Space-time Permutation Scan Statistic Research Articles

Space-time analysis of the relationship between landslides occurrence, rainfall variability and ENSO in the Tropical Andean Mountain region in Colombia

According to studies by the Intergovernmental Panel on Climate Change (IPCC), tropical mountainous areas are experiencing increasingly unfavorable climatic conditions regarding geohazards due to a heightened occurrence of intense rainfall events. These climatic shifts contribute to heightened geological risks, notably an elevated frequency of landslides, exacerbating the challenges faced by these regions. There is an urgent need to understand and measure how rainfall variability affects geo-hydrological hazards, which remain difficult to determine and predict. The complex and non-linear space-time relationships and dynamics of rainfall, El Niño-Southern Oscillation (ENSO), and landslides in the Tropical Andean Mountain region in Colombia require an adequate analysis and understanding of their link in terms of its spatial and temporal component at different scales. By evaluation in annual, seasonal, and monthly scales, additional insights on the relationships using a wavelet spectral analysis and a space-time permutation scan statistics method using SaTScan™ are provided. In this study, a space-time and frequency analysis of landslides using a 42-year (1981–2022) rainfall and Multivariate ENSO Index v2.0 time series is presented. According to the results, landslides closely aligned with rainfall patterns, exhibiting a bimodal annual cycle. The ENSO added complexity, with La Niña years leading to more frequent landslides throughout the year and El Niño years showing concentrated occurrences in specific months. This study highlights the influence of rainfall patterns and antecedent rainfall on landslide occurrence, the impact of ENSO phases on rainfall and landslides, and the increasing trend of landslides in Colombia.

Space-time permutation statistics application to detect cloud-to-ground lightning prone area (Case study: Pasuruan, Indonesia)

Lightning is a natural phenomenon caused by the release of positive and negative charges occur in cumulonimbus (CB) clouds. CG lightning is a lightning that strikes from the clouds to the ground. This lightning is dangerous for human activities which can cause burns, blindness, and even temporary deafness. This research will determine the areas prone to CG lightning strikes and lightning characteristics in the city of Pasuruan. Space-time permutation scan statistics is a method used to detect prone areas by considering spatial and temporal aspects. This method merely requires case data, such as location and time without using population data. The detected prone areas will be tested for significance using Monte Carlo. It is used to determine the distribution of the sample. In this study, the Monte Carlo test for scanning window is 0.048 (p-value < 0.05), Sekargadung. Thus, making Sekargadung a hotspot for lightning-prone areas. Furthermore, the value is taken based on the highest ratio test (LRT), 11.46, which is the most likely cluster. Based on space-time permutation statistics Sekargadung is the main hotspot prone area in this case study. It has 226 strikes with the intensity of their occurrence with characteristics area dominant paddy field.

Examining leopard attacks: spatio-temporal clustering of human injuries and deaths in Western Himalayas, India

Shared spaces in Africa and Asia accommodate both humans and big cats. This engenders rare but distinctive cases of human fatalities by lions, tigers, and leopards. Among big cats, leopards have the widest range and occur even among high densities of humans. This increased potential for encounters with humans results in attacks, exemplified most by India where 50% of the states report human injuries and deaths due to leopards. Himachal Pradesh (HP) state reported 30 lethal and 287 non-lethal leopard attacks on humans per year between 2004 – 2015 (N=317). Identifying patterns in big cat attacks on people facilitates targeted interventions for decreasing such fatalities. This study aims to detect if leopards are cluster-causing agents of human injuries and deaths. We identify the patterns of leopard attacks on humans in Himachal Pradesh by examining the following questions: (a) do leopard-attributed attacks on humans cluster in space and time? and among the leopard-attributed attacks (b) do unprovoked attacks on humans cluster spatio-temporally? and (c) what environmental factors are associated with the clustered leopard attacks on humans? We employed a space-time permutation scan statistic commonly used in epidemiology to test for spatio-temporal clustering of leopard attacks. Attacks were spread across 75% (~42,000 km sq.) of HP in 11 out of 12 districts. We found that 23% of attacks clustered into 12 significant spatio-temporal clusters. Nearly 14% of the leopard-attributed attacks (N=317) were unprovoked and attacks displaying “predatory” signs did not form significant clusters. Binomial regression models were run to test association of eight environmental factors with clustered attacks. We found that leopard-attributed attacks farther away from the protected area boundary and closer to the district boundary had higher probability of clustering. The framework developed in this study to identify the outbreak of unprovoked leopard attacks confirms the absence of dedicated “man-eaters” in the study region. This approach can be applied to adaptively manage human-wildlife conflict and it also demonstrates the utility of scan statistic in ecological research.

