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

2004 ◽  
Author(s):  
Daniel B. Neill ◽  
A. Moore
Keyword(s):  
Scan Statistic ◽  
Disease Clusters ◽  
Grid Based

Flexible scan statistic test to detect disease clusters in hierarchical trees

2011 ◽  
Vol 27 (4) ◽  
pp. 715-737 ◽  
Author(s):  
Marcos O. Prates ◽  
Renato M. Assunção ◽  
Marcelo A. Costa
Keyword(s):  
Scan Statistic ◽  
Disease Clusters ◽  
Hierarchical Trees

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

2018 ◽  
Vol 41 (1) ◽  
pp. 65-72 ◽  
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.

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

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.

scholarly journals Spatially varying effects of measured confounding variables on disease risk

2021 ◽  
Vol 20 (1) ◽  
Author(s):  
Chih-Chieh Wu ◽  
Yun-Hsuan Chu ◽  
Sanjay Shete ◽  
Chien-Hsiun Chen
Keyword(s):  
Risk Factors ◽  
Disease Risk ◽  
Disease Incidence ◽  
Spatial Scan Statistic ◽  
Scan Statistic ◽  
Disease Clusters ◽  
Peak Incidence ◽  
Confounding Variables ◽  
And Gender ◽  
Spatially Varying

Abstract Background The presence of considerable spatial variability in incidence intensity suggests that risk factors are unevenly distributed in space and influence the geographical disease incidence distribution and pattern. As most human common diseases that challenge investigators are complex traits and as more factors associated with increased risk are discovered, statistical spatial models are needed that investigate geographical variability in the association between disease incidence and confounding variables and evaluate spatially varying effects on disease risk related to known or suspected risk factors. Information on geography that we focus on is geographical disease clusters of peak incidence and paucity of incidence. Methods We proposed and illustrated a statistical spatial model that incorporates information on known or hypothesized risk factors, previously detected geographical disease clusters of peak incidence and paucity of incidence, and their interactions as covariates into the framework of interaction regression models. The spatial scan statistic and the generalized map-based pattern recognition procedure that we recently developed were both considered for geographical disease cluster detection. The Freeman-Tukey transformation was applied to improve normality of distribution and approximately stabilize the variance in the model. We exemplified the proposed method by analyzing data on the spatial occurrence of sudden infant death syndrome (SIDS) with confounding variables of race and gender in North Carolina. Results The analysis revealed the presence of spatial variability in the association between SIDS incidence and race. We differentiated spatial effects of race on SIDS incidence among previously detected geographical disease clusters of peak incidence and incidence paucity and areas outside the geographical disease clusters, determined by the spatial scan statistic and the generalized map-based pattern recognition procedure. Our analysis showed the absence of spatial association between SIDS incidence and gender. Conclusion The application to the SIDS incidence data demonstrates the ability of our proposed model to estimate spatially varying associations between disease incidence and confounding variables and distinguish spatially related risk factors from spatially constant ones, providing valuable inference for targeted environmental and epidemiological surveillance and management, risk stratification, and thorough etiologic studies of disease.

Reciprocal Polarizable Embedding with a Transferable H2O Potential Function I: Formulation & Tests on Dimer

2019 ◽  
Author(s):  
Elvar Jónsson ◽  
Asmus Ougaard Dohn ◽  
Hannes Jonsson
Keyword(s):  
Potential Function ◽  
Dft Method ◽  
Multipole Expansion ◽  
Energy Functional ◽  
Real Space ◽  
Functional Formulation ◽  
General Energy ◽  
Projector Augmented Wave ◽  
Grid Based ◽  
Polarizable Embedding

This work describes a general energy functional formulation of a polarizable embedding QM/MM scheme, as well as an implementation where a real-space Grid-based Projector Augmented Wave (GPAW) DFT method is coupled with a potential function for H<sub>2</sub>O based on a Single Center Multipole Expansion (SCME) of the electrostatics, including anisotropic dipole and quadrupole polarizability.

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