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Crime against women (CAW) is not a present-day problem but has been prevalent in the world through the ages and since the beginning of civilizations. The cases of CAW have been increasing in almost all parts of the world and India is no exception. The distribution of CAW cases has not been found uniform across the country. The evidence of heterogeneity of cases has been a concern. Rajasthan, the largest state in India, has witnessed a very high surge in CAW in recent years. Therefore, there arises a need to study and analyze the pattern of CAW to identify the areas with high intensity for prevention and control. The CAW data from the National Crime Records Bureau (NCRB) website for the period 2014 to 2021 and the population census data of 2011 are used for the analysis. The Statistical analysis software, SaTScan, is employed for hotspot (areas with a high concentration of crimes) detection. Python programming is used to compute the data's trend or pattern through visualization and descriptive statistics. In addition, the simple exponential smoothing method is applied for predicting the CAW for the year 2021. Our work elucidates Jhalawar, Baran, Kota, Bundi, Sawai Madhopur, and Chittorgarh districts as consistently occurring hotspots of CAW in the state. A comparative study of the hotspots found is made with the result obtained from the descriptive analysis. The trend in the data explains the years 2017 and 2019 as trough and crest of CAW cases. The hotspot detected using the forecast value of 2021 appears to be the same districts as for the period 2014 to 2020. Our work concludes that the consistency and the most likely cluster of CAW are distributed distinctly. We also found that the hotspot of CAW is not by chance but has certain man-made reasons. Most of the clusters have been identified as districts sharing boundaries with adjacent states. This further implies that if sincere efforts to collaborate with the government of the adjacent states like Madhya Pradesh, Haryana, Punjab, Uttar Pradesh, and Gujarat, the incidences resulting in detrimental effects due to CAW could reduce effectively and significantly. Thus, our study may help the government, law enforcement agencies, police organizations, judiciaries, and other stakeholders to optimize their scarce resources most effectively to curb such incidents. [ABSTRACT FROM AUTHOR]

Abstract Background Detection of pathogen-related clusters within a hospital is key to early intervention to prevent onward transmission. Various automated surveillance methods for outbreak detection have been implemented in hospital settings. However, direct comparison is difficult due to heterogenicity of data sources and methodologies. In the hospital setting, we assess the performance of three different methods for identifying microbiological clusters when applied to various pathogens with distinct occurrence patterns. Methods In this retrospective cohort study we use WHONET-SaTScan, CLAR (CLuster AleRt system) and our currently used percentile-based system (P75) for the means of cluster detection. The three methods are applied to the same data curated from 1st January 2014 to 31st December 2021 from a tertiary care hospital. We show the results for the following case studies: the introduction of a new pathogen with subsequent endemicity, an endemic species, rising levels of an endemic organism, and a sporadically occurring species. Results All three cluster detection methods showed congruence only in endemic organisms. However, there was a paucity of alerts from WHONET-SaTScan (n = 9) compared to CLAR (n = 319) and the P75 system (n = 472). WHONET-SaTScan did not pick up smaller variations in baseline numbers of endemic organisms as well as sporadic organisms as compared to CLAR and the P75 system. CLAR and the P75 system revealed congruence in alerts for both endemic and sporadic organisms. Conclusions Use of statistically based automated cluster alert systems (such as CLAR and WHONET-Satscan) are comparable to rule-based alert systems only for endemic pathogens. For sporadic pathogens WHONET-SaTScan returned fewer alerts compared to rule-based alert systems. Further work is required regarding clinical relevance, timelines of cluster alerts and implementation.

Aim s : Haemorrhagic fever with renal syndrome (HFRS) is a significant zoonotic disease transmitted by rodents. The distribution of HFRS in the European part of Russia has been studied quite well; however, much less is known about the endemic area in the Russian Far East. The mutual influence of the epidemic situation in the border regions and the possibility of cross‐border transmission of infection remain poorly understood. This study aims to identify the spatiotemporal hot spots of the incidence and the impact of environmental drivers on the HFRS distribution in the Russian Far East. Methods and Results: A two‐scale study design was performed. Kulldorf's spatial scan statistic was used to conduct spatiotemporal analysis at a regional scale from 2000 to 2020. In addition, an ecological niche model based on maximum entropy was applied to analyse the contribution of various factors and identify spatial favourability at the local scale. One spatiotemporal cluster that existed from 2002 to 2011 and located in the border area and one pure temporal cluster from 2004 to 2007 were revealed. The best suitability for orthohantavirus persistence was found along rivers, including those at the Chinese–Russian border, and was mainly explained by land cover, NDVI (as an indicator of vegetation density and greenness) and elevation. Conclusions: Despite the stable incidence in recent years in, targeted prevention strategies are still needed due to the high potential for HRFS distribution in the southeast of the Russian Far East. [ABSTRACT FROM AUTHOR]

Understanding the distribution of tegumentary leishmaniasis (TL) in different periods enables the adequate conduction of actions at the public health level. The present study analyzes the spatiotemporal evolution of TL incidence rates in the municipalities of Brazil and identifies priority areas from 2001 to 2020. Notifications of new cases were analyzed employing space-time scan statistics and Local Indicators of Spatial Association. As TL incidence rates presented a downward trend in most Brazilian municipalities, spatiotemporal clusters of high relative risks (RR) were more frequent in the first decade of the series. There was a concentration of those clusters in the North and Northeast regions, mainly in the Legal Amazon area. More recent high-RR areas were identified in municipalities of different regions. The number of priority municipalities showed a stable trend in Brazil. There was a great concentration of such municipalities in the states of Acre, Mato Grosso, Rondônia, Pará, and Amapá, as well as large areas in Roraima, Amazonas, Maranhão, and Tocantins, and smaller areas in the states of Goiás, Ceará, Bahia, Minas Gerais, São Paulo, and Paraná. The present study contributes to the understanding of the historical evolution of TL in Brazil and subsidizes actions to combat the disease. [ABSTRACT FROM AUTHOR]

Background: Detection of pathogen-related clusters within a hospital is key to early intervention to prevent onward transmission. Various automated surveillance methods for outbreak detection have been implemented in hospital settings. However, direct comparison is difficult due to heterogenicity of data sources and methodologies. In the hospital setting, we assess the performance of three different methods for identifying microbiological clusters when applied to various pathogens with distinct occurrence patterns. Methods: In this retrospective cohort study we use WHONET-SaTScan, CLAR (CLuster AleRt system) and our currently used percentile-based system (P75) for the means of cluster detection. The three methods are applied to the same data curated from 1st January 2014 to 31st December 2021 from a tertiary care hospital. We show the results for the following case studies: the introduction of a new pathogen with subsequent endemicity, an endemic species, rising levels of an endemic organism, and a sporadically occurring species. Results: All three cluster detection methods showed congruence only in endemic organisms. However, there was a paucity of alerts from WHONET-SaTScan (n = 9) compared to CLAR (n = 319) and the P75 system (n = 472). WHONET-SaTScan did not pick up smaller variations in baseline numbers of endemic organisms as well as sporadic organisms as compared to CLAR and the P75 system. CLAR and the P75 system revealed congruence in alerts for both endemic and sporadic organisms. Conclusions: Use of statistically based automated cluster alert systems (such as CLAR and WHONET-Satscan) are comparable to rule-based alert systems only for endemic pathogens. For sporadic pathogens WHONET-SaTScan returned fewer alerts compared to rule-based alert systems. Further work is required regarding clinical relevance, timelines of cluster alerts and implementation. [ABSTRACT FROM AUTHOR]

