Kulldorff's Space-time Scan Statistic Research Articles

Epidemiological Characteristics of Varicella in Anhui Province, China, 2012-2021: Surveillance Study.

Varicella is a mild, self-limited disease caused by varicella-zoster virus (VZV) infection. Recently, the disease burden of varicella has been gradually increasing in China; however, the epidemiological characteristics of varicella have not been reported for Anhui Province. The aim of this study was to analyze the epidemiology of varicella in Anhui from 2012 to 2021, which can provide a basis for the future study and formulation of varicella prevention and control policies in the province. Surveillance data were used to characterize the epidemiology of varicella in Anhui from 2012 to 2021 in terms of population, time, and space. Spatial autocorrelation of varicella was explored using the Moran index (Moran I). The Kulldorff space-time scan statistic was used to analyze the spatiotemporal aggregation of varicella. A total of 276,115 cases of varicella were reported from 2012 to 2021 in Anhui, with an average annual incidence of 44.8 per 100,000, and the highest incidence was 81.2 per 100,000 in 2019. The male-to-female ratio of cases was approximately 1.26, which has been gradually decreasing in recent years. The population aged 5-14 years comprised the high-incidence group, although the incidence in the population 30 years and older has gradually increased. Students accounted for the majority of cases, and the proportion of cases in both home-reared children (aged 0-7 years who are not sent to nurseries, daycare centers, or school) and kindergarten children (aged 3-6 years) has changed slightly in recent years. There were two peaks of varicella incidence annually, except for 2020, and the incidence was typically higher in the winter peak than in summer. The incidence of varicella in southern Anhui was higher than that in northern Anhui. The average annual incidence at the county level ranged from 6.61 to 152.14 per 100,000, and the varicella epidemics in 2018-2021 were relatively severe. The spatial and temporal distribution of varicella in Anhui was not random, with a positive spatial autocorrelation found at the county level (Moran I=0.412). There were 11 districts or counties with high-high clusters, mainly distributed in the south of Anhui, and 3 districts or counties with high-low or low-high clusters. Space-time scan analysis identified five possible clusters of areas, and the most likely cluster was distributed in the southeastern region of Anhui. This study comprehensively describes the epidemiology and changing trend of varicella in Anhui from 2012 to 2021. In the future, preventive and control measures should be strengthened for the key populations and regions of varicella.

A spatial analysis of co-circulating dengue and chikungunya virus infections during an epidemic in a region of Northeastern Brazil

The aim of this study was to describe, through spatial analysis, the cases of arboviruses (dengue and chikungunya), including deaths, during the first epidemic after the circulation of the chikungunya virus (CHIKV) in the state of Pernambuco, Northeastern Brazil. This was an ecological study in both Pernambuco and the state capital, Recife, from 2015 to 2018. The odds ratios (OR) were estimated, and the statistical significance was considered p≤0.05. For the spatial analysis, Kulldorff's space-time scan statistics method was adopted to identify spatial clusters and to provide the relative risk (RR). In order to assess the significance at a level of p < 0.01 of the model, the number of Monte Carlo replications was 999 times. To perform the scan statistics we used the Poisson probability model, with a circular scanning window; annual temporal precision and retrospective analysis. A total of 227 deaths and 158,728 survivors from arboviruses was reported during the study period, with 100 deaths from dengue and 127 from CHIKV. The proportion of deaths from dengue was 0.08% and from chikungunya was 0.35%. The proportion of all those infected (deaths plus survivors) with dengue was 77.42% and with chikungunya was 22.58%. Children aged 0 to 9 years were around 3 times more likely to die than the reference group (OR 2.84; CI95% 1.16–5.00). From the age of 40, the chances of death increased significantly: 40–49 (OR 2.52; CI95% 1.19–5.29), 50–59 (OR 5.55; CI95% 2.76–11.17) and 60 or more (OR 14.90; CI95% 7.79–28.49). Males were approximately twice as likely to die as females (OR 1.77; CI95% 1.36–2.30). White-skinned people were less likely to die compared to non-white (OR 0.60; CI95% 0.41–0.87). The space-time analysis of prevalence in the state of Pernambuco revealed the presence of four clusters in the years 2015 and 2016, highlighting the Metropolitan Macro-region with a relative risk=4 and the Agreste and Hinterland macro-regions with a relative risk=3.3. The spatial distribution of the death rate in the municipality of Recife smoothed by the local empirical Bayesian estimator enabled a special pattern to be identified in the southwest and northeast of the municipality. The spatiotemporal analysis of the death rate revealed the presence of two clusters in the year 2015. In the primary cluster, it may be noted that the aforementioned aggregate presented a RR=7.2, and the secondary cluster presented a RR=6.0. The spatiotemporal analysis with Kulldorff's space-time scan statistics method, proved viable in identifying the risk areas for the occurrence of arboviruses, and could be included in surveillance routines so as to optimize prevention strategies during future epidemics.

