Spatio-temporal Scan Statistics Research Articles

Application of spatiotemporal scan statistics in cases of intentional homicides in northern Brazil

The crime of intentional homicide in Brazil is worrying. In the northern region, this type of crime has been growing since 2020. In this sense, we decided to apply Kuldorff's prospective space-time scan statistics in order to identify emerging counties. We conclude that there are two emerging clusters of high relative risk (Amazonas and Pará) that require rapid intervention and two clusters of low relative risk (Acre, Roraima, Amazonas, Amapá and Pará) that do not require urgent intervention. Some characteristics of these two clusters are presented: radius, population, relative risk, likelihood ratio.

Detecting spatial clusters of human scabies in Tigray, Ethiopia from 2018 to 2023

BackgroundScabies is one of public health concerns among communicable disease in Ethiopia, especially among disadvantaged and the poor. This current study aims to detect the spatiotemporal patterns of scabies in Tigray from 2018 to 2023 using scabies data aggregated at the zonal level. The study also examined the persistent patterns in the spatial variation of scabies incidence across the administrative regions during the study period.MethodWe collected scabies data using a weekly disease surveillance reporting format of the country from 2018 to 2023 across all accessible district health facilities in Tigray region, Ethiopia. We conducted retrospective analyses using both purely spatial and spatiotemporal scan statistic approaches, employing a discrete Poisson probability model to identify statistically significant clusters of high scabies rates throughout the Tigray regional zones in Ethiopia. Our methodology involved the use of Kulldorff’s spatial scan statistic software (SaTScan v10.1.3), R programming software version 4.3.1, and ArcGIS Pro for all analyses.ResultsA total of 101,116 cases of scabies were reported from 2018 to 2023. Our study indicated a spatial heterogeneity in the pattern of scabies across Tigray region as well as its localization among geographically contiguous zones across space, except for the Western zone of Tigray where no data was collected. The detected statistically significant spatial clusters \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\:p-value<0.05$$\\end{document} persisted mainly in the Central, Eastern and Northwestern zones of Tigray over the six years of the study period. The highest relative risk (RR) was recorded in year 2021 (\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\:RR=8.49,\\:LLR=6860.34,p-value=0.01)$$\\end{document}. The central zone had the major clusters of scabies at district level from 2018 to 2023. The heterogeneous distribution of scabies across Tigray could be due to the spatial variations in the determinants of scabies (such as socioeconomic status, demographics, and material deprivation) across the region.ConclusionAn enormous burden of scabies was reported over a period of six years. The present study found localized clusters of high scabies rates at district and zonal levels in Tigray, Ethiopia, possibly due to differences in various determinants of scabies such as access to WASH services. The findings could help the government and health authorities to develop and implement scabies control strategies in Tigray, with a focus on high-risk districts and zones to ensure optimal resource allocation.

Detection of spatial, temporal and spatiotemporal distribution of diarrhea incidence among under-five children in Central Gondar zone, Northwest Ethiopia: a time-series study (2019–2022)

Under-five children’s diarrhea is a significant public health threat and the World Health Organization (WHO) reported it as the second leading cause of children’s death worldwide. In this study area, little is known about the spatiotemporal distribution of under-5 diarrhea incidence. This study was therefore, conducted among all districts in the Central Gondar zone to assess the spatial, temporal, and spatiotemporal variation in diarrhea incidence among under-five children in the Central Gondar zone. The data for children under 5 years of age with diarrhea was obtained from Central Gondar Zone Health Department diarrhea reports from January 2019 to December 2022. All districts were included and geo-coded. The spatial data were created in ArcGIS 10.8.1. Global and local spatial autocorrelation were used to detect hot spots and cold spots. The Poisson model was generated by applying the Kulldorff method in SaTScan™9.6 to analyse the the purely temporal, spatial, and space-time clusters. The study revealed spatial variation of under-5 diarrhea where Gondar City, Gondar Zuria, East Dembia, and Lay Armacho districts were the high-rate spatial clusters during the study period. A year search window for temporal scan statistic identified 01 January 2020-30 December 2021 as risk periods across all districts. Spatiotemporal scan statistics detected high-rate clusters at Gondar City, Gondar Zuria, East Dembia, Lay Armacho, and Alefa between 2019 and 2022. In conclusion, there has been a spatial, temporal, and spatiotemporal variability of under-5 children’s diarrhea in the Central Gondar Zone. Interventional and preventive strategies should be developed and given priority to the areas that has been detected as a hotspot in this study to reduce the mortality and morbidity of under 5 children.