Open and shut: Identifying activity patterns by volunteer organizations active in disaster using space-time permutation scan statistics

After a catastrophic flood, the pace of residential cleanup is an important precondition of community resilience. When the process is fast, cleanup reduces additional health or economic risks by preventing structural or electrical damage from escalating. Mobilizing volunteers to complete cleanup work quickly is a challenge that the U.S. Natural Disaster Recovery Framework has assigned to the nonprofit sector. Nonprofit coordination networks—such as those convened through volunteer organizations active in disaster (VOAD) in the United States—aim to create systems of communication that improve the efficiency and equity of post-disaster cleanup. However, the process of coordination and the conditions that encourage quick action remain understudied. The aim of this paper is to identify the impacts of network coordination on the timeline of post-disaster cleanup using geospatial analytics. To do this, we apply a space-time permutation scan statistic (STPSS) to data on the speed at which organizations from the North Carolina VOAD (NC VOAD) closed requests for volunteer assistance following Hurricane Florence in 2018. STPSS results identify clusters of requests that were filled at different speeds through space and time. In total, we identified six space-time clusters indicative of coordinated cleanup. Exploration of clustered data helps to generate new questions about why coordination sometimes happened months after the disaster and suggests ways to use data exploration to inform network function and to leverage the unique capacities of individual nonprofits while also prioritizing housing resilience in socially vulnerable communities.

Space-time self-harm and suicide clusters in two cities in Taiwan.

Suicidal acts may cluster in time and space and lead to community concerns about further imitative suicidal episodes. Although suicide clusters have been researched in previous studies, less is known about the clustering of non-fatal suicidal behaviour (self-harm). Furthermore, most previous studies used crude temporal and spatial information, e.g., numbers aggregated by month and residence area, for cluster detection analysis. This study aimed to (i) identify space-time clusters of self-harm and suicide using daily incidence data and exact address and (ii) investigate the characteristics of cluster-related suicidal acts. Data on emergency department presentations for self-harm and suicide deaths in Taipei City and New Taipei City, Taiwan, were used in this study. In all-age and age-specific analyses, self-harm and suicide clusters were identified using space-time permutation scan statistics. A cut-off of 0.10 for the p value was used to identify possible clusters. Logistic regression was used to investigate the characteristics associated with cluster-related episodes. A total of 5,291 self-harm episodes and 1,406 suicides in Taipei City (2004-2006) and 20,531 self-harm episodes and 2,329 suicides in New Taipei City (2012-2016) were included in the analysis. In the two cities, two self-harm clusters (n [number of self-harm episodes or suicide deaths in the cluster]=4 and 8 in Taipei City), four suicide clusters (n=3 in Taipei City and n=4, 11 and 4 in New Taipei City) and two self-harm and suicide combined clusters (n=4 in Taipei City and n=8 in New Taipei City) were identified. Space-time clusters of self-harm, suicide, and self-harm and suicide combined accounted for 0.05%, 0.59%, and 0.08% of the respective groups of suicidal acts. Cluster-related episodes of self-harm and suicide were more likely to be male (adjusted odds ratio [aOR]=2.22, 95% confidence interval [CI] 1.26, 3.89) and young people aged 10-29years (aOR=2.72, 95% CI 1.43, 5.21) than their cluster-unrelated counterparts. Space-time clusters of self-harm, suicide, and self-harm and suicide combined accounted for a relatively small proportion of suicidal acts and were associated with some sex/age characteristics. Focusing on suicide deaths alone may underestimate the size of some clusters and/or lead to some clusters being overlooked. Future research could consider combining self-harm and suicide data and use social connection information to investigate possible clusters of suicidal acts.

