Factors Associated With Severe Fever With Thrombocytopenia Syndrome in Endemic Areas of China

Development and Comparison of Time Series Models in Predicting Severe Fever with Thrombocytopenia Syndrome Cases - Hubei Province, China, 2013-2020

INTRODUCTION

Severe fever with thrombocytopenia syndrome (SFTS) is an emerging infectious disease caused by the SFTS virus, which has a high mortality rate. Predicting the number of SFTS cases is essential for early outbreak warning and can offer valuable insights for establishing prevention and control measures.

METHODS

In this study, data on monthly SFTS cases in Hubei Province, China, from 2013 to 2020 were collected. Various time series models based on seasonal auto-regressive integrated moving average (SARIMA), Prophet, eXtreme Gradient Boosting (XGBoost), and long short-term memory (LSTM) were developed using these historical data to predict SFTS cases. The established models were evaluated and compared using mean absolute error (MAE) and root mean squared error (RMSE).

RESULTS

Four models were developed and performed well in predicting the trend of SFTS cases. The XGBoost model outperformed the others, yielding the closest fit to the actual case numbers and exhibiting the smallest MAE (2.54) and RMSE (2.89) in capturing the seasonal trend and predicting the monthly number of SFTS cases in Hubei Province.

CONCLUSION

The developed XGBoost model represents a promising and valuable tool for SFTS prediction and early warning in Hubei Province, China.

The classification, origin, and evolutionary dynamics of severe fever with thrombocytopenia syndrome virus circulating in East Asia

The classification of severe fever with thrombocytopenia syndrome virus (SFTSV) lacked consistency due to limited virus sequences used across previous studies, and the origin and transmission dynamics of the SFTSV remains not fully understood. In this study, we analyzed the diversity and phylodynamics of SFTSV using the most comprehensive and largest dataset publicly available for a better understanding of SFTSV classification and transmission. A total of 1267 L segments, 1289 M segments, and 1438 S segments collected from China, South Korea, and Japan were included in this study. Maximum likelihood trees were reconstructed to classify the lineages. Discrete phylogeographic analysis was conducted to infer the phylodynamics of SFTSV. We found that the L, M, and S segments were highly conserved, with mean pairwise nucleotide distances of 2.80, 3.36, and 3.35% and could be separated into 16, 13, and 15 lineages, respectively. The evolutionary rate for L, M, and the S segment was 0.61 × 10-4 (95% HPD: 0.48-0.73 × 10-4), 1.31 × 10-4 (95% HPD: 0.77-1.77 × 10-4) and 1.27 × 10-4 (95% HPD: 0.65-1.85 × 10-4) subs/site/year. The SFTSV most likely originated from South Korea around the year of 1617.6 (95% HPD: 1513.1-1724.3), 1700.4 (95% HPD: 1493.7-1814.0), and 1790.1 (95% HPD: 1605.4-1887.2) for L, M, and S segments, respectively. Hubei Province in China played a critical role in the geographical expansion of the SFTSV. The effective population size of SFTSV peaked around 2010 to 2013. We also identified several codons under positive selection in the RdRp, Gn-Gc, and NS genes. By leveraging the largest dataset of SFTSV, our analysis could provide new insights into the evolution and dispersal of SFTSV, which may be beneficial for the control and prevention of severe fever with thrombocytopenia syndrome.

Spatial and Temporal Analysis of Severe Fever with Thrombocytopenia Syndrome in Anhui Province from 2011 to 2023

OBJECTIVE

To analyze the spatial autocorrelation and spatiotemporal clustering characteristics of severe fever with thrombocytopenia syndrome(SFTS) in Anhui Province from 2011 to 2023.

METHODS

Data of SFTS in Anhui Province from 2011 to 2023 were collected. Spatial autocorrelation analysis was conducted using GeoDa software, while spatiotemporal scanning was performed using SaTScan 10.0.1 software to identify significant spatiotemporal clusters of SFTS.

RESULTS

From 2011 to 2023, 5720 SFTS cases were reported in Anhui Province, with an average annual incidence rate of 0.7131/100,000. The incidence of SFTS in Anhui Province reached its peak mainly from April to May, with a small peak in October. The spatial autocorrelation results showed that from 2011 to 2023, there was a spatial positive correlation(P < 0.05) in the incidence of SFTS in all counties and districts of Anhui Province. Local autocorrelation high-high clustering areas are mainly located in the south of the Huaihe River. The spatiotemporal scanning results show three main clusters of SFTS in recent years: the first cluster located in the lower reaches of the Yangtze River, the eastern region of Anhui Province; the second cluster primarily focused on the region of the Dabie Mountain range, while the third cluster primarily focused on the region of the Huang Mountain range.