Space-time analysis of work-related musculoskeletal disorders in Brazil: an ecological study.

This study aimed to analyze the incidence of work-related musculoskeletal disorders (WMSD) in Brazil from 2007 to 2019, examining the spatial, temporal, and spatiotemporal patterns of their occurrence. An ecological time series study was conducted using spatial analysis techniques. WMSD morbidity data from 2007 to 2019 were collected from the Brazilian Information System for Notificable Diseases of the Brazilian Health Informatics Department. Incidence rates were standardized and smoothed using the local empirical Bayes' theorem. Time trends were analyzed by segmented linear regression. Spatial analysis was performed using Moran's univariate global (I) and local (LISA) indexes. The spatiotemporal scan statistic was used to identify high-risk spatiotemporal clusters for WMSD. A total of 93,387 cases of WMSD were recorded in Brazil. Temporal trends showed an increase in all regions except the Northeast, which remained stable. The incidence of WMSD showed a spatial dependence, with spatial and space-time clusters identified, especially in the Southeast region, overlapping the largest economic-industrial center of the country. The spatiotemporal clustering observed in one region suggests the highest level of industrial and economic development. Our findings highlight the need to implement intersectoral surveillance policies, inspect working conditions, and invest in the prevention and promotion of workers' health.

Spatiotemporal distribution characteristics and influencing factors of the rate of cardiovascular hospitalization in Ganzhou city of China.

The objective of this study was to analyze hospitalization rates for cardiovascular diseases (CVD) in Ganzhou City, Jiangxi Province of China from 2015 to 2020 and to uncover the spatiotemporal distribution characteristics and influencing factors, and thus to provide reference for the prevention and control of CVD and public health resources planning. The hospitalization data for CVDs from 2016 to 2020 was obtained from the First Affiliated Hospital of Gannan Medical University, and ArcGIS 10.8, SaTScan 9.5, and Matlab 20.0 were used to analyze the spatial autocorrelation, spatiotemporal scan statistics, and potential affecting factors of the hospitalization rates. The hospitalization rate for CVDs in Ganzhou City showed a slightly increasing trend from 2016 to 2020, with higher rates in winter and summer than that in spring and autumn, and the individuals aged 61 and above constitute a higher proportion compared to other age groups. Additionally, there was a positive correlation between hospitalization rates for CVDs and the counties and districts in Ganzhou City, with high-high aggregation areas mainly distributed in Nankang District, the western urban area of Ganzhou City. The spatial scan analysis identified three different types of significant aggregation areas: high-risk, low-risk, and middle-risk areas. The high-risk area was mainly centered around Zhanggong District or Shangyu County in the central and western regions, with a disease hospitalization rate 2-3 times higher than the rest areas. The study also found that environmental meteorological factors such as the annual average concentration of NO2, O3, average annual temperature, and annual maximum temperature diurnal range had a significant positive effect on hospitalization rates for CVDs in Ganzhou City, with O3 concentration and average annual temperature having significant positive indirect spatial spillover effects. Winter and summer are the seasons with high hospitalization rate of cardiovascular diseases. County residents aged 61 and above are the higher-risk population that needs to pay more attention on for prevention and control of CVD in Ganzhou City, which exhibits significant spatiotemporal clustering. The urban areas of Zhanggong and Nankang in Ganzhou City are the key areas for prevention and control of CVD. The hospitalization rate of CVD in Ganzhou City is influenced by the aforementioned four environmental meteorological factors, with the annual maximum temperature diurnal range showing the most significant positive direct effect.