Objectives: In Canada, substance-related accidental acute toxicity deaths (AATDs) continue to rise at the national and sub-national levels. However, it is unknown if, where, when, and to what degree AATDs cluster in space, time, and space–time across the country. The objectives of this study were to 1) assess for clusters of AATDs that occurred in Canada during 2016 and 2017 at the national and provincial/territorial (P/T) levels, and 2) examine the substance types detected in AATD cases within each cluster. Methods: Two years of person-level data on AATDs were abstracted from coroner and medical examiner files using a standardized data collection tool, including the decedent's postal code and municipality information on the places of residence, acute toxicity (AT) event, and death, and the substances detected in the death. Data were combined with Canadian census information to create choropleth maps depicting AATD rates by census division. Spatial scan statistics were used to build Poisson models to identify clusters of high rates (p < 0.05) of AATDs at the national and P/T levels in space, time, and space–time over the study period. AATD cases within clusters were further examined for substance types most present in each cluster. Results: Eight clusters in five regions of Canada at the national level and 24 clusters in 15 regions at the P/T level were identified, highlighting where AATDs occurred at far higher rates than the rest of the country. The risk ratios of identified clusters ranged from 1.28 to 9.62. Substances detected in clusters varied by region and time, however, opioids, stimulants, and alcohol were typically the most commonly detected substances within clusters. Conclusion: Our findings are the first in Canada to reveal the geographic disparities in AATDs at national and P/T levels using spatial scan statistics. Rates associated with substance types within each cluster highlight which substance types were most detected in the identified regions. Findings may be used to guide intervention/program planning and provide a picture of the 2016 and 2017 context that can be used for comparisons of the geographic distribution of AATDs and substances with different time periods. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The south of the Russian Far East is distinguished by diversity of natural conditions for the presence of vectors and circulation of pathogens, primarily tick-borne infections. Despite the relatively low proportion of tick-borne encephalitis in the structure of tick-borne infections and the rather low incidence rate compared to other Russian regions, the disease here has epidemiological significance, which is associated with its severe course. Therefore, it is important to identify local areas of greatest epidemic manifestation of the disease and potential drivers influencing the spread of tick-borne encephalitis. This study uses data on population incidence in the municipal districts of Khabarovsk Krai, Amur Oblast, Jewish Autonomous Oblast and Zabaikalsky Krai between 2000 and 2020. Based on Kulldorf spatial scanning statistics, a temporally stable cluster of virus circulation in the population in the southwest of Zabaikalsky Krai was identified, which existed during 2009-2018. Regression modeling using zero-inflated negative binomial regression based on a set of environmental and socio-economic predictors allowed to identify variables determining the probability of infection: the share of forest, the amount of precipitation in the warm period, population density, as well as variables reflecting population employment and socio-economic well-being. Despite the fact that tick-borne encephalitis is a natural focal disease and may be characterized by natural periods of increased incidence, the influence of the social component can have a strong impact on the epidemiological manifestation. The identified spatio-temporal differences within the study region and potential drivers must be taken into account when developing a set of preventive measures.

Pulmonary Tuberculosis, hereinafter abbreviated as pulmonary TB, is an infectious disease caused by a group of acid-resistant bacteria, namely Mycobacterium tuberculosis. Pulmonary TB is also still a major health problem in Banjar District, South Kalimantan Province, Indonesia. This research is a descriptive study that aims to identify spatial and temporal clusters of pulmonary TB cases in Karang Intan District, Banjar Regency during 2020 – 2021. The statistical retrospective space-time scan with a Poisson probability model was performed to analyzed the data. The spatial units analyzed in this study included 26 villages in Karang Intan District, with a time unit of 2 years, namely 2020 – 2021. The results of the analysis show that there were 3 clusters of pulmonary TB detected and spread across 26 villages in Karang Intan District. There were 2 clusters in 2020, and 1 cluster in 2021. The transmission of pulmonary TB clusters during 2020-2021 was mostly found in areas with relatively medium or high population density. However, the three detected clusters show a P-value > 0,05 which indicates that the clusters are not statistically significant.

Many cities have promoted nightlife or entertainment districts – concentrations of restaurants, bars, and other entertainment-related businesses – in order to revitalize declining neighborhoods. While entertainment districts can boost economic growth, they can also contribute to public health risks including violent crime, traffic accidents, and other harms. With data from the National Establishment Time Series (NETS) business database, we developed methods to use SaTScan cluster detection software to identify entertainment districts, and applied the method in a case-study of Philadelphia, Pennsylvania. Using SaTScan, we identified and mapped 101 spatial clusters of entertainment businesses in the city. Our approach is scalable and does not require prior local knowledge about entertainment areas. The results add to a small but growing literature about the use of SaTScan to map neighborhood features. Placing entertainment districts in spatial context can inform how the built environment might amplify or minimize the potential health risks of these districts.

Introduction: Lumpy skin disease is a viral disease that affects cattle belonging to genus Capripoxvirus (Poxviridae) and lead to significant economic losses. Objective: The objective of this study was to evaluate the distribution of lumpy skin disease (LSD) outbreaks and predict future patterns based on retrospective outbreak reports in Ethiopia. Methods: Data were collected through direct communication with regional laboratories and a hierarchical reporting system from the Peasant Associations to Ministry of Agriculture. Time-series data for the LSD outbreaks were analyzed using classical additive time-series decomposition and STL decomposition. Four models (ARIMA, SARIMA, ETS, STLF) were also used to forecast the number of LSD outbreaks that occurred each month for the years (2021-2025) after the models' accuracy test was performed. Additionally, the space-time permutation model (STP) were also used to study retrospective space-time cluster analysis of LSD outbreaks in Ethiopia. Results: This study examined the geographical and temporal distribution of LSD outbreaks in Ethiopia from 2008 to 2020, reporting a total of 3,256 LSD outbreaks, 14,754 LSD-positive cases, 7,758 deaths, and 289 slaughters. It also covered approximately 68% of Ethiopia's districts, with Oromia reporting the highest LSD outbreaks. In the LSD's temporal distribution, the highest peak was reported following the rainy season in September to December and its lowest peak in the dry months of April and May. Out of the four models tested for forecasting, the SARIMA (3, 0, 0) (2, 1, 0) [12] model performed well for the validation data, while the STLF+Random Walk had a robust prediction for the training data. Thus, the SARIMA and STLF+Random Walk models produced a more accurate forecast of LSD outbreaks between 2020 and 2025. From retrospective Space-Time Cluster Analysis of LSD, eight possible clusters were also identified, with five of them located in central part of Ethiopia. Conclusion: The study's time series and ST-cluster analysis of LSD outbreak data provide valuable insights into the spatial and temporal dynamics of the disease in Ethiopia. These insights can aid in the development of effective strategies to control and prevent the spread of the disease and holds great potential for improving efforts to combat LSD in the country. [ABSTRACT FROM AUTHOR]

Abstract Background Cancer is a significant public health concern and the second leading cause of death. This study aims to visualize spatial patterns of top common cancer types and identify high-risk and low-risk counties for these cancers in Iran from 2014 to 2017. Methods In this study, we analyzed 482,229 newly diagnosed cancer cases recorded by the Iranian National Population-Based Cancer Registry from 2014 to 2017. We employed a purely spatial scanning model and local Moran I analysis to explore spatial patterns across Iran. Results Approximately 53% of all cases were male. The average age of cancer diagnosis was 62.58 ± 17.42 years for males and 56.11 ± 17.33years for females. Stomach cancer was the most common cancer in men. The northern and northwestern regions of Iran were identified as high-risk areas for stomach cancer in both genders, with a relative risk (RR) ranging from 1.26 to 2.64 in males and 1.19 to 3.32 in females. These areas recognized as high-risk areas for trachea, bronchus, and lung (TBL) cancer specifically in males (RR:1.15–2.02). Central regions of Iran were identified as high-risk areas for non-melanoma skin cancers in both genders, ranking as the second most common cancer (RR:1.18–5.93 in males and 1.24–5.38 in females). Furthermore, bladder cancer in males (RR:1.32–2.77) and thyroid cancer in females (RR:1.88–3.10) showed concentration in the central part of Iran. Breast cancer, being the most common cancer among women (RR:1.23–5.54), exhibited concentration in the northern regions of the country. Also, northern regions of Iran were identified as high-risk clusters for colon cancer (RR:1.31–3.31 in males and 1.33–4.13 in females), and prostate cancer in males (RR:1.22–2.31). Brain, nervous system cancer, ranked sixth among women (RR:1.26–5.25) in central areas. Conclusions The study's revelations on the spatial patterns of common cancer incidence in Iran provide crucial insights into the distribution and trends of these diseases. The identification of high-risk areas equips policymakers with valuable information to tailor targeted screening programs, facilitating early diagnosis and effective disease control strategies.