Spatial diffusion of COVID-19 in Algeria during the third wave.

The differential geographic impact of the third wave of COVID-19 is unknown in Algeria. We thus analyze the spatiotemporal variations of cases and deaths of COVID-19 in Algeria, between January and mid-August 2021. Cases and deaths due to COVID-19 were aggregated at the wilaya (province) level. The space–time permutation scan statistic was applied retrospectively to identify spatial–temporal clusters of COVID-19 cases and deaths. We detected 14 spatio-temporal clusters of COVID-19 cases, with only one high risk cluster. Among the 13 low risk clusters, 7 clusters emerged before the start of the third wave and were mostly located in wilayas with lower population density compared to the clusters that emerged during the third wave. For deaths, the largest geographic low-risk cluster emerged in southern Algeria, between April and early July 2021. Northern and coastal wilayas should be prioritized when allocating resources and implementing various quarantine and isolation measures to slow viral transmission.

832Detecting spatial clusters of human salmonellosis in Victoria

Abstract Focus of Presentation ‘Cluster Tracker’ is an automated tool for spatial cluster detection of notifiable disease data collected by the Department of Health (DH), Victoria. The tool combines R statistical software and a SaTScan cluster detection algorithm (prospective space-time permutation scan statistic) to detect notifiable disease case clusters in Victoria and is presently implemented for salmonellosis (categorised by type and/or MLVA). The objective of the tool is to conduct an initial screening of case data to improve the prioritisation of salmonellosis cases for epidemiological investigation. Findings The Cluster Tracker tool parameters have been validated using historical data from 2017-2018, comparing DH outbreak and cluster investigations identified by usual surveillance activities with clusters detected by the Cluster Tracker tool. Parameter selection considered cluster detection agreement and disagreement, disease-specific epidemiological characteristics, and operational requirements. The Cluster Tracker tool was able to provide closely-aligned agreement with existing DH outbreak and cluster investigations using the validated parameters. Implications This automated spatial cluster detection tool complements existing desktop surveillance of salmonellosis notifications to enhance public health decision making, and serves as an example of how spatial methods can improve real-time surveillance. Key messages Advanced spatial statistical tools have a role alongside traditional methods to make better use of limited epidemiological capacity and improve the timeliness and prioritisation of surveillance activities for notifiable diseases.

A Spatio-temporal distribution analysis of vesicular stomatitis outbreak in Ecuador, 2018

Vesicular stomatitis (VS) is a viral disease primarily affecting cattle, swine, and equine causing economic losses. It is of particular interest because its outward signs are similar to those of foot-and-mouth disease. Outbreaks of VS occurred in several herds in Ecuador in 2018, affecting principally bovines. In this sense, the present study was conducted to characterize the temporal and spatial dynamics of Vesicular stomatitis occurrence between January and December 2018. During the study period, 583 animals with symptoms of VS were reported. In this way, tissue samples were collected, VS was diagnosed, and outbreaks were defined as herds with a confirmed positive test for the disease. Outbreaks were georeferenced, and Space-time clusters were used to determine zones where the number of reported outbreaks was more significant than expected. A space-time permutation scan statistic (STPSS) was used to identify hot spots of space-time interaction within patterns of the cases reported. Standard Monte Carlo Critical Value was used to test for the cluster's significance. A total of 399 outbreaks were presented in 18 provinces. Spatial scan statistics allowed the detection of four significant space-time clusters of VS outbreaks. The highest incidence was reported around week 35 and week 44, which were observed outbreaks increase in the country's north region. In this sense, clusters coincided with the areas with the highest incidence of outbreaks. Besides, maps showed places where the disease is not shared. The information showed in the present study may contribute to prevents VS spread into regions of Ecuador that is only sporadically affected by the disease. Monitoring in affected zones may lead to quick responses to possible outbreaks issuing alerts when there is a greater than typical risk of spreading the disease

Automated outbreak detection of hospital-associated pathogens: Value to infection prevention programs.