Spatiotemporal distribution and environmental influences of severe fever with thrombocytopenia syndrome in Shandong Province, China

BackgroundSevere fever with thrombocytopenia syndrome (SFTS) is an emerging infectious disease discovered in China in 2009. The purpose of this study was to describe the spatiotemporal distribution of SFTS and to identify its environmental influencing factors and potential high-risk areas in Shandong Province, China.MethodsData on the SFTS incidence from 2010 to 2021 were collected. Spatiotemporal scan statistics were used to identify the time and area of SFTS clustering. The maximum entropy (MaxEnt) model was used to analyse environmental influences and predict high-risk areas.ResultsFrom 2010 to 2021, a total of 5705 cases of SFTS were reported in Shandong. The number of SFTS cases increased yearly, with a peak incidence from April to October each year. Spatiotemporal scan statistics showed the existence of one most likely cluster and two secondary likely clusters in Shandong. The most likely cluster was in the eastern region, from May to October 2021. The first secondary cluster was in the central region, from May to October 2021. The second secondary cluster was in the southeastern region, from May to September 2020. The MaxEnt model showed that the mean annual wind speed, NDVI, cattle density and annual cumulative precipitation were the key factors influencing the occurrence of SFTS. The predicted risk map showed that the area of high prevalence was 28,120 km2, accounting for 18.05% of the total area of the province.ConclusionsThe spatiotemporal distribution of SFTS was heterogeneous and influenced by multidimensional environmental factors. This should be considered as a basis for delineating SFTS risk areas and developing SFTS prevention and control measures.

Geographic and temporal trends in pediatric and young adult brain tumors in Kentucky, 1995–2019

IntroductionPediatric and young adult brain tumors (PYBT) account for a large share of cancer-related morbidity and mortality among children in the United States, but their etiology is not well understood. Previous research suggests the Appalachian region of Kentucky has high rates of PYBT. This study explored PYBT incidence over 25 years in Kentucky to identify geographic and temporal trends and generate hypotheses for future research. MethodsThe Kentucky Cancer Registry contributed data on all PYBT diagnosed among those aged 0–29 during years 1995–2019. Age- and sex-adjusted spatio-temporal scan statistics—one for each type of PYBT, and one for all types—comprised the primary analysis. These results were mapped along with environmental and occupational data. ResultsFindings indicated that north-central Kentucky and the Appalachian region experienced higher rates of some PYBT. High rates of astrocytomas were clustered in a north-south strip of central Kentucky toward the end of the study period, while high rates of other specified types of intracranial and intraspinal neoplasms were significantly clustered in eastern Kentucky. The area where these clusters overlapped, in north-central Kentucky, had significantly higher rates of PYBT generally. DiscussionThis study demonstrates north-central Kentucky and the Appalachian region experienced higher PYBT risk than the rest of the state. These regions are home to some of Kentucky’s signature industries, which should be examined in further research. Future population-based and individual-level studies of genetic factors are needed to explore how the occupations of parents, as well as prenatal and childhood exposures to pesticides and air pollutants, impact PYBT incidence.

The COVID-19 pandemic and changes in spatio-temporal patterns of suicide: monthly variations among localities in Argentina

The aim of this study was to assess space-time clustering of suicide in Argentina between 2017 and 2020. A spatio-temporal scan statistic using a space-time permutation model was employed to retrospectively detect clusters of total suicides by sex and by age group. From 2017 to 2020, six statistically significant spatio-temporal clusters were identified in Argentina with either more or less suicides than expected. Two clusters extended from May 2019 to July and September 2020, while the remaining four clusters were detected only during 2020. The start of the COVID-19 pandemic seems to have changed the spatio-temporal patterns of suicide in Argentina. Despite the national decrease in the number of suicides during 2020 compared to previous years, the spatio-temporal analysis revealed both areas with decreases and increases in suicide. This heterogeneous scenario highlights the need for the study of local effects behind the emergence of these spatio-temporal suicide clusters.