Abstract Background Leptospirosis, a zoonotic disease, stands as one of the prevailing health issues in some tropical areas of Iran. Over a decade, its incidence rate has been estimated at approximately 2.33 cases per 10,000 individuals. Our research focused on analyzing the spatiotemporal clustering of Leptospirosis and developing a disease prevalence model as an essential focal point for public health policymakers, urging targeted interventions and strategies. Methods The SaTScan and Maximum Entropy (MaxEnt) modeling methods were used to find the spatiotemporal clusters of the Leptospirosis and model the disease prevalence in Iran. We incorporated nine environmental covariates by employing a spatial resolution of 1 km x 1 km, the finest resolution ever implemented for modeling Human Leptospirosis in Iran. These covariates encompassed the Digital Elevation Model (DEM), slope, displacement areas, water bodies, and land cover, monthly recorded Normalized Difference Vegetation Index (NDVI), monthly recorded precipitation, monthly recorded mean and maximum temperature, contributing significantly to our disease modeling approach. The analysis using MaxEnt yielded the Area Under the Receiver Operating Characteristic Curve (AUC) metrics for the training and test data, to evaluate the accuracy of the implemented model. Results The findings reveal a highly significant primary cluster (p-value

Background: Cancer is a significant public health concern and the second leading cause of death. This study aims to visualize spatial patterns of top common cancer types and identify high-risk and low-risk counties for these cancers in Iran from 2014 to 2017. Methods: In this study, we analyzed 482,229 newly diagnosed cancer cases recorded by the Iranian National Population-Based Cancer Registry from 2014 to 2017. We employed a purely spatial scanning model and local Moran I analysis to explore spatial patterns across Iran. Results: Approximately 53% of all cases were male. The average age of cancer diagnosis was 62.58 ± 17.42 years for males and 56.11 ± 17.33years for females. Stomach cancer was the most common cancer in men. The northern and northwestern regions of Iran were identified as high-risk areas for stomach cancer in both genders, with a relative risk (RR) ranging from 1.26 to 2.64 in males and 1.19 to 3.32 in females. These areas recognized as high-risk areas for trachea, bronchus, and lung (TBL) cancer specifically in males (RR:1.15–2.02). Central regions of Iran were identified as high-risk areas for non-melanoma skin cancers in both genders, ranking as the second most common cancer (RR:1.18–5.93 in males and 1.24–5.38 in females). Furthermore, bladder cancer in males (RR:1.32–2.77) and thyroid cancer in females (RR:1.88–3.10) showed concentration in the central part of Iran. Breast cancer, being the most common cancer among women (RR:1.23–5.54), exhibited concentration in the northern regions of the country. Also, northern regions of Iran were identified as high-risk clusters for colon cancer (RR:1.31–3.31 in males and 1.33–4.13 in females), and prostate cancer in males (RR:1.22–2.31). Brain, nervous system cancer, ranked sixth among women (RR:1.26–5.25) in central areas. Conclusions: The study's revelations on the spatial patterns of common cancer incidence in Iran provide crucial insights into the distribution and trends of these diseases. The identification of high-risk areas equips policymakers with valuable information to tailor targeted screening programs, facilitating early diagnosis and effective disease control strategies. [ABSTRACT FROM AUTHOR]

Background: Leptospirosis, a zoonotic disease, stands as one of the prevailing health issues in some tropical areas of Iran. Over a decade, its incidence rate has been estimated at approximately 2.33 cases per 10,000 individuals. Our research focused on analyzing the spatiotemporal clustering of Leptospirosis and developing a disease prevalence model as an essential focal point for public health policymakers, urging targeted interventions and strategies. Methods: The SaTScan and Maximum Entropy (MaxEnt) modeling methods were used to find the spatiotemporal clusters of the Leptospirosis and model the disease prevalence in Iran. We incorporated nine environmental covariates by employing a spatial resolution of 1 km x 1 km, the finest resolution ever implemented for modeling Human Leptospirosis in Iran. These covariates encompassed the Digital Elevation Model (DEM), slope, displacement areas, water bodies, and land cover, monthly recorded Normalized Difference Vegetation Index (NDVI), monthly recorded precipitation, monthly recorded mean and maximum temperature, contributing significantly to our disease modeling approach. The analysis using MaxEnt yielded the Area Under the Receiver Operating Characteristic Curve (AUC) metrics for the training and test data, to evaluate the accuracy of the implemented model. Results: The findings reveal a highly significant primary cluster (p-value < 0.05) located in the western regions of the Gilan province, spanning from July 2013 to July 2015 (p-value < 0.05). Moreover, there were four more clusters (p-value < 0.05) identified near Someh Sara, Neka, Gorgan and Rudbar. Furthermore, the risk mapping effectively illustrates the potential expansion of the disease into the western and northwestern regions. The AUC metrics of 0.956 and 0.952 for the training and test data, respectively, underscoring the robust accuracy of the implemented model. Interestingly, among the variables considered, the influence of slope and distance from water bodies appears to be minimal. However, altitude and precipitation stand out as the primary determinants that significantly contribute to the prevalence of the disease. Conclusions: The risk map generated through this study carries significant potential to enhance public awareness and inform the formulation of impactful policies to combat Leptospirosis. These maps also play a crucial role in tracking disease incidents and strategically directing interventions toward the regions most susceptible. [ABSTRACT FROM AUTHOR]

Earthquakes are catastrophic natural disasters and along with their aftereffects, they have caused significant fatalities, injuries and economic losses throughout history, and have changed the landscape physically. There is a need to understand the distribution and associated damage patterns of earthquakes to be better prepared and to ensure mitigation of damage in the future. This study analyses the spatial and spatiotemporal trends of earthquake occurrence and associated fatality at a global scale over the 215-year period between 1800 and 2015. Spatial and spatiotemporal analyses revealed that certain countries in Asia including Türkiye, China, India, Pakistan and Indonesia suffered the most both in terms of fatality and earthquake occurrence. There were significant spatiotemporal clusters of earthquake occurrence over this time period on the southern half of Asia, Türkiye and southwest Europe and northern Africa. The findings of the study provide a spatial and spatiotemporal characterization of fatal earthquakes and improve our understanding of these patterns at the global scale. Spatial analyses covering longer time intervals at regional and global scales should be undertaken in future studies to provide a more comprehensive understanding of earthquake occurrence and associated damage patterns. [ABSTRACT FROM AUTHOR]

IntroductionLumpy skin disease is a viral disease that affects cattle belonging to genus Capripoxvirus (Poxviridae) and lead to significant economic losses.ObjectiveThe objective of this study was to evaluate the distribution of lumpy skin disease (LSD) outbreaks and predict future patterns based on retrospective outbreak reports in Ethiopia.MethodsData were collected through direct communication with regional laboratories and a hierarchical reporting system from the Peasant Associations to Ministry of Agriculture. Time-series data for the LSD outbreaks were analyzed using classical additive time-series decomposition and STL decomposition. Four models (ARIMA, SARIMA, ETS, STLF) were also used to forecast the number of LSD outbreaks that occurred each month for the years (2021–2025) after the models’ accuracy test was performed. Additionally, the space–time permutation model (STP) were also used to study retrospective space–time cluster analysis of LSD outbreaks in Ethiopia.ResultsThis study examined the geographical and temporal distribution of LSD outbreaks in Ethiopia from 2008 to 2020, reporting a total of 3,256 LSD outbreaks, 14,754 LSD-positive cases, 7,758 deaths, and 289 slaughters. It also covered approximately 68% of Ethiopia’s districts, with Oromia reporting the highest LSD outbreaks. In the LSD’s temporal distribution, the highest peak was reported following the rainy season in September to December and its lowest peak in the dry months of April and May. Out of the four models tested for forecasting, the SARIMA (3, 0, 0) (2, 1, 0) [12] model performed well for the validation data, while the STLF+Random Walk had a robust prediction for the training data. Thus, the SARIMA and STLF+Random Walk models produced a more accurate forecast of LSD outbreaks between 2020 and 2025. From retrospective Space–Time Cluster Analysis of LSD, eight possible clusters were also identified, with five of them located in central part of Ethiopia.ConclusionThe study’s time series and ST-cluster analysis of LSD outbreak data provide valuable insights into the spatial and temporal dynamics of the disease in Ethiopia. These insights can aid in the development of effective strategies to control and prevent the spread of the disease and holds great potential for improving efforts to combat LSD in the country.

Abstract Background Correctly identifying spatial disease cluster is a fundamental concern in public health and epidemiology. The spatial scan statistic is widely used for detecting spatial disease clusters in spatial epidemiology and disease surveillance. Many studies default to a maximum reported cluster size (MRCS) set at 50% of the total population when searching for spatial clusters. However, this default setting can sometimes report clusters larger than true clusters, which include less relevant regions. For the Poisson, Bernoulli, ordinal, normal, and exponential models, a Gini coefficient has been developed to optimize the MRCS. Yet, no measure is available for the multinomial model. Results We propose two versions of a spatial cluster information criterion (SCIC) for selecting the optimal MRCS value for the multinomial-based spatial scan statistic. Our simulation study suggests that SCIC improves the accuracy of reporting true clusters. Analysis of the Korea Community Health Survey (KCHS) data further demonstrates that our method identifies more meaningful small clusters compared to the default setting. Conclusions Our method focuses on improving the performance of the spatial scan statistic by optimizing the MRCS value when using the multinomial model. In public health and disease surveillance, the proposed method can be used to provide more accurate and meaningful spatial cluster detection for multinomial data, such as disease subtypes.

