OBJECTIVE: 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.
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.

Severe fever with thrombocytopenia syndrome (SFTS), an emerging tick-borne disease first reported in rural areas of central China, has become a major public health concern in endemic areas. The epidemic dynamic and ecologic factors of SFTS incidence at a village scale remain unclear. Here we analyzed the epidemiological characteristics of SFTS cases in Shangcheng County, the first reported areas of SFTS in China. A retrospective space-time cluster analysis was conducted to identify the dynamics of hotspot areas, and the negative binomial regression model was conducted to examine potential factors contributing to the incidence of SFTS at the village level. A total of 1,219 SFTS cases were reported in Shangcheng County from 2011 to 2020, with a case fatality rate of 12.0%. The median age of patients was 64 years, and 81.7% of patients were over 50 years old. Women accounted for 60.3% of all cases, and the incidence rate was significantly higher than that of men (Pearson χ2 test, P<0.001). Five spatial-temporal clusters were identified, and mostly distributed in the central part of the county. Higher risk of SFTS incidence was shown in villages with higher percentage coverages of forest and tea plantation, and higher goat density. In villages where the ratio of cultivated land area to forest land area was between 0.2 and 1.2, the risk of SFTS incidence increased significantly, with an incidence rate ratio of 1.33 (95% CI: 1.04‒1.72, p = 0.024). Our findings indicated that ecotone between forest and cultivated land might be the most important risk settings for exposure and infection with SFTS virus in endemic areas of central China. Precise identification of risk factors and high-risk areas at a suitable scale is conducive to carrying out targeted measures and improving the surveillance of the disease.

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.

Severe fever with thrombocytopenia syndrome (SFTS) is an emerging natural focus, tick-borne disease caused by a novel bunyavirus named SFTS virus (SFTSV). The main purpose of this study was to analyze the environmental risk factors and geographic distribution of SFTS natural foci in Jiangsu Province. A retrospective space-time analysis by SaTScan software was used to detect clusters at the town level. The maximum entropy modeling method was applied to construct the ecological niche model and analyze the environmental risk factors, and then to draw the predicted risk map. The performance of the model was assessed using the area under the curve (AUC) and known occurrence locations. During the years 2010-2016, a total of 140 laboratory-confirmed indigenous SFTS cases occurred in Jiangsu Province, with 66 occurrence locations. The reported number of SFTS cases increased year by year and SFTS cases occurred from April to October with a peak between May and August each year. Three clusters detected by space-time scan statistical analysis were connected together and shared the similar ecological environmental characteristic of hilly landscape. Fifteen environmental variables with different percent contribution can influence the ecological niche model in different degrees, whereas slope (suitable range: 0.1-4) and maximum temperature of warmest month (suitable range: 32.8-34.2°C) as the key environmental factors contributed 46.1% and 23.2%, respectively. The model had high accuracy on prediction with the averaged training AUC of 0.926. Within a predicted risk map, potential areas at high risk and 10 previously unidentified endemic regions were recognized. The distribution of SFTS natural foci was under the influence of multidimensional environmental factors. Slope and maximum temperature of warmest month were the key environmental risk factors. These results provide a valuable basis for the selection of prevention and control strategies, and the identification of potential risk areas.

This study aims to describe the spatial and temporal characteristics of human infections with H7N9 virus in China using data from 19 February 2013 to 30 September 2017 extracted from Centre for Health Protection of the Department of Health (CHP) and electronic databases managed by China\'s Center for Disease Control (CDC) and provincial CDCs synthetically using the Geographic Information System (GIS) software ArcMap™ 10.2 and SaTScan. Based on the multiple analyses of the A(H7N9) epidemics, there was a strong seasonal pattern in A(H7N9) virus infection, with high activity in the first quarter of the year, especially in January, February, and April, and a gradual dying out in the third quarter. Spatial distribution analysis indicated that Eastern China contained the most severely affected areas, such as Zhejiang Province, and the distribution shifted from coastline areas to more inland areas over time. In addition, the cases exhibited local spatial aggregation, with high-risk areas most found in the southeast coastal regions of China. Shanghai, Jiangsu, Zhejiang, and Guangdong were the high-risk epidemic areas, which should arouse the attention of local governments. A strong cluster from 9 April 2017 to 24 June 2017 was also identified in Northern China, and there were many secondary clusters in Eastern and Southern China, especially in Zhejiang, Fujian, Jiangsu, and Guangdong Provinces. Our results suggested that the spatial-temporal clustering of H7N9 in China is fundamentally different, and is expected to contribute to accumulating knowledge on the changing temporal patterns and spatial dissemination during the fifth epidemic and provide data to enable adequate preparation against the next epidemic.