Understanding COVID-19: comparison of spatio-temporal analysis methods used to study epidemic spread patterns in the United States

This article examines three spatiotemporal methods used for analyzing of infectious diseases, with a focus on COVID-19 in the United States. The methods considered include inverse distance weighting (IDW) interpolation, retrospective spatiotemporal scan statistics and Bayesian spatiotemporal models. The study covers a 12-month period from May 2020 to April 2021, including monthly data from 49 states or regions in the United States. The results show that the spread of COVID-19 pandemic increased rapidly to a high value in winter of 2020, followed by a brief decline that later reverted into another increase. Spatially, the COVID-19 epidemic in the United States exhibited a multi-centre, rapid spread character, with clustering areas represented by states such as New York, North Dakota, Texas and California. By demonstrating the applicability and limitations of different analytical tools in investigating the spatiotemporal dynamics of disease outbreaks, this study contributes to the broader field of epidemiology and helps improve strategies for responding to future major public health events.

Soil-transmitted helminthiasis in mainland China from 2016 to 2020: a population-based study

Soil-transmitted helminthiasis is epidemic in China and many other countries of the world, and has caused substantial burdens to human health. We conducted successive national monitoring in China from 2016 to 2020 to analyze the prevalence, changing trends, and factors influencing soil-transmitted helminthiasis, which provided a reference for future control strategies. Soil-transmitted helminth monitoring was carried out in 31 provinces (autonomous regions or municipalities, herein after referred to as "provinces") throughout China. Each province determined the number and location of monitoring sites (counties), and a unified sampling method was employed. At least 1,000 subjects were investigated in each monitoring county. Stool samples were collected and the modified Kato-Katz thick smear method was employed for stool examination. Infection data and the details of factors influencing soil-transmitted helminthiasis from 2016 to 2020 were collected from national monitoring sites. Additional influencing factors such as environment, climate and human activities were obtained from authoritative websites. Prevalence of soil-transmitted helminths was presented by species, province, sex, and age group. ArcGIS software was used to conduct spatial autocorrelation and hotspot analysis on the infection data. A Poisson distribution model and SaTScan software were used to analyze the infection data with retrospective spatiotemporal scan statistics. A database was built by matching village-level infection rate data with influencing factors. Subsequently, machine learning methods, including a Linear Regression (LR), a Random Forest (RF), a Gradient Boosted Machine (GBM), and an Extreme gradient boosting (XGBOOST) model was applied to construct a model to analyze the main influencing factors of soil-transmitted helminthiasis. The infection rates of soil-transmitted helminths at national monitoring sites from 2016 to 2020 were 2.46% (6,456/262,380), 1.78% (5,293/297,078), 1.29% (4,200/326,207), 1.40% (5,959/424,766), and 0.84% (3,485/415,672), respectively. The infection rate of soil-transmitted helminths in 2020 decreased by 65.85% compared to that in 2016. From 2016 to 2020, the infection rate of soil-transmitted helminthiasis was relatively high in southern and southwestern China, including Hainan, Yunnan, Sichuan, Guizhou, and Chongqing. In general, the infection rate was higher in females than in males, with the highest rate in the population aged 60 years and above, and the lowest in children aged 0-6 years. Global autocorrelation and hotspot analyses revealed spatial aggregation in both the national and local distribution of soil-transmitted helminthiasis in China from 2016 to 2020. The hotspots were concentrated in southwestern China. The spatiotemporal scanning analysis revealed aggregation years from 2016 to 2017 located in southwestern China, including Yunnan, Sichuan, Chongqing, Guizhou and Guangxi. The RF model was the best fit model for the infection rate of soil-transmitted helminths in China. The top six influencing factors of this disease in the model were landform, barefoot farming, isothermality, temperature seasonality, year, and the coverage of sanitary toilets. The overall infection rate of soil-transmitted helminths in China showed a decreasing trend from 2016-2020 due to the implementation of control measures and the economic boom in China. However, there are still areas with high infection rates and the distribution of such areas exhibit spatiotemporal aggregation. As a strategic next step, control measures should be adjusted to local conditions based on the main influencing factors and the prevalence of different sites to aid in the control and elimination of soil-transmitted helminthiasis. This research was funded by the National Key Research and Development Program of China (Grant Nos. 2021YFC2300800 and 2021YFC2300804) and the National Natural Science Foundation of China (Grant No. 32161143036).