Lung cancer is the most common cause of cancer-related death in Michigan. Most patients are diagnosed at advanced stages of the disease. There is a need to detect clusters of lung cancer incidence over time, to generate new hypotheses about causation and identify high-risk areas for screening and treatment. The Michigan Cancer Surveillance database of individual lung cancer cases, 1985 to 2018 was used for this study. Spatial and spatiotemporal clusters of lung cancer and level of disease (localized, regional and distant) were detected using discrete Poisson spatial scan statistics at the zip code level over the study time period. The approach detected cancer clusters in cities such as Battle Creek, Sterling Heights and St. Clair County that occurred prior to year 2000 but not afterwards. In the northern area of the lower peninsula and the upper peninsula clusters of late-stage lung cancer emerged after year 2000. In Otter Lake Township and southwest Detroit, late-stage lung cancer clusters persisted. Public and patient education about lung cancer screening programs must remain a health priority in order to optimize lung cancer surveillance. Interventions should also involve programs such as telemedicine to reduce advanced stage disease in remote areas. In cities such as Detroit, residents often live near industry that emits air pollutants. Future research should therefore, continue to focus on the geography of lung cancer to uncover place-based risks and in response, the need for screening and health care services.

Purpose: Despite affecting up to 20% of women and being the leading cause of preventable deaths during the perinatal and postpartum period, maternal mental health conditions are chronically understudied. This study is the first to identify spatial patterns in perinatal mental health conditions, and relate these patterns to place-based social and environmental factors that drive cluster development. Methods: We performed spatial clustering analysis of emergency department (ED) visits for perinatal mood and anxiety disorders (PMAD), severe mental illness (SMI), and maternal mental disorders of pregnancy (MDP) using the Poisson model in SatScan from 2016 to 2019 in North Carolina. Logistic regression was used to examine the association between patient and community-level factors and high-risk clusters. Results: The most significant spatial clustering for all three outcomes was concentrated in smaller urban areas in the western, central piedmont, and coastal plains regions of the state, with odds ratios greater than 3 for some cluster locations. Individual factors (e.g., age, race, ethnicity) and contextual factors (e.g., racial and socioeconomic segregation, urbanity) were associated with high risk clusters. Conclusions: Results provide important contextual and spatial information concerning at-risk populations with a high burden of maternal mental health disorders and can better inform targeted locations for the expansion of maternal mental health services.

Background: Correctly identifying spatial disease cluster is a fundamental concern in public health and epidemiology. The spatial scan statistic is widely used for detecting spatial disease clusters in spatial epidemiology and disease surveillance. Many studies default to a maximum reported cluster size (MRCS) set at 50% of the total population when searching for spatial clusters. However, this default setting can sometimes report clusters larger than true clusters, which include less relevant regions. For the Poisson, Bernoulli, ordinal, normal, and exponential models, a Gini coefficient has been developed to optimize the MRCS. Yet, no measure is available for the multinomial model. Results: We propose two versions of a spatial cluster information criterion (SCIC) for selecting the optimal MRCS value for the multinomial-based spatial scan statistic. Our simulation study suggests that SCIC improves the accuracy of reporting true clusters. Analysis of the Korea Community Health Survey (KCHS) data further demonstrates that our method identifies more meaningful small clusters compared to the default setting. Conclusions: Our method focuses on improving the performance of the spatial scan statistic by optimizing the MRCS value when using the multinomial model. In public health and disease surveillance, the proposed method can be used to provide more accurate and meaningful spatial cluster detection for multinomial data, such as disease subtypes. [ABSTRACT FROM AUTHOR]

Simple Summary: In this study, we utilized the methods of spatiotemporal analysis to reveal and visualize the areas in the Russian Federation where an increased density of wild boar population might be related to the concentration of African swine fever (ASF) cases in wild boar. We demonstrated the areas (at the municipality level), where the elevated wild boar population density has continued to rise in recent years, that may be treated as high-risk areas, subject to the application of enhanced surveillance and population control measures. African swine fever (ASF) is an infectious disease that affects both domestic pigs (DPs) and wild boar (WB). The WB population plays an important role in the spread of ASF as the WB acts as a natural reservoir of the virus and transmits it to other susceptible wild and domestic pigs. Our study was aimed at revealing the areas with a high concentration of the WB population, and their potential relationships with the grouping of ASF cases in WB during the course of the ASF spread in the Russian Federation (2007–2022). We collected the annual data on WB numbers by municipalities within the regions of the most intensive ASF spread. We then conducted spatiotemporal analysis to identify clustering areas of ASF cases and compare them with the territories with a high density of WB population. We found that some of the territories with elevated ASF incidence in WB demonstrated spatial and temporal coincidence with the areas with a high WB population density. We also visualized the zones ("emerging hot spots") with a statistically significant rise in the WB population density in recent years, which may be treated as areas of paramount importance for the application of surveillance measures and WB population control. [ABSTRACT FROM AUTHOR]

Abstract Background Cutaneous leishmaniasis (CL) is a wide-reaching infection of major public health concern. Iran is one of the six most endemic countries in the world. This study aims to provide a spatiotemporal visualization of CL cases in Iran at the county level from 2011 to 2020, detecting high-risk zones, while also noting the movement of high-risk clusters. Methods On the basis of clinical observations and parasitological tests, data of 154,378 diagnosed patients were obtained from the Iran Ministry of Health and Medical Education. Utilizing spatial scan statistics, we investigated the disease’s purely temporal, purely spatial, spatial variation in temporal trends and spatiotemporal patterns. At P = 0.05 level, the null hypothesis was rejected in every instance. Results In general, the number of new CL cases decreased over the course of the 9-year research period. From 2011 to 2020, a regular seasonal pattern, with peaks in the fall and troughs in the spring, was found. The period of September–February of 2014–2015 was found to hold the highest risk in terms of CL incidence rate in the whole country [relative risk (RR) = 2.24, P

Anaemia is a common public-health problem affecting about two-thirds of pregnant women in developing countries. Spacetime cluster analysis of anemia cases is important for publichealth policymakers to design evidence-based intervention strategies. This study discovered the potential space-time clusters of anemia in pregnant women in Khyber Pakhtunkhwa Province, Pakistan, from 2014 to 2020 using space-time scan statistic (SatScan). The results show that the most likely cluster of anemia was seen in the rural areas in the eastern part of the province covering five districts from 2017 to 2019. However, three secondary clusters in the West and one in the North were still active, signifying important targets of interest for public-health interventions. The potential anemia clusters in the province’s rural areas might be associated with the lack of nutritional education in women and lack of access to sufficient diet due to financial constraints.

BackgroundThe observance of nonrandom space-time groupings of childhood cancer has been a concern of health professionals and the general public for decades. Many childhood cancers are suspected to have initiated in utero; therefore, we examined the spatial-temporal randomness of the birthplace of children who later developed cancer.MethodsWe performed a space-time cluster analysis using birth addresses of 5,896 cases and 23,369 population-based, age-, sex-, and race/ethnicity-matched controls in California from 1997 to 2007, evaluating 20 types of childhood cancer and three a priori designated subgroups of childhood acute lymphoblastic leukemia (ALL). We analyzed data using a newly designed semiparametric analysis program, ClustR, and a common algorithm, SaTScan.ResultsWe observed evidence for nonrandom space-time clustering for ALL diagnosed at 2-6 years of age in the South San Francisco Bay Area (ClustR P = 0.04, SaTScan P = 0.07), and malignant gonadal germ cell tumors in a region of Los Angeles (ClustR P = 0.03, SaTScan P = 0.06). ClustR did not identify evidence of clustering for other childhood cancers, although SaTScan suggested some clustering for Hodgkin lymphoma (P = 0.09), astrocytoma (P = 0.06), and retinoblastoma (P = 0.06).ConclusionsOur study provides evidence that childhood ALL diagnosed at 2-6 years and malignant gonadal germ cell tumors sporadically occurs in nonrandom space-time clusters. Further research is warranted to identify epidemiologic features that may inform the underlying etiology.