CONCLUSIONS

The incidence of SFTS in Anhui Province in 2011-2023 was spatially clustered.

Long-term effects of meteorological factors on severe fever with thrombocytopenia syndrome incidence in eastern China from 2014 to 2020: An ecological time-series study

BACKGROUND

Severe fever with thrombocytopenia syndrome (SFTS) is an emerging tick-borne disease with susceptibility influenced by meteorological factors. However, there is limited understanding of the delayed and interactive impacts of meteorological factors on SFTS incidence.

METHODS

Daily incidence data of SFTS and corresponding meteorological factors for the Jiaodong Peninsula in northeast China were collected from January 1, 2014, to December 31, 2020. Random forest regression model, based on custom search, was performed to compare the importance of meteorological factors. Generalized additive model with quasi-Poisson regression was conducted to examine the nonlinear relationships and interactive effects using penalized spline methods. A distributed lag nonlinear model with quasi-Poisson regression was constructed to estimate exposure-lag effects of meteorological factors.

RESULTS

The most important meteorological factor was weekly mean lowest temperature. The relationship between meteorological factors and SFTS incidence revealed a nonlinear and intricate pattern. Interaction analyses showed that prolonged sunshine duration posed a climatic risk within a specific temperature range for SFTS incidence. The maximum relative risk (RR) observed under extremely low temperature (-4°C) was 1.33 at lag of 15 week, while under extremely high temperature (25°C), the minimum RR was 0.65 at lag of 13 week. The RRs associated with both extremely high and low sunshine duration escalated with an increase in lag weeks.

CONCLUSIONS

This study underscores that meteorological factors exert nonlinear, delayed, and interactive effects on SFTS incidence. These findings highlight the importance of understanding the dependency of SFTS incidence on meteorological factors in particular climates.

Time series models in prediction of severe fever with thrombocytopenia syndrome cases in Shandong province, China

Severe fever with thrombocytopenia syndrome (SFTS) is an emerging infectious disease caused by the SFTS virus (SFTSV). Predicting the incidence of this disease in advance is crucial for policymakers to develop prevention and control strategies. In this study, we utilized historical incidence data of SFTS (2013-2020) in Shandong Province, China to establish three univariate prediction models based on two time-series forecasting algorithms Autoregressive Integrated Moving Average (ARIMA) and Prophet, as well as a special type of recurrent neural network Long Short-Term Memory (LSTM) algorithm. We then evaluated and compared the performance of these models. All three models demonstrated good predictive capabilities for SFTS cases, with the predicted results closely aligning with the actual cases. Among the models, the LSTM model exhibited the best fitting and prediction performance. It achieved the lowest values for mean absolute error (MAE), mean square error (MSE), and root mean square error (RMSE). The number of SFTS cases in the subsequent 5 years in this area were also generated using this model. The LSTM model, being simple and practical, provides valuable information and data for assessing the potential risk of SFTS in advance. This information is crucial for the development of early warning systems and the formulation of effective prevention and control measures for SFTS.

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

BACKGROUND

Severe 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.

METHODS

Data 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.

RESULTS

From 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.

CONCLUSIONS

The 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.

Analysis of the spatial-temporal components driving transmission of the severe fever with thrombocytopenia syndrome in Shandong Province, China, 2016-2018

Existing models about the spatial-temporal distribution of the severe fever with thrombocytopenia syndrome (SFTS) entirely concentrate on aggregation, which provides limited knowledge to develop effective measures to control the epidemic of SFTS. This study aimed to identify the main spatial-temporal components and heterogeneity in different regions in Shandong Province, China. We applied the spatial-temporal multicomponent model to detect the spatial-temporal component values. A total of 2814 cases were reported from 2016 to 2018 in Shandong Province. The prevalence rate was 0.627 per 100,000, with an overall case fatality rate of 8.99%. SFTS cases were mostly clustered in central and eastern regions of Shandong Province. The total effect values of the autoregressive component, the spatiotemporal component and the endemic component were 0.586, 0.244 and 0.084, respectively, which demonstrated that the autoregressive component was the main factor driving the incidence of SFTS, followed by the spatiotemporal component. Gross domestic product per capita and weekly mean atmospheric pressure contributed to the incidence of SFTS with inverse effects. Obvious heterogeneity across regions for the autoregressive component and the spatiotemporal component was identified. In conclusion, the autoregressive and spatiotemporal components play a key role in driving the transmission of SFTS in Shandong Province. Based on the main component values, targeted measures should be formulated to control SFTS epidemics in different regions.