Objective: To develop a spatio-temporal model of schistosomiasis japonica based on Bayesian model, and to analyze the spatio-temporal pattern of schistosomiasis, as well as to evaluate the impact of environment changes on schistosomiasis endemic.
Methods: Different Bayesian models were established by employing the data of the periodical surveillance on schistosomiasis during 1996-2005 period by taking into account of the uncertainty in sensitivity and specificity of diagnostic test, then the best fitness model was selected to analyze the spatio-temporal pattern of schistosomiasis and evaluate the impact of environment changes on schistosomiasis.
Results: The model with space-time interaction was a better fitting model. No significant temporal correlation was found in human infection rate of Schistosoma japonicum, and the difference of spatial structure between human infection rates of each year was significant. The prediction map of S. japonicum infection showed the changes of infection in the south areas of the Yuan River were not significant, while the prevalence increased significantly in the north areas of the river, which indicated that the impact of the implementation of project on partial abandon areas for water storing on prevalence of S. japonicum was stronger than that of the project on completed abandon areas for water storing.
Conclusions: It is feasible to develop the spatio-temporal model of schistosomiasis japonica based on Bayesian model, and this inetegrated Bayesian model approach may become a powerful and statistically robust tool for estimating and evaluating the control strategy.
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Neural Netw
December 2024
Department of Physics, University of Trento, Via Sommarive 14, Trento, 38123, TN, Italy.
In this study, we address the challenge of analyzing electrophysiological measurements in neuronal networks. Our computational model, based on the Reservoir Computing Network (RCN) architecture, deciphers spatio-temporal data obtained from electrophysiological measurements of neuronal cultures. By reconstructing the network structure on a macroscopic scale, we reveal the connectivity between neuronal units.
View Article and Find Full Text PDFJ Environ Manage
December 2024
School of Design, Shanghai Jiao Tong University, Shanghai, 200240, China. Electronic address:
This study delves into the multi-scale temporal and spatial variations of soil heat flux (G) within riparian zones and its correlation with net radiation (Rn) across six riparian woodlands in Shanghai, each characterized by distinct vegetation types. The objective is to assess the complex interrelations between G and Rn, and how these relationships are influenced by varying vegetation and seasons. Over the course of a year, data on G and Rn is collected to investigate their dynamics.
View Article and Find Full Text PDFSci Rep
December 2024
Henan University of Engineering, Zhengzhou, 451191, China.
Social media generates vast amounts of spatio-temporal sequential data. However, current methods often ignore the complex spatio-temporal correlations within these data. This oversight makes it difficult to fully capture the dynamic features of the data.
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December 2024
School of Geography, Liaoning Normal University, Dalian, 116029, China.
Sustainable development is a hot topic of global concern and sustainable human settlements (HS) are crucial to people's happiness. Thus, strengthening the construction of HS will help enrich human settlements geography with theories of HS interactions, clarify the existing problems of the Chengdu-Chongqing urban agglomeration (CC), promote the harmonization of the human-land relationship, and realize the SDGs. The results were as follows.
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December 2024
Mahidol-Oxford Tropical Medicine Research Unit, Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand.
Dengue fever poses a significant public health burden in tropical regions, including Thailand, where periodic epidemics strain healthcare resources. Effective disease surveillance is essential for timely intervention and resource allocation. Various methods exist for spatiotemporal cluster detection, but their comparative performance remains unclear.
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