Abstract P272: Spatial and Temporal Trends of Infective Endocarditis Hospitalizations in Kentucky, 2008-2018

Background: Infective endocarditis (IE) hospitalization rates have increased significantly in the last decade, primarily due to the opioid abuse epidemic. A North Carolina study showed an increase in intravenous drug use infective endocarditis (IDU-IE) from 0.9 to 10.9/100,000 during 2007-2017. In 2016, Kentucky ranked among the top ten states with the highest opioid-related overdose deaths. Limited data about IE trends are available for Kentucky. Objective: To examine county-level spatial and temporal trends in IE rates in Kentucky and identify significant high-rate clusters. Methods: We used the Kentucky hospital inpatient discharge data (2008-2018). We included hospitalizations for patients 18 years and older who are Kentucky residents. All IE admissions were identified using ICD-9 and ICD-10 diagnosis codes (421.0, 421.1, 421.9, 424.90, 424.91, 424.99, 112.81, I33.0, I39, I33.9, I38, A3282, and B376). IDU-IE were identified using additional ICD codes for injected illicit drug use. We used US Census data to calculate yearly hospitalization rates of IE /100,000 residents. Joinpoint regression was used to calculate annual percent change (APC). We conducted spatiotemporal cluster analysis at the county level using a Poisson model in SaTScan. Results: Throughout the study period, there were 17,787 IE hospital admissions, including 3,577 IDU-IE. Medicaid patients were more often in the IDU-IE group rather than the non-IDU-IE (71% versus 15%). The IDU-IE hospitalization rate increased from 1.2 to 27.3 / 100,000 in 2008 to 2018 (Figure). From 2013 to 2018, APC for IDU-IE hospitalizations was 71.3, (95% CI: 47.8, 98.5). The spatiotemporal scan statistics identified five statistically significant clusters. The largest cluster occurred during 2015-2018 and included 34 counties out of 120 Kentucky counties, all located in the Appalachian region of Eastern Kentucky (RR= 2.1, p&lt;0.00001). Conclusion: Our study showed increased rates of IE hospitalizations due to an increase in IDU-IE, especially in Eastern Kentucky.

Spatial modeling of homicide mortality in the Northeast region of Brazil.

To analyze the spatiotemporal distribution of homicide mortality and association with social determinants of health in the Northeast Region of Brazil. Ecological study with spatiotemporal modeling of homicide deaths between 2000 and 2019. Temporal trends were analyzed by segmented linear regression. Crude mortality was calculated and adjusted by smoothing the local empirical Bayesian method and analyzed by the Global/Local Moran Index and spatiotemporal scan statistics. The association between social determinants of health and homicide mortality was performed using multiple linear regression and autoregressive spatial models. 353,089 deaths were recorded. Mortality increased from 2000 to 2019, with an annual increase of 4.37 in males and 3.57 in females. High risk spatial and spatiotemporal clusters were identified in the coastal region of the states. The spatial regression model showed an association with socioeconomic inequalities. High risk areas for homicides associated with socioeconomic inequality, which should be considered as a priority for designing and investing in public health policies were investigated.

