster aggregation in space and time, as observed in our results, could suggest the occurrence of community transmission.

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