Abstract Background Drug Use Disorder (DUD) is a major contributor to world-wide morbidity and mortality. The extensive national registers in Sweden provide the basis for a study of spatial and temporal patterns of DUD onset and recurrence in Sweden from 2001–2015. Methods To identify patterns of DUD over space, time and gender for Swedish individuals aged 15–35, space–time clustering using SaTScan was applied. We used yearly information on residential locations in Demographic Statistical Areas (DeSO) for all of Sweden. The clustering analysis used a Poisson probability model and a null hypothesis that the expected number of cases in each DeSO was proportional to the population size of DeSOs. As SaTScan results can be unstable, steps were taken to determine stable clusters and to refine and optimize cluster size. Results for each gender-register combination were compared to the results of spatial clustering using Gi* statistics. The space–time scanning model was also run with an adjustment for neighborhood socioeconomic status to determine DUD prevalence as it relates to education, income, unemployment and receipt of social welfare. Results DUD prevalence increased over time. Males yielded more significant clusters than females for both criminal and medical registers. Female DUD prevalence rates increased over time, especially after 2012. Higher correlations in DUD rates existed across the two registers than across gender. Male clusters were present from 2004 onwards while female–criminal clusters appeared after 2007, and female–medical clusters not until 2010. By 2013, clusters existed for all gender–register combinations. Male–criminal clusters were concentrated in Stockholm, Göteborg and Malmö as were male and female-medical clusters. Neighborhood SES was more highly related to the distribution of criminal than medical DUD clusters. A persistent gap in core clusters was identified in Stockholm in an area with notably high SES. Conclusions Persistent hotspots of DUD in Sweden were confirmed as well as new and emerging hotspots, especially in Stockholm, Göteborg and Malmö. Higher correlations existed in DUD rates across registers than across gender. The findings are useful for monitoring the current drug problem and for identifying drivers underlying patterns of spread and important causal pathways to DUD.

Background: Cutaneous leishmaniasis (CL) is a wide-reaching infection of major public health concern. Iran is one of the six most endemic countries in the world. This study aims to provide a spatiotemporal visualization of CL cases in Iran at the county level from 2011 to 2020, detecting high-risk zones, while also noting the movement of high-risk clusters. Methods: On the basis of clinical observations and parasitological tests, data of 154,378 diagnosed patients were obtained from the Iran Ministry of Health and Medical Education. Utilizing spatial scan statistics, we investigated the disease's purely temporal, purely spatial, spatial variation in temporal trends and spatiotemporal patterns. At P = 0.05 level, the null hypothesis was rejected in every instance. Results: In general, the number of new CL cases decreased over the course of the 9-year research period. From 2011 to 2020, a regular seasonal pattern, with peaks in the fall and troughs in the spring, was found. The period of September–February of 2014–2015 was found to hold the highest risk in terms of CL incidence rate in the whole country [relative risk (RR) = 2.24, P < 0.001)]. In terms of location, six significant high-risk CL clusters covering 40.6% of the total area of the country were observed, with the RR ranging from 1.87 to 9.69. In addition, spatial variation in the temporal trend analysis found 11 clusters as potential high-risk areas that highlighted certain regions with an increasing tendency. Finally, five space-time clusters were found. The geographical displacement and spread of the disease followed a moving pattern over the 9-year study period affecting many regions of the country. Conclusions: Our study has revealed significant regional, temporal, and spatiotemporal patterns of CL distribution in Iran. Over the years, there have been multiple shifts in spatiotemporal clusters, encompassing many different parts of the country from 2011 to 2020. The results reveal the formation of clusters across counties that cover certain parts of provinces, indicating the importance of conducting spatiotemporal analyses at the county level for studies that encompass entire countries. Such analyses, at a finer geographical scale, such as county level, might provide more precise results than analyses at the scale of the province. [ABSTRACT FROM AUTHOR]

Abstract Background Up to April 2022, there were six waves of infection of coronavirus disease 2019 (COVID-19) in Japan. As the outbreaks continue to grow, it is critical to detect COVID-19’s clusters to allocate health resources and improve decision-making substantially. This study aimed to identify active clusters of COVID-19 in Nagasaki Prefecture and form the spatiotemporal pattern of high-risk areas in different infection periods. Methods We used the prospective space-time scan statistic to detect emerging COVID-19 clusters and examine the relative risk in five consecutive periods from April 1, 2020 to April 7, 2022, in Nagasaki Prefecture. Results The densely inhabited districts (DIDs) in Nagasaki City have remained the most affected areas since December 2020. Most of the confirmed cases in the early period of each wave had a history of travelling to other prefectures. Community-level transmissions are suggested by the quick expansion of spatial clusters from urban areas to rural areas and remote islands. Moreover, outbreaks in welfare facilities and schools may lead to an emerging cluster in Nagasaki Prefecture’s rural areas. Conclusions This study gives an overall analysis of the transmission dynamics of the COVID-19 pandemic in Nagasaki Prefecture, based on the number of machi-level daily cases. Furthermore, the findings in different waves can serve as references for subsequent pandemic prevention and control. This method helps the health authorities track and investigate outbreaks of COVID-19 that are specific to these environments, especially in rural areas where healthcare resources are scarce.

Introduction: The identification of climate temperature-sensitive pathogens and infectious diseases is essential in addressing health risks resulting from global warming. Such research is especially crucial in regions where climate change may have a more significant impact like Russia. Recent studies have reasoned that the abundance of V. cholerae is environmentally driven. The aim of the study is to investigate the spatial-temporal trends and thermo-climatic sensitivity of non-toxigenic V. cholerae abundance in Russia. Methods: This study employed spatial epidemiology tools to identify persistent clusters of the V. cholerae ctx- isolation and areas for exploring temperature-depended patterns of the vibrio distribution. Correlation analysis was used to identify regions with temperature-driven Vibrio abundance in water samples. Results: The spatial analysis detected 16 persistent (7-8 year) clusters of V. cholerae ctx- across the study period 2005-2021. The persistent clusters should become targeted areas to improve sanitation conditions. A distinct significant thermo-climatic effect on the abundance of V. cholerae ctx- in water basins was found in three Russian regions with temperate marine (the Kaliningrad region) and sharp continental climatic conditions (the Irkutsk region and the Republic of Sakha). Conclusion: The study offers valuable outcomes to support simplified empirical evaluations of the potential hazards of vibrio abundance that might be useful locally for public health authorities and globally as a part of Russia's warning system of climate change effects.

BackgroundAlthough the burden of the coronavirus disease 2019 (COVID-19) has been different across communities in the US, little is known about the disparities in COVID-19 burden in North Dakota (ND) and yet this information is important for guiding planning and provision of health services. Therefore, the objective of this study was to identify geographic disparities of COVID-19 hospitalization risks in ND.MethodsData on COVID-19 hospitalizations from March 2020 to September 2021 were obtained from the ND Department of Health. Monthly hospitalization risks were computed and temporal changes in hospitalization risks were assessed graphically. County-level age-adjusted and spatial empirical Bayes (SEB) smoothed hospitalization risks were computed. Geographic distributions of both unsmoothed and smoothed hospitalization risks were visualized using choropleth maps. Clusters of counties with high hospitalization risks were identified using Kulldorff's circular and Tango's flexible spatial scan statistics and displayed on maps.ResultsThere was a total of 4,938 COVID-19 hospitalizations during the study period. Overall, hospitalization risks were relatively stable from January to July and spiked in the fall. The highest COVID-19 hospitalization risk was observed in November 2020 (153 hospitalizations per 100,000 persons) while the lowest was in March 2020 (4 hospitalizations per 100,000 persons). Counties in the western and central parts of the state tended to have consistently high age-adjusted hospitalization risks, while low age-adjusted hospitalization risks were observed in the east. Significant high hospitalization risk clusters were identified in the north-west and south-central parts of the state.ConclusionsThe findings confirm that geographic disparities in COVID-19 hospitalization risks exist in ND. Specific attention is required to address counties with high hospitalization risks, especially those located in the north-west and south-central parts of ND. Future studies will investigate determinants of the identified disparities in hospitalization risks.