Analysis of the distribution characteristics of prostate cancer and its environmental factors in China.

The high incidence and mortality and the increasing trend of prostate cancer has been one of the public health issues in many countries and regions. Meanwhile, the spatio-temporal heterogeneity of prostate cancer implies that lifestyle and ecological changes may be associated with prostate cancer, however, sufficient evidence is still lacking. This paper tried to reveal the spatial and temporal distribution characteristics of prostate cancer in China and explore the potential associations with related socioeconomic and natural condition factors. Data on prostate cancer incidence and mortality in 182 counties (districts) in mainland China from 2014-2016 were collected, and the distribution characteristics of prostate cancer were analyzed using spatiotemporal scan statistic. Spatial regression models and geodetector method were used to analyze the potential associations between meteorological conditions, socioeconomic development, and prostate cancer incidence and mortality. SaTScan, GeoDa, and GeoDetector were used for the above statistical analyses. The high-risk clusters for prostate cancer incidence and mortality were located in southeastern China, and the low-risk clusters were located in north-central China. Spatial regression models showed that the number of industrial enterprises/km2 (incidence: β = 0.322, P < 0.001; mortality: β = 0.179, P < 0.001), GDP (incidence:β = 0.553, P < 0.001; mortality: β = 0.324, P < 0.001), number of beds in medical and health institutions/1000 persons (incidence: β = 0.111, P = 0.005; mortality: β = 0.068, P = 0.021), and urbanization rate (incidence: β = 0.156, P < 0.001; mortality: β = 0.100, P < 0.001) were positively associated with the incidence and mortality of prostate cancer. The urbanization rate (incidence: q = 0.185, P < 0.001; mortality: q = 0.182, P < 0.001) has the greatest explanatory power, and the interaction of all factors was bivariate enhanced or nonlinearly enhanced. The distribution of prostate cancer in China has obvious spatial heterogeneity. The incidence and mortality rate of prostate cancer are on the rise, and special plans should be formulated in each region according to local conditions.

A Novel Non-Parametric Spatiotemporal Scan Statistic: An Application to Detect Disease Outbreaks

The majority of the widely used scan statistics are based on distributional assumptions. Contrary to the existing methods, with a new perspective in clustering, the Mann-Whitney Scan Statistic was introduced to detect clusters in continuous data indexed by time or space, without any distributional assumptions or parameters to set up. We propose an extension of the Mann-Whitney Scan Statistic that can be applied to spatiotemporal data based on spatiotemporal distance measure. This novel scan statistic is distribution-free and seems to be powerful against parametric spatiotemporal scan statistics. The results are applicable in a wide variety of spatiotemporal domains, including epidemiology, socioeconomic analysis and climate sciences, irrespective of continuous or discrete data.

Spatiotemporal Pattern Evolution and Driving Factors of Brucellosis in China, 2003-2019.

Brucellosis is a prevalent zoonotic disease worldwide. However, the spatiotemporal patterns evolution and its driving factors of Brucellosis have not been well explored. In this study, spatiotemporal scan statistics were applied to describe the spatiotemporal pattern of evolution in Brucellosis from 2003 to 2019 in mainland China, and GeoDetector analysis was further conducted to explore the driving effects of environmental, meteorological, and socioeconomic factors. We identified a distinct seasonal pattern for Brucellosis, with a peak in May and lowest incidence between September and December. High-risk clusters were first observed in the northwestern pastoral areas and later expanded to the southern urban areas. The spatiotemporal heterogeneity was mainly explained by total SO2 emissions, average annual temperature, sheep output, and consumption of meat per capita with explanatory powers of 45.38%, 44.60%, 40.76%, and 30.46% respectively. However, the explanatory power changed over time. Specifically, the explanatory power of average annual temperature tended to decrease over time, while consumption of meat per capita and total output of animal husbandry tended to increase. The most favorable conditions for the spread of Brucellosis include 0.66–0.70 million tons of SO2 emissions, 9.54–11.68 °C of average annual temperature, 63.28–72.40 million heads of sheep output, and 16.81–20.58 kg consumption of meat per capita. Brucellosis remains more prevalent in traditional pastoral areas in Northwest China, with the tendency of spreading from pastoral to non-pastoral, and rural to urban, areas. Total SO2 emission, average annual temperature, sheep output, and consumption of meat per capita dominated the spatial heterogeneity of Brucellosis with changes in explanatory power over time.

