Severe fever with thrombocytopenia syndrome (SFTS) is an emerging infectious disease in China. Polymerase chain reaction and enzyme-linked immunosorbent assay were used to detect SFTS virus (SFTSV) and Anaplasma phagocytophilum in previous clinically diagnosed human anaplasmosis patients and SFTS patients. A serosurvey for SFTSV infection was also conducted on healthy persons and animals in Yiyuan County in Shandong Province of China. Among 21 patients SFTSV was detected in 17 (81%) however A. phagocytophilum was not detected in any of the patients. The seroprevalence rate of IgG antibody to SFTSV antigens was 1.3% (1 of 78) in healthy persons, 95% (19 of 20) in goats, 50% (1 of 2) in dogs, 0% in cattle (0 of 21), and rats (Rattus norvegicus) (0 of 35). The conclusion of this study was that co-infection of SFTSV and A. phagocytophilum are rare in SFTS patients and goats might play an important role in transmission of SFTSV.

Hemorrhagic fever with renal syndrome (HFRS) is highly endemic in mainland China, where human cases account for 90 % of the total global cases. Yiyuan County is one of the most serious affected areas in China. Therefore, there is an urgent need for monitoring and predicting HFRS incidence in Yiyuan to make the control of HFRS more effective. The study was based on the reported cases of HFRS from the National Notifiable Disease Surveillance System. The demographic and spatial distributions of HFRS in Yiyuan were established. Then we fit autoregressive integrated moving average (ARIMA) models and predict the HFRS epidemic trend. There were 362 cases reported in Yiyuan during the 10-year study period. The human infections in the fall and winter reflected a seasonal characteristic pattern of Hantaan virus (HTNV) transmission. The best model was ARIMA (2, 1, 1) × (0, 1, 1)12 (AIC value 516.86) with a high validity. The ARIMA model fits the fluctuations in HFRS frequency and it can be used for future forecasting when applied to HFRS prevention and control.

Background: Hand-foot-mouth disease (HFMD) is a global public health issues, especially in China. It has threat the health of children under 5 years old. The early recognition of high-risk districts and understanding of epidemic characteristics can facilitate health sectors to prevent the occurrence of HFMD effectively.Methods: Descriptive analysis was used to summarize epidemic characteristics, and the spatial autocorrelation analysis and space-time scan analysis were utilized to explore distribution pattern of HFMD and identify hot spots with statistical significance. The result was presented in ArcMap.Results: A total of 52,095 HFMD cases were collected in Zibo city from 1 Jan 2010 to 31 Dec 2019. The annual average incidence was 129.72/100,000. The distribution of HFMD was a unimodal trend, with peak from April to September. The most susceptible age group was children under 5 years old (92.46%), and the male-to-female ratio is 1.60: 1. The main clusters were identified in Zhangdian District from 12 April 2010 to 18 September 2012. Spatial autocorrelation analysis showed that the global spatial correlation in Zibo were no statistical significance, except in 2012, 2014, 2015, 2016 and 2018. Cold spots were gathered in Boshan county and Linzi district, while hot spots only in Zhangdian District in 2018, but other years were no significance.Conclusion: Hot spots mainly concentrated in the central and surrounding city of Zibo city. We suggest that imminent public health planning and resource allocation should be focused within those areas.

The early identification and prediction of hand-foot-and-mouth disease (HFMD) play an important role in the disease prevention and control. However, suitable models are different in regions due to the differences in geography, social economy factors. We collected data associated with daily reported HFMD cases and weather factors of Zibo city in 2010 similar to 2019 and used the generalised additive model (GAM) to evaluate the effects of weather factors on HFMD cases. Then, GAM, support vectors regression (SVR) and random forest regression (RFR) models are used to compare predictive results. The annual average incidence was 129.72/100 000 from 2010 to 2019. Its distribution showed a unimodal trend, with incidence increasing from March, peaking from May to September. Our study revealed the nonlinear relationship between temperature, rainfall and relative humidity and HFMD cases and based on the predictive result, the performances of three models constructed ranked in descending order are: SVR > GAM > RFR, and SVR has the smallest prediction errors. These findings provide quantitative evidence for the prediction of HFMD for special high-risk regions and can help public health agencies implement prevention and control measures in advance.

Stroke is a major cause of mortality, particularly in low- and middle-income countries [1,2]. Ischemic stroke (IS) is a primary subtype of stroke and is mainly characterised by vascular stenosis and occlusion, typically resulting from thrombotic or embolic events [3]. According to the 2019 Global Burden of Disease (GBD 2019) study, in 204 countries and […]