Background: Drug Use Disorder (DUD) is a major contributor to world-wide morbidity and mortality. The extensive national registers in Sweden provide the basis for a study of spatial and temporal patterns of DUD onset and recurrence in Sweden from 2001–2015. Methods: To identify patterns of DUD over space, time and gender for Swedish individuals aged 15–35, space–time clustering using SaTScan was applied. We used yearly information on residential locations in Demographic Statistical Areas (DeSO) for all of Sweden. The clustering analysis used a Poisson probability model and a null hypothesis that the expected number of cases in each DeSO was proportional to the population size of DeSOs. As SaTScan results can be unstable, steps were taken to determine stable clusters and to refine and optimize cluster size. Results for each gender-register combination were compared to the results of spatial clustering using Gi* statistics. The space–time scanning model was also run with an adjustment for neighborhood socioeconomic status to determine DUD prevalence as it relates to education, income, unemployment and receipt of social welfare. Results: DUD prevalence increased over time. Males yielded more significant clusters than females for both criminal and medical registers. Female DUD prevalence rates increased over time, especially after 2012. Higher correlations in DUD rates existed across the two registers than across gender. Male clusters were present from 2004 onwards while female–criminal clusters appeared after 2007, and female–medical clusters not until 2010. By 2013, clusters existed for all gender–register combinations. Male–criminal clusters were concentrated in Stockholm, Göteborg and Malmö as were male and female-medical clusters. Neighborhood SES was more highly related to the distribution of criminal than medical DUD clusters. A persistent gap in core clusters was identified in Stockholm in an area with notably high SES. Conclusions: Persistent hotspots of DUD in Sweden were confirmed as well as new and emerging hotspots, especially in Stockholm, Göteborg and Malmö. Higher correlations existed in DUD rates across registers than across gender. The findings are useful for monitoring the current drug problem and for identifying drivers underlying patterns of spread and important causal pathways to DUD. [ABSTRACT FROM AUTHOR]

Aim: This study aims to detect the spatial cluster, the temporal cluster, as well as the spatio-temporal cluster of high dengue disease incidence in Peninsular Malaysia for the year 2015 until 2017. As dengue disease cases are properly recorded according to their location and time, cluster analysis of the dengue disease based on its spatial, temporal, as well as spatio-temporal similarity would be more efficient. Subject and Methods: Clustering is one of the essential components in disease mapping that could be helpful in controlling diseases, especially those related to infectious disease. It is used to group large sets of disease data based on their similarity. The clustering method that has been used in this study is Kulldorff's retrospective space-time scan statistics. The data used in this study are weekly dengue disease cases in districts of Peninsular Malaysia from 2015 to 2017. Results and Conclusions: From the results, there were significant spatial cluster, temporal cluster, and spatio-temporal clusters of dengue disease incidence in Peninsular Malaysia for 2015, 2016, and 2017. Results from spatial clustering focused on areas in Selangor as the dengue clustered area and results from temporal clustering revealed the weeks in October until December as the dengue clustered period. Meanwhile, the spatio-temporal clustering demonstrated that there exists another clustered region of high dengue disease incidence in different areas and time periods. [ABSTRACT FROM AUTHOR]

This follow-up study investigated the spatio-temporal clustering of adolescent bereavement during the extended response to COVID-19 from October 2020–January 2022 in the continental United States. Deidentified and anonymized bereavement data from Crisis Text Line (CTL), a text-based crisis intervention service, and SaTScan cluster analysis were used to identify space-time clustering of bereavement among adolescents, aged 24 years and less, during the COVID-19 pandemic. Clustering of bereavement conversations occurred during waves of high COVID-19 case and death counts, with the highest risk occurring in the Southeastern United States during the fall of 2020 (relative risk: 5.86, confidence interval: 3.48–8.24). Of the CTL texters who shared their demographic information, Indigenous American, Black, male, and female adolescents were more likely to seek help for bereavement when compared to the other CTL users. Findings show an increased need for bereavement counseling resources during periods of high COVID-19 cases and deaths. [ABSTRACT FROM AUTHOR]

Brazil experienced one of the fastest increasing numbers of coronavirus disease (COVID-19) cases worldwide. The Sao Paulo State (SPS) reported a high incidence, particularly in Sao Paulo municipality. This study aimed to identify clusters of incidence and mortality of hospitalized patients with severe acute respiratory syndrome for COVID-19 in the SPS, in 2020-2021, and describe the origin flow pattern of the cases. Cases and mortality risk area clusters were identified through different analyses (spatial clusters, spatio-temporal clusters, and spatial variation in temporal trends) by weighting areas. Ripley's K12-function verified the spatial dependence between the cases and infrastructure. There were 517,935 reported cases, with 152,128 cases resulting in death. Of the 470,441 patients hospitalized and residing in the SPS, 357,526 remained in the original municipality, while 112,915 did not. Cases and death clusters were identified in the Sao Paulo metropolitan region (SPMR) and Baixada Santista region in the first study period, and in the SPMR and the Campinas, Sao Jose do Rio Preto, Barretos, and Sorocaba municipalities during the second period. We highlight the priority areas for control and surveillance actions for COVID-19, which could lead to better outcomes in future outbreaks. [ABSTRACT FROM AUTHOR]

The great disparities in resource availability, power, wealth, social institution and opportunities have led to rise in crime incidents especially in urban agglomerations. Therefore, addressing violence and mapping of crime hotspots is important in order to build a socially sustainable community. Current work aims to apply Space–Time Permutation Model (STPM) inbuilt in SatScan (an open source statistical tool), for crime hotspot detection in Pune city, Maharashtra, India, and compare the result with two GIS-based statistical methods namely, Kernel Density Estimation (KDE), and Getis-Ord Gi*, being utilized extensively for crime hotspot detection and mapping. The datasets of four different crime namely robbery, molestation, rape and dacoity for a period from 2012 to 2015 were used. Twenty six significant crime clusters (p < 0.1, log likelihood ratio, LLR > 1.3) obtained through SatScan show similar pattern, as obtained from KDE (92% matching) and Getis-Ord Gi* (92% matching). A total of 11 clusters were obtained as most significant clusters (with p < 0.000, LLR > 2.72, predictive accuracy index, PAI > 57.11). Locations situated in the central part of the city such as Swargate, Bibwewadi, Deccan, Bund Garden, and Farashkhana show relatively more crime clusters possibly due to inter-state road connectivity, more liquor shops, famous tourist places, large no. of auto rickshaws and tongawals, and a variety of immigrants in the search of livelihood. [ABSTRACT FROM AUTHOR]

African swine fever (ASF) is an infectious disease that affects both domestic pigs (DPs) and wild boar (WB). The WB population plays an important role in the spread of ASF as the WB acts as a natural reservoir of the virus and transmits it to other susceptible wild and domestic pigs. Our study was aimed at revealing the areas with a high concentration of the WB population, and their potential relationships with the grouping of ASF cases in WB during the course of the ASF spread in the Russian Federation (2007–2022). We collected the annual data on WB numbers by municipalities within the regions of the most intensive ASF spread. We then conducted spatiotemporal analysis to identify clustering areas of ASF cases and compare them with the territories with a high density of WB population. We found that some of the territories with elevated ASF incidence in WB demonstrated spatial and temporal coincidence with the areas with a high WB population density. We also visualized the zones (“emerging hot spots”) with a statistically significant rise in the WB population density in recent years, which may be treated as areas of paramount importance for the application of surveillance measures and WB population control.

Background: During the peak of the winter 2020-2021 surge, the number of weekly reported COVID-19 outbreaks in Washington State was 231; the majority occurred in high-priority settings such as workplaces, community settings, and schools. The Washington State Department of Health used automated address matching to identify clusters at health care facilities. No other systematic, statewide outbreak detection methods were in place. This was a gap given the high volume of cases, which delayed investigations and decreased data completeness, potentially leading to undetected outbreaks. We initiated statewide cluster detection using SaTScan, implementing a space-time permutation model to identify COVID-19 clusters for investigation., Objective: To improve outbreak detection, the Washington State Department of Health initiated a systematic cluster detection model to identify timely and actionable COVID-19 clusters for local health jurisdiction (LHJ) investigation and resource prioritization. This report details the model's implementation and the assessment of the tool's effectiveness., Methods: In total, 6 LHJs participated in a pilot to test model parameters including analysis type, geographic aggregation, cluster radius, and data lag. Parameters were determined through heuristic criteria to detect clusters early when they are smaller, making interventions more feasible. This study reviews all clusters detected after statewide implementation from July 17 to December 17, 2021. The clusters were analyzed by LHJ population and disease incidence. Clusters were compared with reported outbreaks., Results: A weekly, LHJ-specific retrospective space-time permutation model identified 2874 new clusters during this period. While the weekly analysis included case data from the prior 3 weeks, 58.25% (n=1674) of all clusters identified were timely-having occurred within 1 week of the analysis and early enough for intervention to prevent further transmission. There were 2874 reported outbreaks during this same period. Of those, 363 (12.63%) matched to at least one SaTScan cluster. The most frequent settings among reported and matched outbreaks were schools and youth programs (n=825, 28.71% and n=108, 29.8%), workplaces (n=617, 21.46% and n=56, 15%), and long-term care facilities (n=541, 18.82% and n=99, 27.3%). Settings with the highest percentage of clusters that matched outbreaks were community settings (16/72, 22%) and congregate housing (44/212, 20.8%). The model identified approximately one-third (119/363, 32.8%) of matched outbreaks before cases were associated with the outbreak event in our surveillance system., Conclusions: Our goal was to routinely and systematically identify timely and actionable COVID-19 clusters statewide. Regardless of population or incidence, the model identified reasonably sized, timely clusters statewide, meeting the objective. Among some high-priority settings subject to public health interventions throughout the pandemic, such as schools and community settings, the model identified clusters that were matched to reported outbreaks. In workplaces, another high-priority setting, results suggest the model might be able to identify outbreaks sooner than existing outbreak detection methods., (©Rachel Martonik, Caitlin Oleson, Ellyn Marder. Originally published in JMIR Public Health and Surveillance (https://publichealth.jmir.org), 16.10.2024.)