The spatial clustering analysis of COVID-19 and its associated factors in mainland China at the prefecture level

Coronavirus disease 2019 (COVID-19) has become a worldwide public health threat. Many associated factors including population movement, meteorological parameters, air quality and socioeconomic conditions can affect COVID-19 transmission. However, no study has combined these various factors in a comprehensive analysis. We collected data on COVID-19 cases and the factors of interest in 340 prefectures of mainland China from 1 December 2019 to 30 April 2020. Moran's I statistic, Getis-Ord Gi⁎ statistic and Kulldorff's space-time scan statistics were used to identify spatial clusters of COVID-19, and the geographically weighted regression (GWR) model was applied to investigate the effects of the associated factors on COVID-19 incidence. A total of 67,449 laboratory-confirmed cases were reported during the study period. Wuhan city as well as its surrounding areas were the cluster areas, and January 25 to February 21, 2020, was the clustering time of COVID-19. The population outflow from Wuhan played a significant role in COVID-19 transmission, with the local coefficients varying from 14.87 to 15.02 in the 340 prefectures. Among the meteorological parameters, relative humidity and precipitation were positively associated with COVID-19 incidence, while the average wind speed showed a negative correlation, but the relationship of average temperature with COVID-19 incidence inconsistent between northern and southern China. NO2 was positively associated, and O3 was negatively associated, with COVID-19 incidence. Environment with high levels of inbound migration or travel, poor ventilation, high humidity or heavy rainfall, low temperature, and high air pollution may be favorable for the growth, reproduction and spread of SARS-CoV-2. Therefore, applying appropriate lockdown measures and travel restrictions, strengthening the ventilation of living and working environments, controlling air pollution and making sufficient preparations for a possible second wave in the relatively cold autumn and winter months may be helpful for the control and prevention of COVID-19.

Space time analysis of dengue fever diagnosed through a network of laboratories in India from 2014-2017.

The Department of Health Research and the Indian Council of Medical Research, Government of India, have established Virus Research and Diagnostic Laboratory Network (VRDLN) to strengthen the laboratory capacity in the country for providing timely diagnosis of disease outbreaks. Fifty-one VRDLs were functional as on December 2017 and had reported about dengue fever across Indian states. The objectives of the study were to detect space time clusters and purely temporal clusters of dengue using Kulldorff's SaTScan statistics using patient level information; and to identify regions at greater risk of developing the disease using Kriging technique aggregating at district level. A total of 211,432 patients from 51 VRDLs were investigated for IgM antibodies or NS1 antigen against dengue virus during the period from 1 January 2014 to 31 December 2017 and among them 60,096 (28.4%) were found to be positive. Kulldorff's space time analysis was used to identify significant clusters over space and time. Kriging technique was used to interpolate dengue data for areas not physically sampled using the relationship in the spatial arrangement of the data set. Maps obtained using both the methods were overlaid to identify the regions at greater risk of developing the disease. Kulldorff Space time Scan Statistics using the Bernoulli model with monthly precision revealed eight statistically significant clusters (P <0.001) for the time period, 1 January 2014 to 31 December 2017. Eight significant clusters identified were districts of Nagpur, Jhunjhunu, Gadag, Dakshin Kannada, Kancheepuram, Sivaganga, Ernakulam and Malda. The purely temporal clusters occurred during the last quarter of 2015 and 2016. The Kriging technique identified north eastern part of the country (Arunachal Pradesh, Nagaland and Manipur) and Gujarat. Dengue fever has spread in all directions in the country. Hence, it is need of the hour to perform an in-depth investigation.