To assess the utility of an automated, statistically-based outbreak detection system to identify clusters of hospital-acquired microorganisms. Multicenter retrospective cohort study. The study included 43 hospitals using a common infection prevention surveillance system. A space-time permutation scan statistic was applied to hospital microbiology, admission, discharge, and transfer data to identify clustering of microorganisms within hospital locations and services. Infection preventionists were asked to rate the importance of each cluster. A convenience sample of 10 hospitals also provided information about clusters previously identified through their usual surveillance methods. We identified 230 clusters in 43 hospitals involving Gram-positive and -negative bacteria and fungi. Half of the clusters progressed after initial detection, suggesting that early detection could trigger interventions to curtail further spread. Infection preventionists reported that they would have wanted to be alerted about 81% of these clusters. Factors associated with clusters judged to be moderately or highly concerning included high statistical significance, large size, and clusters involving Clostridioides difficile or multidrug-resistant organisms. Based on comparison data provided by the convenience sample of hospitals, only 9 (18%) of 51 clusters detected by usual surveillance met statistical significance, and of the 70 clusters not previously detected, 58 (83%) involved organisms not routinely targeted by the hospitals' surveillance programs. All infection prevention programs felt that an automated outbreak detection tool would improve their ability to detect outbreaks and streamline their work. Automated, statistically-based outbreak detection can increase the consistency, scope, and comprehensiveness of detecting hospital-associated transmission.

Retrospect and Risk Analysis of Foot-and-Mouth Disease in China Based on Integrated Surveillance and Spatial Analysis Tools.

Foot-and-mouth disease (FMD) is a highly contagious disease of livestock and seriously affects the development of animal husbandry. It is necessary to defend the spread of FMD. To explore the distribution characteristics and transmission of FMD between 2010 and 2017 in China, Global Moran's I test and Getis-Ord Gi index were used to analyze the spatial cluster. A space-time permutation scan statistic was applied to analyze the spatio-temporal pattern. GIS-based method was employed to create a map representing the distribution pattern, directional trend, and hotspots for each outbreak. The number of cases was defined as the number of animals with FMD for the above analysis. We also constructed a phylogenetic tree to compare the homology and variation of FMD virus (FMDV) to provide a clue for the potential development of an effective vaccine. The results indicated that the FMD outbreaks in China had obvious time patterns and clusters in space and space-time, with the outbreaks concentrated in the first half of each year. The outbreaks of FMD decreased each year from 2010 with an obvious downward trend of hotspots. Spatial analysis revealed that the distribution of FMD outbreaks in 2010, 2015, and 2017 exhibited a clustered pattern. Space-time scanning revealed that the spatio-temporal clusters were centered in Guangdong, Tibet and the junction of Wuhan, Jiangxi, Anhui. Comparison of the spatial analysis and space-time analysis of FMD outbreaks revealed that Guangdong was the same cluster of the two in 2010. In addition, the directional trend analysis indicated that the FMD transmission was oriented northwest-southeast. The findings demonstrated that FMDV in China can be divided into three pedigrees and the homology of these strains is very high while comparing the first FMDV strain with the others. The data provide a basis for the effective monitoring and prevention of FMD, and for the development of an FMD vaccine in China.

Spatial-Temporal Clustering Analysis of Dating Violence in Gwanak-gu, Seoul and Its Policy Implications for Dating Violence Victim Protection

