Malaria control can significantly benefit from a holistic and precise way of quantitatively measuring the transmission intensity,which needs to incorporate spatiotemporally varying risk factors.In this study,we conduc...Malaria control can significantly benefit from a holistic and precise way of quantitatively measuring the transmission intensity,which needs to incorporate spatiotemporally varying risk factors.In this study,we conduct a systematic investigation to characterize malaria transmission intensity by taking a spatiotemporal network perspective,where nodes capture the local transmission intensities resulting from dominant vector species,the population density,and land cover,and edges describe the cross-region human mobility patterns.The inferred network enables us to accurately assess the transmission intensity over time and space from available empirical observations.Our study focuses on malaria-severe districts in Cambodia.The malaria transmission intensities determined using our transmission network reveal both qualitatively and quantitatively their seasonal and geographical characteristics:the risks increase in the rainy season and decrease in the dry season;remote and sparsely populated areas generally show higher transmission intensities than other areas.Our findings suggest that:the human mobility(e.g.,in planting/harvest seasons),environment(e.g.,temperature),and contact risk(coexistences of human and vector occurrence)contribute to malaria transmission in spatiotemporally varying degrees;quantitative relationships between these influential factors and the resulting malaria transmission risk can inform evidence-based tailor-made responses at the right locations and times.展开更多
Spatio-temporal heterogeneous data is the database for decisionmaking in many fields,and checking its accuracy can provide data support for making decisions.Due to the randomness,complexity,global and local correlatio...Spatio-temporal heterogeneous data is the database for decisionmaking in many fields,and checking its accuracy can provide data support for making decisions.Due to the randomness,complexity,global and local correlation of spatiotemporal heterogeneous data in the temporal and spatial dimensions,traditional detection methods can not guarantee both detection speed and accuracy.Therefore,this article proposes a method for detecting the accuracy of spatiotemporal heterogeneous data by fusing graph convolution and temporal convolution networks.Firstly,the geographic weighting function is introduced and improved to quantify the degree of association between nodes and calculate the weighted adjacency value to simplify the complex topology.Secondly,design spatiotemporal convolutional units based on graph convolutional neural networks and temporal convolutional networks to improve detection speed and accuracy.Finally,the proposed method is compared with three methods,ARIMA,T-GCN,and STGCN,in real scenarios to verify its effectiveness in terms of detection speed,detection accuracy and stability.The experimental results show that the RMSE,MAE,and MAPE of this method are the smallest in the cases of simple connectivity and complex connectivity degree,which are 13.82/12.08,2.77/2.41,and 16.70/14.73,respectively.Also,it detects the shortest time of 672.31/887.36,respectively.In addition,the evaluation results are the same under different time periods of processing and complex topology environment,which indicates that the detection accuracy of this method is the highest and has good research value and application prospects.展开更多
基金funded by the Ministry of Science and Technology of China(2021ZD0112501/2021ZD0112502)the HKSAR Research Grants Council(12201318/12201619/12202220)the HKBU/CSD Departmental Start-up Fund for New Assistant Professors.
文摘Malaria control can significantly benefit from a holistic and precise way of quantitatively measuring the transmission intensity,which needs to incorporate spatiotemporally varying risk factors.In this study,we conduct a systematic investigation to characterize malaria transmission intensity by taking a spatiotemporal network perspective,where nodes capture the local transmission intensities resulting from dominant vector species,the population density,and land cover,and edges describe the cross-region human mobility patterns.The inferred network enables us to accurately assess the transmission intensity over time and space from available empirical observations.Our study focuses on malaria-severe districts in Cambodia.The malaria transmission intensities determined using our transmission network reveal both qualitatively and quantitatively their seasonal and geographical characteristics:the risks increase in the rainy season and decrease in the dry season;remote and sparsely populated areas generally show higher transmission intensities than other areas.Our findings suggest that:the human mobility(e.g.,in planting/harvest seasons),environment(e.g.,temperature),and contact risk(coexistences of human and vector occurrence)contribute to malaria transmission in spatiotemporally varying degrees;quantitative relationships between these influential factors and the resulting malaria transmission risk can inform evidence-based tailor-made responses at the right locations and times.
基金supported by the National Natural Science Foundation of China under Grants 42172161by the Heilongjiang Provincial Natural Science Foundation of China under Grant LH2020F003+2 种基金by the Heilongjiang Provincial Department of Education Project of China under Grants UNPYSCT-2020144by the Innovation Guidance Fund of Heilongjiang Province of China under Grants 15071202202by the Science and Technology Bureau Project of Qinhuangdao Province of China under Grants 202101A226.
文摘Spatio-temporal heterogeneous data is the database for decisionmaking in many fields,and checking its accuracy can provide data support for making decisions.Due to the randomness,complexity,global and local correlation of spatiotemporal heterogeneous data in the temporal and spatial dimensions,traditional detection methods can not guarantee both detection speed and accuracy.Therefore,this article proposes a method for detecting the accuracy of spatiotemporal heterogeneous data by fusing graph convolution and temporal convolution networks.Firstly,the geographic weighting function is introduced and improved to quantify the degree of association between nodes and calculate the weighted adjacency value to simplify the complex topology.Secondly,design spatiotemporal convolutional units based on graph convolutional neural networks and temporal convolutional networks to improve detection speed and accuracy.Finally,the proposed method is compared with three methods,ARIMA,T-GCN,and STGCN,in real scenarios to verify its effectiveness in terms of detection speed,detection accuracy and stability.The experimental results show that the RMSE,MAE,and MAPE of this method are the smallest in the cases of simple connectivity and complex connectivity degree,which are 13.82/12.08,2.77/2.41,and 16.70/14.73,respectively.Also,it detects the shortest time of 672.31/887.36,respectively.In addition,the evaluation results are the same under different time periods of processing and complex topology environment,which indicates that the detection accuracy of this method is the highest and has good research value and application prospects.