Spatio-temporal analysis of the local African swine fever epidemics in the Russian Federation, 2007 - 2015

Event Abstract Back to Event Spatio-temporal analysis of the local African swine fever epidemics in the Russian Federation, 2007 - 2015 Svetlana Malkhazova1, Fedor Korennoy2*, Olga Petrova2, Vladimir Gulenkin2 and Anton Karaulov2 1 Faculty of Geography, Lomonosov Moscow State University, Russia 2 Federal Center for Animal Health (FGBI ARRIAH), Russia Background and objectives African swine fever (ASF) is a contagious viral disease of domestic and wild pigs, which since its introduction in 2007 has been spreading in the territory of the Russian Federation (RF), affecting both pigs in backyard holdings and large commercial farms, and wild boars. The disease leads to nearly 100% mortality in infected animals and causes huge economic losses to the country’s pig industry (Klinovitskaja et al., 2016). In the course of epidemics, the disease demonstrates both local transmission, which happens within relatively limited area between neighboring farms, villages, etc., and far distant transmission that leads to infection of new regions (Oganesyan et al., 2013; Malogolovkin et al., 2015). Previous studies found 156 km to be an average distance of the ASF spread between farms (Korennoy et al., 2014) and 133 km to be a maximum distance of the disease cases’ clustering in wild boars (Iglesias et al., 2016). The objectives of the present study were: 1) to detect spatio-temporal clusters of the ASF outbreaks the RF, treated as local epidemics; 2) to analyze within-cluster dynamics of outbreaks emergence in domestic and wild pigs; and 3) to reveal associations between within-cluster disease spread patterns and some geographical factors, which supposedly facilitate the local transmission of the ASF virus. Methods A map of the ASF outbreaks in the RF 2007-2015 was created using the national ASF incidence records. The Kulldorff space-time scan statistics (Kulldorff et al., 2005) was employed as a cluster analysis method to detect local ASF epidemics in: 1) domestic pigs’ population (DP); 2) wild boars population (WB), and 3) cumulative population of both. The main quantitative parameters of clusters including the radius, duration and the observed number of outbreaks were obtained. The correlation between the number of outbreaks and geographical factors within each statistically significant cluster was estimated. Results A total of 17, 9 and 17 statistically significant ASF outbreaks’ clusters were detected in PD, WB and cumulative populations respectively. It was found, that all clusters in WB comprise at least one outbreak in DP within the same time period, while only 7 out of 17 clusters in DP comprise WB outbreaks. Majority of cumulative population’s clusters in southern regions of the RF starts from outbreaks in WB, while clusters in central and north regions mainly start from DP outbreaks. Strong positive correlation between the observed number of outbreaks in domestic pigs and the total motorway length, total pig population and the number of rural settlements within the clusters was detected (Pearson correlation coefficients r = 0.83, 0.80 and 0.89 respectively). The number of outbreaks in wild boars and the forest coverage within the clusters were also found to strongly correlate (r = 0.96). Conclusions Our results agree well with the findings of previous studies, which revealed associations between the local ASF spread patterns and density of domestic pigs, densities of major roads and some other human economic activity-related factors (Korennoy et al., 2014; Vergne et al., 2016). It can concluded that the wild boar population should not be seen as a primary source of the disease spread in the RF. The obtained results are expected to contribute into development of a geospatial model for assessment of risks of the ASF local spread in case of the virus introduction into new areas. Acknowledgements The reported study was funded by Russian Foundation for Basic Research (RFBR) according to the research project № 18-05-60037