최근 데이트폭력이 사회적 주목을 끌면서 관련 연구도 활발히 이루어지고 있다. 다만, 데이트폭력을 분석한 종래의 연구들은 주로 미시적 수준에서 개인의 특성과 데이트 폭력 간의 상관관계에 초점을 두고 연구를 진행한 측면이 있다. 반면, 국외에서는 데이 트폭력을 사회구조적 관점에서 분석, 접근을 시도하는 실증연구들이 증가하고 있는 추세이다. 이러한 연구는 데이트폭력에 대한 단순한 분석을 넘어 예방에 관한 시사점을 제공한다는 점에서 중요한 의미를 갖는다. 그러나 그러한 국외연구에서조차 데이트폭력의 공간적 군집성에만 착안한 나머지, 시간적 분포에 대한 분석에는 한계를 보이고 있다. 이에, 본 연구는 시공간 순열검정 통계량(space-time permutation scan statistics, STPSS) 기법을 활용하여 데이트폭력의 시공간적 군집성을 분석하였다. STPSS모형은 독립변수의 영향력에 대한 가정 없이 데이트폭력의 유의미한 시공간적 분포를 분석할 수있는 모형으로, 이를 통해 본 연구는 데이트폭력이 시공간적으로 군집을 이루고 있을 뿐만 아니라, 특정 주거지에서 반복하여 발생함을 추정하였다. 이러한 추정을 토대로 데이 트폭력이 시공간적으로 군집을 이루는 지역에 대해서는 적절한 초기 대응 및 재발 방지를 통한 피해자 보호가 필요하며, 그러한 활동을 뒷받침할 수 있는 법적 근거가 마련되 어야 함을 제안하였다.Recently, a growing number of research has actively studied dating violence as several atrocious dating violence cases attracted social attention. However, previous researches examining dating violence mainly focused on the relationship between personal characteristics and dating violence at the individual-level. On the other hand, there is an increasing number of empirical researches attempting to understand dating violence at the structural-level. These previous researches are important in that they provide several policy implications beyond individual-levels. However, even these structural researches failed to consider temporal aspect of dating violence while only focusing on the spatial aspect of dating violence. Therefore, this study aimed to analyze both spatial and temporal aspect of dating violence by using space-time permutation scan statistics (STPSS) technique. The STPSS is useful for analyzing the possible spatial and temporal clusters of dating violence without assuming the influence of other socio-structural variables. This study found that dating violence occurred repeatedly in a specific residential area. Based on these assumptions and findings, this study suggested that victims of dating violence should be provided with more active police response at the early stage of dating violence incidence, and that legal basis should be established to support such police activities.

Detecting Drop-offs in Electronic Laboratory Reporting for Communicable Diseases in New York City.

The Bureau of Communicable Disease at the New York City Department of Health and Mental Hygiene receives an average of more than 1000 reports daily via electronic laboratory reporting. Rapid recognition of any laboratory reporting drop-off of test results for 1 or more diseases is necessary to avoid delays in case investigation and outbreak detection. We modified our outbreak detection approach using the prospective space-time permutation scan statistic in SaTScan. Instead of searching for spatiotemporal clusters of high case counts, we reconceptualized "space" as "laboratory" and instead searched for clusters of recent low reporting, overall and for each of 52 diseases and 10 hepatitis test types, within individual laboratories. Each analysis controlled for purely temporal trends affecting all laboratories and accounted for multiple testing. A SAS program automatically created input files, invoked SaTScan, and further processed SaTScan analysis results and output summaries to a secure folder. Analysts reviewed output weekly and reported concerning drop-offs to coordinators, who liaised with reporting laboratory staff to investigate and resolve issues. During a 42-week evaluation period, October 2017 to July 2018, we detected 62 unique signals of reporting drop-offs. Of these, 39 (63%) were verified as true drop-offs, including failures to generate or transmit files and programming errors. For example, a hospital laboratory stopped reporting influenza after changing a multiplex panel result from "positive" to "detected." Six drop-offs were detected despite low numbers of expected reports missing (<10 per drop-off). Our novel application of SaTScan identified a manageable number of possible electronic laboratory reporting drop-offs for investigation. Ongoing maintenance requirements are minimal but include accounting for laboratory mergers and referrals. Automated analyses facilitated rapid identification and correction of electronic laboratory reporting errors, even with small numbers of expected reports missing, suggesting that our approach might be generalizable to smaller jurisdictions.

Detecting spatial-temporal clusters of violent behavior in South Korea with space-time permutation scan statistics

PurposeThe purpose of this paper is to detect spatial-temporal clusters of violence in Gwanak-gu, Seoul with space-time permutation scan statistics (STPSS) and identifies the temporal threshold for such detection to alert law enforcement officers quickly.Design/methodology/approachThe case study was the Gwanak Police Station Call Database 2017 where civilian calls reporting violence were georeferenced with coordinated points. In analyzing the database, this study used the STPSS requiring only individual case data, such as time and location, to detect clusters of investigated phenomena. This study executed a series of experiments using different minimum and maximum temporal thresholds in detecting clusters of violence.FindingsResults of the STPSS analyses with different temporal thresholds detected spatial-temporal clusters in Gwanak-gu. Number, location and duration of clusters depended on the temporal settings of the scanning window. Among four models, a model allowing the possible clusters to be detected within a 7-day minimum and 30-day maximum temporal threshold was more representative of reality than other models.Originality/valueThis study illustrates the clustering of violence with the STPSS by detecting spatial-temporal clusters of violence and identifying the appropriate temporal threshold in detecting such clusters. Identification of such a threshold is useful to alert law enforcement officers quickly and enables them to allocate their resources optimally.