This study provides an overview of the current status of clonorchiasis and cholangiocarcinoma (CCA), and their relationship in Korea during 2012-2020. Data were obtained from the Health Insurance Review & Assessment Service of Korea. Cluster, trend, and correlation analyses were performed. Gyeongsangnam-do and Seoul had the highest average number of cases (1,026 and 4,208) and adjusted rate (306 and 424) for clonorchiasis and CCA, respectively. The most likely clusters (MLC) for clonorchiasis and CCA were Busan/Gyeongsangnam-do/Ulsan/Daegu/Gyeongsangbuk-do (Relative Risk; RR=4.55, Likelihood Ratio; LLR=9,131.115) joint cluster and Seoul (RR=2.29, LLR=7,602.472), respectively. The MLC for clonorchiasis was in the southeastern part of Korea, while that for CCA was in the southern part. Clonorchiasis showed a decreasing trend in the southeastern districts, while increased in the southwestern districts. Cities in the central region had a decreasing trend, while the western districts had an increasing trend. In most adults (30-59), infection rate of clonorchiasis showed a significant decrease until 2018, while thereafter increased, although not significant. CCA showed a sharply decreasing tendency. The incidence of clonorchiasis and CCA were positively correlated. In general, the correlation was weak (r=0.39, P<0.001), but it was strongly positive around the 4 river basins (r=0.74, P<0.001). This study might provide an analytic basis for developing an effective system against clonorchiasis and CCA. [ABSTRACT FROM AUTHOR]

Background: Up to April 2022, there were six waves of infection of coronavirus disease 2019 (COVID-19) in Japan. As the outbreaks continue to grow, it is critical to detect COVID-19's clusters to allocate health resources and improve decision-making substantially. This study aimed to identify active clusters of COVID-19 in Nagasaki Prefecture and form the spatiotemporal pattern of high-risk areas in different infection periods.Methods: We used the prospective space-time scan statistic to detect emerging COVID-19 clusters and examine the relative risk in five consecutive periods from April 1, 2020 to April 7, 2022, in Nagasaki Prefecture.Results: The densely inhabited districts (DIDs) in Nagasaki City have remained the most affected areas since December 2020. Most of the confirmed cases in the early period of each wave had a history of travelling to other prefectures. Community-level transmissions are suggested by the quick expansion of spatial clusters from urban areas to rural areas and remote islands. Moreover, outbreaks in welfare facilities and schools may lead to an emerging cluster in Nagasaki Prefecture's rural areas.Conclusions: This study gives an overall analysis of the transmission dynamics of the COVID-19 pandemic in Nagasaki Prefecture, based on the number of machi-level daily cases. Furthermore, the findings in different waves can serve as references for subsequent pandemic prevention and control. This method helps the health authorities track and investigate outbreaks of COVID-19 that are specific to these environments, especially in rural areas where healthcare resources are scarce. [ABSTRACT FROM AUTHOR]

Brown bear‐mediated conflicts have caused immense economic loss to the local people living across the distribution range. In India, limited knowledge is available on the Himalayan brown bear (HBB), making human–brown bear conflict (HBC) mitigation more challenging. In this study, we studied HBC in the Lahaul valley using a semi‐structured questionnaire survey by interviewing 398 respondents from 37 villages. About 64.8% of respondents reported conflict in two major groups—crop damage (30.6%) and livestock depredations (6.2%), while 28% reported both. Conflict incidences were relatively high in summer and frequently occurred in areas closer to the forest (<500 m) and between the elevations range of 2700 m to 3000 m above sea level (asl). The dependency of locals on forest resources (70%) for their livelihood makes them vulnerable to HBC. The "upper lower" class respondents were most impacted among the various socioeconomic classes. Two of the four clusters were identified as HBC hot spots in Lahaul valley using SaTscan analysis. We also obtained high HBC in cluster II with a 14.35 km radius. We found that anthropogenic food provisioning for HBB, livestock grazing in bear habitats, and poor knowledge of animal behavior among the communities were the major causes of HBC. We suggest horticulture crop waste management, controlled and supervised grazing, ecotourism, the constitution of community watch groups, and others to mitigate HBC. We also recommend notifying a few HBB abundant sites in the valley as protected areas for the long‐term viability of the HBB in the landscape. [ABSTRACT FROM AUTHOR]

Endemic Q fever in small ruminants remains an ongoing challenge for veterinary and human public health agencies. Though surveillance programs are implemented in Belgium, infection patterns and vaccination profiles, driving variables, as well as geographical clustering were not presented until now. Based on data from a decade of bulk tank milk analysis between 2009 and 2019, shedding in dairy goat herds declined from 16% (8/50) to 6% (10/162), whereas seroprevalence remained between 32% and 40%. Merely up to two shedding dairy sheep flocks were detected until 2019; seroprevalence peaked in 2017 (43%, 12/28) and declined thereafter. The number of animals in the holding influenced significantly (p =.048) the likelihood of shedding, whereas other established risk factors such as uncovered manure, high abortion rates and diversified farm structure could not be confirmed to significantly affect infection on Belgian herd level. Intermittent, incomplete and unsynchronized vaccinated herds shed Coxiella burnetii significantly more often and longer (p <.001) than continuously, complete and synchronized vaccinated herds. Spatial analyses revealed restricted but matching, homogenous clusters with ≤35 km diameter, concentrated in the coastal region close to the border to the Netherlands from 2009 to 2012, and broadened, heterogeneous clusters with ≥45 km diameter between 2014 and 2016 spreading south‐west. Though the majority of human cases was notified in this region, the animal clusters could not be allied with Q fever cases. The impact of environmental factors as well as the role of wildlife, rodents and ticks on the transmission between flocks and to humans remains to be elucidated to harness additional epidemiological drivers of Q fever in Belgium. In conclusion, attempts to reduce the burden of Q fever in Belgium should particularly focus on the timely, complete and synchronized vaccination of flocks, including the breeding sire, and particularity in high‐risk areas. [ABSTRACT FROM AUTHOR]

Abstract Background The spatial scan statistic is a useful tool for cluster detection analysis in geographical disease surveillance. The method requires users to specify the maximum scanning window size or the maximum reported cluster size (MRCS), which is often set to 50% of the total population. It is important to optimize the maximum reported cluster size, keeping the maximum scanning window size at as large as 50% of the total population, to obtain valid and meaningful results. Results We developed a measure, a Gini coefficient, to optimize the maximum reported cluster size for the exponential-based spatial scan statistic. The simulation study showed that the proposed method mostly selected the optimal MRCS, similar to the true cluster size. The detection accuracy was higher for the best chosen MRCS than at the default setting. The application of the method to the Korea Community Health Survey data supported that the proposed method can optimize the MRCS in spatial cluster detection analysis for survival data. Conclusions Using the Gini coefficient in the exponential-based spatial scan statistic can be very helpful for reporting more refined and informative clusters for survival data.

Abstract Background Effective reduction of tuberculosis (TB) requires information on the distribution of TB incidence rate across time and location. This study aims to identify the spatio-temporal pattern of TB incidence rate in Iran between 2008 and 2018. Methods This cross-sectional study was conducted on aggregated TB data (50,500 patients) at the provincial level provided by the Ministry of Health in Iran between 2008 and 2018. The Anselin Local Moran’s I and Getis-Ord Gi* were performed to identify the spatial variations of the disease. Furthermore, spatial scan statistic was employed for purely temporal and spatio-temporal analyses. In all instances, the null hypothesis of no clusters was rejected at p ≤ 0.05. Results The overall incidence rate of TB decreased from 13.46 per 100,000 (95% CI: 13.19–13.73) in 2008 to 10.88 per 100,000 (95% CI: 10.65–11.11) in 2018. The highest incidence rate of TB was observed in southeast and northeast of Iran for the whole study period. Additionally, spatial cluster analysis discovered Khuzestan Province, in the West of the country, having significantly higher rates than neighbouring provinces in terms of both total TB and smear-positive pulmonary TB (SPPTB). Purely temporal analysis showed that high-rate and low-rate clusters were predominantly distributed in the time periods 2010–2014 and 2017–2018. Spatio-temporal results showed that the statistically significant clusters were mainly distributed from centre to the east during the study period. Some high-trend TB and SPPTB statistically significant clusters were found. Conclusion The results provided an overview of the latest TB spatio-temporal status In Iran and identified decreasing trends of TB in the 2008–2018 period. Despite the decreasing incidence rate, there is still need for screening, and targeting of preventive interventions, especially in high-risk areas. Knowledge of the spatio-temporal pattern of TB can be useful for policy development as the information regarding the high-risk areas would contribute to the selection of areas needed to be targeted for the expansion of health facilities.