Visual-spatial and temporal characteristics related to infectious Tuberculosis epidemics in Guangxi Zhuang Autonomous Region, 2012-2015

Objective: To study the spatial and temporal mode of infectious TB transmission in Guangxi Zhuang Autonomous Region (Guangxi). Methods: Data related to infectious TB case (Include smear and/or culture positive patients) in Guangxi were collected from the National Notifiable Disease Reported System (NNDRS) from 2010 to 2015. Spatial-temporal analysis and prediction were performed by SaTScan 7.0.2, GeoDa 1.8.12, R program v 3.3.1 and SPSS 19.0 software, using the time series model, Moran's I global and local spatial autocorrelation (Empirical Bayes adjustment). Kulldorff 's space-time scan statistics displayed by R software was used to identify the temporal and spatial trend of TB. Results: The total number of infectious TB cases, collected from NNDRS was 76 151, and showing a decreasing trend on annual incidence (value of Chi-square for Linear trend=3 464.53, P-value=0.000). The forecast value of TB cases in 2016 was 7 764 (4 971-10 557), with peak in March, analyzed through the Winters'multiplicative model. The Moran's I global Statistics was greater than 0 (0.257-0.390). TB cluster seemed to have been existed for several years. The most significant hot spots seemed to be mainly located in the central and western parts of Guangxi, shown by local spatial autocorrelation statistics and the result from space-time scanning.Counties or districts that located in the east parts of Guangxi presented the low-low relation (significant cold spots). The situation of infectious TB seemed migratory. Conclusions: Our data showed an annual decreasing trend of incidence on infectious TB with temporal concentration in spring and summer. Main clusters (hot spots) were found to be located in the central and western parts of Guangxi. Hopefully, our findings can provide clues to uncover the real mode of TB transmission at the molecular-biological level.

Spatial and temporal analysis of tuberculosis in Zhejiang Province, China, 2009-2012.

BackgroundTuberculosis (TB) is an infectious disease of major public health concern. The disease has demonstrated large space-time variations. This study aims to explore the space-time dynamics of TB cases in an economically and geographically dynamic province in China with specific references of TB control for policy makers.MethodsData on all reported TB cases from 2009 to 2012 were collected from the TB program at the Zhejiang Provincial Center for Disease Control and Prevention. We employed time series and exploratory spatial data analyses, including Moran’s I, Local Getis’s Gi*, and Kulldorff’s space-time scan statistics, to identify the temporal trends and spatial patterns of TB at a county level.ResultsA total of 147,941 TB cases were reported during 2009–2012 in Zhejiang. A higher proportion of TB cases were younger, male, and registered permanent residents among all TB cases notified in the province. TB cases were reported most frequently in April with small peaks in June, July, and October. This disease was spatially clustering with Moran’s I values ranged from 0.29 to 0.32 (p < 0.001). A most likely cluster and ten secondary clusters were identified, mainly concentrated in the southeast and west counties of the province.ConclusionsThis study identified seasonal patterns and significant space-time clusters of TB cases in Zhejiang, China. Poverty, migration, and seasonal effects may play important roles in potential clusters.Electronic supplementary materialThe online version of this article (doi:10.1186/s40249-016-0104-2) contains supplementary material, which is available to authorized users.

Spatiotemporal transmission dynamics of hemorrhagic fever with renal syndrome in China, 2005-2012.