The final stages of dog rabies elimination from Japan.

Rabies is a lethal zoonotic disease mainly transmitted to humans by dog bites. The purpose of this study was to assess the efficacy of rabies control policies in Japan, which resulted in the elimination of the disease from the country in 1957. Using historical records from the Kanto region (Chiba, Kanagawa, Saitama and Tokyo Prefectures) between 1947 and 1956 where the final canine cases were recorded, we undertook a descriptive epidemiological study, applying spatio-temporal scan statistics using SaTScan and estimating the effective reproduction number (Rt ) for the clusters and each prefecture using the growth rates. There were 1,567 dog rabies and 161 human rabies cases recorded during this period. Vaccination coverage in registered dogs was over 70% after 1951, with much lower coverage in free-roaming and unregistered dogs. Eight clusters of dog rabies cases were identified: the first appeared in 1947 in Tokyo and was linked to three further clusters in peripheral prefectures between 1947 and 1951. Three more clusters occurred in Tokyo again between 1952 and 1954, and the last cluster was in Tokyo and Kanagawa between 1955 and 1956. Rt in the first cluster was 1.68, and Rt values in the others ranged between 1.18 and 1.86, with an exception of 4.05 in the smallest cluster in Tokyo in 1952 (10 cases). The moving average of Rt coincided with the clusters. As dog vaccination and dog management progressed, and the number of dog rabies cases declined, the moving average of Rt declined to below 1. Delays in the implementation of dog management policies in Kanagawa may have prolonged this last outbreak. These results demonstrate the effectiveness of coordinated control policy involving dog vaccination and management of free-roaming dog populations for rabies elimination.

Spatio-temporal epidemiology of animal and human rabies in northern South Africa between 1998 and 2017

BackgroundRabies is a fatal zoonotic disease that is maintained in domestic dogs and wildlife populations in the Republic of South Africa. A retrospective study was conducted to improve understanding of the dynamics of rabies in humans, domestic dogs, and wildlife species, in relation to the ecology for three northern provinces of South Africa (Limpopo, Mpumalanga, and North-West) between 1998 and 2017.MethodsA descriptive epidemiology study was conducted for human and animal rabies. Dog rabies cases were analyzed using spatio-temporal scan statistics. The reproductive number (Rt) was estimated for the identified disease clusters. A phylogenetic tree was constructed based on the genome sequences of rabies viruses isolated from dogs, jackals, and an African civet, and Bayesian evolutionary analysis using a strict time clock model. Several ecological and socio-economic variables associated with dog rabies were modeled using univariate analyses with zero-inflated negative binomial regression and multivariable spatial analyses using the integrated nested Laplace approximation for two time periods: 1998–2002 and 2008–2012.ResultsHuman rabies cases increased in 2006 following an increase in dog rabies cases; however, the human cases declined in the next year while dog rabies cases fluctuated. Ten disease clusters of dog rabies were identified, and utilizing the phylogenetic tree, the dynamics of animal rabies over 20 years was elucidated. In 2006, a virus strain that re-emerged in eastern Limpopo Province caused the large and persistent dog rabies outbreaks in Limpopo and Mpumalanga Provinces. Several clusters included a rabies virus variant maintained in jackals in Limpopo Province, and the other variant in dogs widely distributed. The widely distributed variant maintained in jackal populations in North-West Province caused an outbreak in dogs in 2014. The Rt was high when the disease clusters were associated with either multiple virus strains or multiple animal species. High-risk areas included Limpopo and Mpumalanga Provinces characterized by woodlands and high temperatures and precipitation.ConclusionCanine rabies was maintained mainly in dog populations but was also associated with jackal species. Rural communities in Limpopo and Mpumalanga Provinces were at high risk of canine rabies originating from dogs.