Abstract Brown bear‐mediated conflicts have caused immense economic loss to the local people living across the distribution range. In India, limited knowledge is available on the Himalayan brown bear (HBB), making human–brown bear conflict (HBC) mitigation more challenging. In this study, we studied HBC in the Lahaul valley using a semi‐structured questionnaire survey by interviewing 398 respondents from 37 villages. About 64.8% of respondents reported conflict in two major groups—crop damage (30.6%) and livestock depredations (6.2%), while 28% reported both. Conflict incidences were relatively high in summer and frequently occurred in areas closer to the forest (

While COVID-19 is a global pandemic, different countries have experienced different morbidity and mortality patterns. We employ retrospective and prospective space–time permutation analysis on COVID-19 positive records across different municipalities in Sweden from March 2020 to February 2021, using data provided by the Swedish Public Health Agency. To the best of our knowledge, this is the first study analyzing nationwide COVID-19 space–time clustering in Sweden, on a season-to-season basis. Our results show that different municipalities within Sweden experienced varying extents of season-dependent COVID-19 clustering in both the spatial and temporal dimensions. The reasons for the observed differences could be related to the differences in the earlier exposures to the virus, the strictness of the social restrictions, testing capabilities and preparedness. By profiling COVID-19 space–time clusters before the introduction of vaccines, this study contributes to public health efforts aimed at containing the virus by providing plausible evidence in evaluating which epidemiologic interventions in the different regions could have worked and what could have not worked. [ABSTRACT FROM AUTHOR]

Introduction: Globally, India tops by contributing the maximum number of diarrhoeal fatality. Forecasting the path and spread of diarrhoeal disease is critical due to its multifactorial cause, which needs robust spatial analysis and experiential investigations of communicable disease. Aim: To investigate purely spatial, purely temporal, and spacetime clusters of diarrhoea among under-five children using a Geographic Information System (GIS) in Karkala taluk of the Udupi district of Karnataka, India. Materials and Methods: A retrospective longitudinal study was conducted involving all the primary health centres of Karkala taluk by the investigators, from the Department of Public Health, K S Hegde Medical Academy, Nitte (DU), Mangalore, Karnataka, India. The data on diarrhoea among under-five children was collected for three years, i.e., from 1st April 2015 to 31st March 2018, at the Udupi district health office. A total of 49 village data were obtained, and 3894 under-five childhood diarrhoea were reported during the study period. The annual incidence of childhood diarrhoea at the taluk level was calculated using excel. The spatial, temporal and space-time diarrhoeal clusters were identified using Kulldorff SaTScan software. Geographic Information System, QGIS 3.20.2 software was used to plot the maps. Results: The analysis of the spatial cluster using SaTScan software for three years in the study area identified eight highrisk areas (p-value<0.0001), covering 17 villages. The most likely spatiotemporal cluster region was located at the northern Karkala, and the most-at-risk period was 1st April 2016 to 30th September 2017 {Log Likelihood Ratio (LLR) =114.67 and p-value <0.00001}. The analysis of purely temporal cluster showed that one most likely cluster happened in all villages (LLR= 73.89, p-value <0.001) from 1st April 2017 to 3rd March 2018. Conclusion: The diarrhoea among under-five children at Karkala taluk was not randomly distributed over space and time. [ABSTRACT FROM AUTHOR]

This study was done to provide an overview of the latest trichomoniasis status in Korea by finding disease clusters and analyzing temporal trends during 2012-2020. Data were obtained from the Health Insurance Review & Assessment Service (HIRA) of Korea. SaTScan and Joinpoint programs were used for statistical analyses. Gyeonggi-do had the highest average population and highest number of cases. The high incidence of T. vaginalis infections were observed among women aged 40-49 and 30-39 years (33,830/year and 33,179/year, respectively). Similarly, the 40-49 and 30-39 age group in men showed the highest average cases (1,319/year and 1,282/year, respectively). Jeollabuk-do was the most likely cluster, followed by Busan/Gyeongsangnam-do/Ulsan/Daegu and Jeju-do and Gwangju. Urban and rural differences were prominent. Trichomoniasis has decreased significantly in most clusters, except for Incheon. Trichomoniasis was decreasing in women recently after peaking around 2014. Men showed different trends according to age. Trichomoniasis was increasing in the 10-39 age groups, but decreasing in the 40-59 age groups. This study might provide an analytic basis for future health measures, policy-makers, and health authorities in developing effective system for prevention of trichomoniasis. [ABSTRACT FROM AUTHOR]

The 2015/2016 National Family Health Survey (NFHS-4) revealed that the prevalence of anemia among children under five years is 58% in India. Lack of nutritional supplementation and lack of health care facilities are found to be important influential factors of anemia among children. We aimed to examine district-level spatial heterogeneity and clustering of associated factors with childhood anemia in India. Geographically weighted regression was applied on the NFHS-5 data for 335 districts. Factors such as prevalence of nutritional supplementation in children and mothers, birth order, antenatal care, diarrhea in children, and stunting were found to be significantly associated. Spatial scan statistics technique identified three significant local spatial clusters of anemia. This study provides findings based on the latest available data which can further assist in the design and execution of tailor-made policies. [ABSTRACT FROM AUTHOR]

Introduction: Globally, India tops by contributing the maximum number of diarrhoeal fatality. Forecasting the path and spread of diarrhoeal disease is critical due to its multifactorial cause, which needs robust spatial analysis and experiential investigations of communicable disease. Aim: To investigate purely spatial, purely temporal, and space-time clusters of diarrhoea among under-five children using a Geographic Information System (GIS) in Karkala taluk of the Udupi district of Karnataka, India. Materials and Methods: A retrospective longitudinal study was conducted involving all the primary health centres of Karkala taluk by the investigators, from the Department of Public Health, KS Hegde Medical Academy, Nitte (DU), Mangalore, Karnataka, India. The data on diarrhoea among under-five children was collected for three years, i.e., from 1st April 2015 to 31st March 2018, at the Udupi district health office. The data from 49 villages were obtained, and 3894 under-five childhood diarrhoea were reported during the study period. The annual incidence of childhood diarrhoea at the taluk level was calculated using excel. The spatial, temporal and space-time diarrhoeal clusters were identified using Kulldorff SaTScan software. Geographic Information System, QGIS 3.20.2 software was used to plot the maps. Results: The analysis of the spatial cluster using SaTScan software for three years in the study area identified eight high-risk areas (p-value

IntroductionThe unprecedented COVID-19 pandemic has greatly affected human health and socioeconomic backgrounds. This study examined the spatiotemporal spread pattern of the COVID-19 pandemic in Malaysia from the index case to 291,774 cases in 13 months, emphasizing on the spatial autocorrelation of the high-risk cluster events and the spatial scan clustering pattern of transmission.MethodologyWe obtained the confirmed cases and deaths of COVID-19 in Malaysia from the official GitHub repository of Malaysia's Ministry of Health from January 25, 2020 to February 24, 2021, 1 day before the national vaccination program was initiated. All analyses were based on the daily cumulated cases, which are derived from the sum of retrospective 7 days and the current day for smoothing purposes. We examined the daily global, local spatial autocorrelation and scan statistics of COVID-19 cases at district level using Moran's I and SaTScan™.ResultsAt the initial stage of the outbreak, Moran's I index > 0.5 (p < 0.05) was observed. Local Moran's I depicted the high-high cluster risk expanded from west to east of Malaysia. The cases surged exponentially after September 2020, with the high-high cluster in Sabah, from Kinabatangan on September 1 (cumulative cases = 9,354; Moran's I = 0.34; p < 0.05), to 11 districts on October 19 (cumulative cases = 21,363, Moran's I = 0.52, p < 0.05). The most likely cluster identified from space-time scanning was centered in Jasin, Melaka (RR = 11.93; p < 0.001) which encompassed 36 districts with a radius of 178.8 km, from November 24, 2020 to February 24, 2021, followed by the Sabah cluster.Discussion and ConclusionBoth analyses complemented each other in depicting underlying spatiotemporal clustering risk, giving detailed space-time spread information at district level. This daily analysis could be valuable insight into real-time reporting of transmission intensity, and alert for the public to avoid visiting the high-risk areas during the pandemic. The spatiotemporal transmission risk pattern could be used to monitor the spread of the pandemic.