BackgroundHemorrhagic fever with renal syndrome (HFRS) is a rodent-borne disease caused by many serotypes of hantaviruses. In China, HFRS has been recognized as a severe public health problem with 90% of the total reported cases in the world. This study describes the spatiotemporal dynamics of HFRS cases in China and identifies the regions, time, and populations at highest risk, which could help the planning and implementation of key preventative measures.MethodsData on all reported HFRS cases at the county level from January 2005 to December 2012 were collected from Chinese Center for Disease Control and Prevention. Geographic Information System-based spatiotemporal analyses including Local Indicators of Spatial Association and Kulldorff's space-time scan statistic were performed to detect local high-risk space-time clusters of HFRS in China. In addition, cases from high-risk and low-risk counties were compared to identify significant demographic differences.ResultsA total of 100,868 cases were reported during 2005–2012 in mainland China. There were significant variations in the spatiotemporal dynamics of HFRS. HFRS cases occurred most frequently in June, November, and December. There was a significant positive spatial autocorrelation of HFRS incidence during the study periods, with Moran's I values ranging from 0.46 to 0.56 (P<0.05). Several distinct HFRS cluster areas were identified, mainly concentrated in northeastern, central, and eastern of China. Compared with cases from low-risk areas, a higher proportion of cases were younger, non-farmer, and floating residents in high-risk counties.ConclusionsThis study identified significant space-time clusters of HFRS in China during 2005–2012 indicating that preventative strategies for HFRS should be particularly focused on the northeastern, central, and eastern of China to achieve the most cost-effective outcomes.

Cluster detection of HIV infection for clients of female sex workers in selected districts of Tamil Nadu, South India

Methods A large cross sectional Integrated Behavioral and Biological Assessment (IBBA) survey for clients of female sex workers (FSWs) in the three districts (Chennai, Madurai and Salem) of Tamil Nadu, South India was carried out by India AIDS initiative, the Avahan project. The survey collected information from 1217 clients of female sex workers between June and September 2009. The data set was geocoded using Google Earth, Kulldorff Space time Scan Statistics was used for detection of clusters among the clients of FSWs.

Spatio-temporal patterns of under-five mortality in Matlab HDSS in rural Bangladesh

Background: Knowledge of spatial and temporal distributions of mortality and morbidity is important to prioritise areas for adjusting the public health system where people need services most. A Health and Demographic Surveillance System (HDSS) plays an important role where accurate national vital events are not available in identifying areas and periods with excess mortality risks.Methods: The HDSS in Matlab, a rural area of Bangladesh, provided data on yearly number of deaths and children aged below 5 years for each of 90 villages during 1998–2007, along with village location points, longitudes and latitudes. Kulldorff's space–time scan statistic was used to identify villages and periods that experienced high mortality risks in the HDSS area with a statistical significance of p<0.001. Logistic regression was conducted to examine if village-level education and economic status explained village-level mortality risks.Results: There were 3,434 deaths among children aged below 5 years in the HDSS area during 1998–2007 with an average yearly rate of 13 deaths per 1,000 under-five child-years. The mortality rate showed a declining trend with high concentration in 1998–2002, but not in 2003–2007. Two clusters of villages had significantly higher mortality risks in 1998–2002, but not later, and the mortality risks in the high-risk clusters reduced little, but remained significant after controlling for adult education and economic status at village level.Conclusions: Spatial clustering of childhood mortality observed during 1998–2002 had disappeared in subsequent years with a decline in mortality rates. Space–time scanning helps identify high-risk areas and periods to enhance public health actions.

A Space-Time Scan Statistic for Detecting Emerging Outbreaks

As a major analytical method for outbreak detection, Kulldorff's space-time scan statistic (2001, Journal of the Royal Statistical Society, Series A 164, 61-72) has been implemented in many syndromic surveillance systems. Since, however, it is based on circular windows in space, it has difficulty correctly detecting actual noncircular clusters. Takahashi et al. (2008, International Journal of Health Geographics 7, 14) proposed a flexible space-time scan statistic with the capability of detecting noncircular areas. It seems to us, however, that the detection of the most likely cluster defined in these space-time scan statistics is not the same as the detection of localized emerging disease outbreaks because the former compares the observed number of cases with the conditional expected number of cases. In this article, we propose a new space-time scan statistic which compares the observed number of cases with the unconditional expected number of cases, takes a time-to-time variation of Poisson mean into account, and implements an outbreak model to capture localized emerging disease outbreaks more timely and correctly. The proposed models are illustrated with data from weekly surveillance of the number of absentees in primary schools in Kitakyushu-shi, Japan, 2006.