Space-time cluster analysis to improve classical swine fever risk-based surveillance in Ecuador

Space-time cluster analysis to improve classical swine fever risk-based surveillance in Ecuador

Exploring the Spatiotemporal Dynamics of Cooperative Members' Switching Decisions

This article analyses the spatiotemporal dynamics of the actual switching behaviour of farmers’ in a dairy cooperative’s membership base. Space-time permutation scan statistic is used to identify clusters of switching decisions in space and time, while objective and publicly available indicators are related to the occurrence of these clusters. The analysis reveals two classes of clustered switching decisions: Clusters in which many farmers switch on a particular day and clusters covering longer periods of time with farmers switching in a herd-like pattern. Additionally, the relationship between farm sizes as well as price incentives and clustered switching decisions is observed. [EconLit citations: Q13; C23; L25].

Simulation of Legionnaires' disease prospective spatiotemporal cluster detection, Allegheny County, Pennsylvania, USA.

Legionnaires' disease (LD) incidence in the USA has quadrupled since 2000. Health departments must detect LD outbreaks quickly to identify and remediate sources. We tested the performance of a system to prospectively detect simulated LD outbreaks in Allegheny County, Pennsylvania, USA. We generated three simulated LD outbreaks based on published outbreaks. After verifying no significant clusters existed in surveillance data during 2014-2016, we embedded simulated outbreak-associated cases into 2016, assigning simulated residences and report dates. We mimicked daily analyses in 2016 using the prospective space-time permutation scan statistic to detect clusters of ⩽30 and ⩽180 days using 365-day and 730-day baseline periods, respectively. We used recurrence interval (RI) thresholds of ⩾20, ⩾100 and ⩾365 days to define significant signals. We calculated sensitivity, specificity and positive and negative predictive values for daily analyses, separately for each embedded outbreak. Two large, simulated cooling tower-associated outbreaks were detected. As the RI threshold was increased, sensitivity and negative predictive value decreased, while positive predictive value and specificity increased. A small, simulated potable water-associated outbreak was not detected. Use of a RI threshold of ⩾100 days minimised time-to-detection while maximizing positive predictive value. Health departments should consider using this system to detect community-acquired LD outbreaks.

Epidemiological characteristics and spatial-temporal clustering analysis on pulmonary tuberculosis in Changsha from 2013 to 2016

To analyze the epidemic characteristics and to explore the spatial-temporal clusters of pulmonary tuberculosis (PTB) in Changsha from 2013 to 2016. Methods: Descriptive analysis and space-time permutation scan statistic were used to analyze the reported PTB cases in Changsha from 2013 to 2016. Results: Between 2013 and 2016, a total of 17 721 PTB cases were reported in Changsha, with annual reported incidence rate at 60.87 per 100 000 population. Males and individuals aged 15 to <60 years accounted for higher proportion of PTB cases compared to females and other age groups. The number of reported PTB cases reached peak from March to May in each year. The space-time permutation scan statistic identified one most likely cluster and two secondary clusters of PTB cases. The most likely cluster covered most areas of Liuyang City and the North-east part of Changsha County from October 1, 2013 to February 28, 2014. The first cluster occupied 12 towns (streets) in Kaifu District and Changsha County in December 2016. The second cluster included four towns (streets) in Yuhua District and Tianxin District from March 1 to September 30, in 2013. Conclusion: Between 2013 and 2016, significant space-time clusters of PTB cases were identified in Changsha. These findings could provide a guide for development of regional intervention strategies for PTB control.