Temporal and Spatial Evolution of the African Swine Fever Epidemic in Vietnam

African swine fever (ASF) is a severe infectious disease affecting domestic and wild suids. Spatiotemporal dynamics analysis of the ASF is crucial to understanding its transmission. The ASF broke out in Vietnam in February 2019. The research on the spatiotemporal evolution characteristics of ASF in Vietnam is lacking. Spatiotemporal statistical methods, including direction analysis, spatial autocorrelation analysis, and spatiotemporal scan statistics were used to reveal the dynamics of the spatial diffusion direction and spatiotemporal aggregation characteristics of ASF in Vietnam. According to the cessation of the epidemic, it was divided into three phases: February to August 2019 (phase 1), April to December 2020 (phase 2), and January 2021 to March 2022 (phase 3). The ASF showed a significant spread trend from north to south in phase 1. The occurrence rate of the ASF aggregated spatially in phase 1 and became random in phases 2 and 3. The high−high ASF clusters (the province was a high cluster and both it and its neighbors had a high ASF occurrence rate) were concentrated in the north in phases 1 and 2. Four spatiotemporal high-risk ASF clusters were identified with a mean radius of 121.88 km. In general, there were significant concentrated outbreak areas and directional spread in the early stage and small-scale, high-frequency, and randomly scattered outbreaks in the later stage. The findings could contribute to a deeper understanding of the spatiotemporal spread of the ASF in Vietnam.

Epidemiological characteristics of mumps from 2004 to 2020 in Jiangsu, China: a flexible spatial and spatiotemporal analysis.

Abstract The mumps resurgence has frequently been reported around the world in recent years, especially in many counties mumps vaccines have been widely used. This study aimed to describe the spatial epidemiological characteristics of mumps in Jiangsu, and provide a scientific basis for the implementation and adjustment of strategies to prevent and control mumps. The epidemiological characteristics were described with ratio or proportion. Spatial autocorrelation, Tango's flexible spatial scan statistics, and Kulldorff's elliptic spatiotemporal scan statistics were applied to identify the spatial autocorrelation, detect hot and cold spots of mumps incidence, and aggregation areas. A total of 172 775 cases were reported from 2004 to 2020 in Jiangsu. The general trend of mumps incidence is declining with a bimodal seasonal distribution identified mainly in summer and winter, respectively. Children aged 5–10 years old are the main risk group. A migration trend of hot spots from southeast to northwest over time was found. Similar high-risk aggregations were detected in the northwestern parts through spatial-temporal analysis with the most likely cluster time frame around 2019. Local medical and health administrations should formulate and implement targeted health care policies and allocate health resources more appropriately corresponding to the epidemiological characteristics of mumps.

Applying Spatio-temporal Scan Statistics and Spatial Autocorrelation Statistics to identify Covid-19 clusters in the world - A Vaccination Strategy?

With the whole world being affected by the pandemic, it is a matter of great importance that studies about spatial and spatio-temporal aspects of the COVID-19 (Sars-Cov-2) pandemic should be conducted, therefore the main goal of this paper is to present the Global Moran's I and the Local Moran's I used to evaluate spatial association in the number of deaths and infections by COVID-19, and a spatio-temporal Poisson scan statistic used to identify emerging or “alive” clusters of infections by Sars-Cov-2 in space and time. As of January 2021 vaccination against COVID-19 already started, since the use of spatial clustering methods to identify non-vaccinated populations is not new among studies on vaccination coverage strategies, this paper also aims to discuss the implementation of spatial and spatio-temporal clustering methods in early vaccination.