BACKGROUND: Tuberculosis (TB) is still one of the most serious infectious diseases in the mainland of China. So it was urgent for the formulation of more effective measures to prevent and control it.
METHODS: The data of reported TB cases in 340 prefectures from the mainland of China were extracted from the China Information System for Disease Control and Prevention (CISDCP) during January 2005 to December 2015. The Kulldorff\'s retrospective space-time scan statistics was used to identify the temporal, spatial and spatio-temporal clusters of reported TB in the mainland of China by using the discrete Poisson probability model. Spatio-temporal clusters of sputum smear-positive (SS+) reported TB and sputum smear-negative (SS-) reported TB were also detected at the prefecture level.
RESULTS: A total of 10 200 528 reported TB cases were collected from 2005 to 2015 in 340 prefectures, including 5 283 983 SS- TB cases and 4 631 734 SS + TB cases with specific sputum smear results, 284 811 cases without sputum smear test. Significantly TB clustering patterns in spatial, temporal and spatio-temporal were observed in this research. Results of the Kulldorff\'s scan found twelve significant space-time clusters of reported TB. The most likely spatio-temporal cluster (RR = 3.27, P <  0.001) was mainly located in Xinjiang Uygur Autonomous Region of western China, covering five prefectures and clustering in the time frame from September 2012 to November 2015. The spatio-temporal clustering results of SS+ TB and SS- TB also showed the most likely clusters distributed in the western China. However, the clustering time of SS+ TB was concentrated before 2010 while SS- TB was mainly concentrated after 2010.
CONCLUSIONS: This study identified the time and region of TB, SS+ TB and SS- TB clustered easily in 340 prefectures in the mainland of China, which is helpful in prioritizing resource assignment in high-risk periods and high-risk areas, and to formulate powerful strategy to prevention and control TB.

Although the incidence of tuberculosis (TB) in most parts of China are well under control now, in less developed areas such as Qinghai, TB still remains a major public health problem. This study aims to reveal the spatio-temporal patterns of TB in the Qinghai province, which could be helpful in the planning and implementing key preventative measures.
We extracted data of reported TB cases in the Qinghai province from the China Information System for Disease Control and Prevention (CISDCP) during January 2009 to December 2016. The Kulldorff\'s retrospective space-time scan statistics, calculated by using the discrete Poisson probability model, was used to identify the temporal, spatial, and spatio-temporal clusters of TB at the county level in Qinghai.
A total of 48,274 TB cases were reported from 2009 to 2016 in Qinghai. Results of the Kulldorff\'s scan revealed that the TB cases in Qinghai were significantly clustered in spatial, temporal, and spatio-temporal distribution. The most likely spatio-temporal cluster (LLR = 2547.64, RR = 4.21, P < 0.001) was mainly concentrated in the southwest of Qinghai, covering seven counties and clustered in the time frame from September 2014 to December 2016.
This study identified eight significant space-time clusters of TB in Qinghai from 2009 to 2016, which could be helpful in prioritizing resource assignment in high-risk areas for TB control and elimination in the future.

Chronic obstructive pulmonary disease (COPD) mortality has been steadily increasing in Taiwan since 2009. In order to understand where the hotspot areas are and what the local risk factors are, we integrated an ecological and a case-control study. We used a two-stage approach to identify hotspots and explore the possible risk factors for developing COPD. The first stage used the annual township COPD mortality from 2000 to 2012 and applied the retrospective space-time scan statistic to calculate the local relative risks in each township. In the second stage, we conducted a case-control study, recruiting 200 patients from one local hospital within the one identified hotspot area located in southern Taiwan. Logistic regression was applied for analyzing the personal risk factors of COPD. The univariate analyses showed that higher percentages of aborigines, patients with tuberculosis (TB) history, and those with smoking history had COPD (p < 0.05). After controlling for demographic variables, aboriginal status (adjusted odds ratios (AORs): 3.01, 95% CI: 1.52-5.93) and smoking history (AORs: 2.64, 95% CI: 1.46-4.76) were still the two significant risk factors. This two-stage approach might be beneficial to examine and cross-validate the findings from an aggregate to an individual scale, and can be easily extended to other chronic diseases.