Using Scan Statistic to Detect Heroin Overdose Clusters with Hospital Emergency Room Visit Data

ObjectiveTo utilize syndromic surveillance data timely detecting herion overdose outbreaks in the community.IntroductionEarly detection of heroin overdose clusters is important in the current battle against the opioid crisis to effectively implement prevention and control measures. The New York State syndromic surveillance system collects hospital emergency department (ED) visit data, including visit time, chief complaint, and patient zip code. This data can be used to timely identify potential heroin overdose outbreaks by detecting spatial-temporal case clusters with scan statistic.MethodsHeroin overdose cases (Heroin_OD) were identified from ED visits by searching Heroin_OD key terms in the chief complaints. Then the space-time permutation model (using the SaTScan package) was applied to detect clusters of Heroin_OD. ED visit date served as the time variable and the case residential zip code was the spatial coordinate variable for the SaTScan analysis. A SAS program was developed to carry out the prospective scan statistics analysis weekly and produces reports of detected clusters in table and map format. Cluster detection parameters were set to detect heroin overdose aggregation in maximum geographic radium of 20 kilometer (km) and maximum time span up of 21 days at the P-value &lt;= 0.05. Chief complaints within the clusters are reviewed to ensure accuracy of detection. Messages have been developed and are shared with community members including law enforcement and public health identifying the cluster and offering suggestions of activities that can occur at the local level to identify and address the cause of the cluster, as well as to reduce potential harm. This includes the 23 syringe exchange programs (SEPs) regulated by the New York State Department of Health.ResultsUsing ED visit data from 138 NY upstate hospitals, a total of 12 Heroin_OD clusters were detected by the SaTScan analysis during the period of 9/1/2016 through 9/17/2017. There were 845 cases identified. The average age was 35 years and ranged from 7 to 95 years. Sixty nine percent (69%) of the cases was in 20 to 39 age group and 66% in males. A cluster was identified earlier 2017 in Suffolk County, and the local SEP was alerted. This encouraged communication between partners within the alerted county which ultimately resulted in identifying the substance endangering people who used drugs in the area. It also helped public health to partner with public safety, ensuring that the availability of the substance was interrupted.ConclusionsAs the space-time permutation scan statistic only requires disease counts, event date and disease location, the method can be easily implemented for detecting disease outbreaks using data routinely collected from disease surveillance systems. The current study showed that scan statistic is a useful tool for identifying clusters of non-fatal overdoses from specific drugs. This method also returns important information to assist outbreak investigations, such as geographic location and time-span of the potential outbreaks.

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

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.

Space-time clusters for early detection of grizzly bear predation.

Accurate detection and classification of predation events is important to determine predation and consumption rates by predators. However, obtaining this information for large predators is constrained by the speed at which carcasses disappear and the cost of field data collection. To accurately detect predation events, researchers have used GPS collar technology combined with targeted site visits. However, kill sites are often investigated well after the predation event due to limited data retrieval options on GPS collars (VHF or UHF downloading) and to ensure crew safety when working with large predators. This can lead to missing information from small‐prey (including young ungulates) kill sites due to scavenging and general site deterioration (e.g., vegetation growth). We used a space–time permutation scan statistic (STPSS) clustering method (SaTScan) to detect predation events of grizzly bears (Ursus arctos) fitted with satellite transmitting GPS collars. We used generalized linear mixed models to verify predation events and the size of carcasses using spatiotemporal characteristics as predictors. STPSS uses a probability model to compare expected cluster size (space and time) with the observed size. We applied this method retrospectively to data from 2006 to 2007 to compare our method to random GPS site selection. In 2013–2014, we applied our detection method to visit sites one week after their occupation. Both datasets were collected in the same study area. Our approach detected 23 of 27 predation sites verified by visiting 464 random grizzly bear locations in 2006–2007, 187 of which were within space–time clusters and 277 outside. Predation site detection increased by 2.75 times (54 predation events of 335 visited clusters) using 2013–2014 data. Our GLMMs showed that cluster size and duration predicted predation events and carcass size with high sensitivity (0.72 and 0.94, respectively). Coupling GPS satellite technology with clusters using a program based on space–time probability models allows for prompt visits to predation sites. This enables accurate identification of the carcass size and increases fieldwork efficiency in predation studies.