Identifying Malaria Hotspots Regions in Ghana Using Bayesian Spatial and Spatiotemporal Models

Background:Malaria remains a significant public health concern in Ghana,with varying risk levels across different geographical areas.Malaria affects millions of people each year and imposes a substantial burden on the health care system and population.Accurate risk estimation and mapping are crucial for effectively allocating resources and implementing targeted interventions to identify regions with disease hotspots.This study aimed to identify regions exhibiting elevated malaria risk so that public health interventions can be implemented,and to identify malaria risk predictors that can be controlled as part of public health interventions for malaria control.Methods:The data on laboratory-confirmed malaria cases from 2015 to 2021 were obtained from the Ghana Health Service and Ghana Statistical Service.We studied the spatial and spatiotemporal patterns of the relative risk of malaria using Bayesian spatial and spatiotemporal models.The malaria risk for each region was mapped to visually identify regions with malaria hotspots.Clustering and heterogeneity of disease risks were established using correlated and uncorrelated structures via the conditional autoregressive and Gaussian models,respectively.Parameter estimates from the marginal posterior distribution were estimated within the Integrated Nested Laplace Approximation using the R software.Results:The spatial model indicated an increased risk of malaria in the North East,Bono East,Ahafo,Central,Upper West,Brong Ahafo,Ashanti,and Eastern regions.The spatiotemporal model results highlighted an elevated malaria risk in the North East,Upper West,Upper East,Savannah,Bono East,Central,Bono,and Ahafo regions.Both spatial and spatiotemporal models identified the North East,Upper West,Bono East,Central,and Ahafo Regions as hotspots for malaria risk.Substantial variations in risk were evident across regions(H=104.9,P<0.001).Although climatic and economic factors influenced malaria infection,statistical significance was not established.Conclusions:Malaria risk was clustered and varied among regions in Ghana.There are many regions in Ghana that are hotspots for malaria risk,and climate and economic factors have no significant influence on malaria risk.This study could provide information on malaria transmission patterns in Ghana,and contribute to enhance the effectiveness of malaria control strategies.

Spatiotemporal distribution model for zinc electrowinning process and its parameter estimation

This paper focuses on the distributed parameter modeling of the zinc electrowinning process(ZEWP)to reveal the spatiotemporal distribution of concentration of zinc ions(CZI)and sulfuric acid(CSA)in the electrolyte.Considering the inverse diffusion of such ions in the electrolyte,the dynamic distribution of ions is described by the axial dispersion model.A parameter estimation strategy based on orthogonal approximation has been proposed to estimate the unknown parameters in the process model.Different industrial data sets are used to test the effectiveness of the spatiotemporal distribution model and the proposed parameter estimation approach.The results demonstrate that the analytical model can effectively capture the trends of the electrolysis reaction in time and thus has the potential to implement further optimization and control in the ZEWP.

Examining spatiotemporal distribution and CPUE-environment relationships for the jumbo flying squid Dosidicus gigas offshore Peru based on spatial autoregressive model

The spatiotemporal distribution and relationship between nominal catch-per-unit-ef fort(CPUE) and environment for the jumbo flying squid( Dosidicus gigas) were examined in of fshore Peruvian waters during 2009–2013. Three typical oceanographic factors aff ecting the squid habitat were investigated in this research, including sea surface temperature(SST), sea surface salinity(SSS) and sea surface height(SSH). We studied the CPUE-environment relationships for D. gigas using a spatially-lagged version of spatial autoregressive(SAR) model and a generalized additive model(GAM), with the latter for auxiliary and comparative purposes. The annual fishery centroids were distributed broadly in an area bounded by 79.5°–82.7°W and 11.9°–17.1°S, while the monthly fishery centroids were spatially close and lay in a smaller area bounded by 81.0°–81.2°W and 14.3°–15.4°S. Our results show that the preferred environmental ranges for D. gigas offshore Peru were 20.9°–21.9°C for SST, 35.16–35.32 for SSS and 27.2–31.5 cm for SSH in the areas bounded by 78°–80°W/82–84°W and 15°–18°S. Monthly spatial distributions during October to December were predicted using the calibrated GAM and SAR models and general similarities were found between the observed and predicted patterns for the nominal CPUE of D. gigas. The overall accuracies for the hotspots generated by the SAR model were much higher than those produced by the GAM model for all three months. Our results contribute to a better understanding of the spatiotemporal distributions of D. gigas off shore Peru, and off er a new SAR modeling method for advancing fishery science.

The non-stationary and spatially varying associations between hand,foot and mouth disease and multiple environmental factors:A Bayesian spatiotemporal mapping model study

The transmission and prevalence of Hand,Foot and Mouth Disease(HFMD)are affected by a variety of natural and socio-economic environmental factors.This study aims to quantitatively investigate the non-stationary and spatially varying associations between various environmental factors and HFMD risk.We collected HFMD surveillance cases and a series of relevant environmental data from 2013 to 2021 in Xi'an,Northwest China.By controlling the spatial and temporal mixture effects of HFMD,we constructed a Bayesian spatiotemporal mapping model and characterized the impacts of different driving factors into global linear,non-stationary and spatially varying effects.The results showed that the impact of meteorological conditions on HFMD risk varies in both type and magnitude above certain thresholds(temperature:30°C,precipitation:70 mm,solar radiation:13000 kJ/m^(2),pressure:945 hPa,humidity:69%).Air pollutants(PM_(2.5),PM_(10),NO_(2))showed an inverted U-shaped relationship with the risk of HFMD,while other air pollutants(O_(3),SO_(2))showed nonlinear fluctuations.Moreover,the driving effect of increasing temperature on HFMD was significant in the 3-year period,while the inhibitory effect of increasing precipitation appeared evident in the 5-year period.In addition,the proportion of urban/suburban/rural area had a strong influence on HFMD,indicating that the incidence of HFMD firstly increased and then decreased during the rapid urbanization process.The influence of population density on HFMD was not only limited by spatial location,but also varied between high and low intervals.Higher road density inhibited the risk of HFMD,but higher night light index promoted the occurrence of HFMD.Our findings further demonstrated that both ecological and socioeconomic environmental factors can pose multiple driving effects on increasing the spatiotemporal risk of HFMD,which is of great significance for effectively responding to the changes in HFMD epidemic outbreaks.

Mesoscale oceanic eddies in the South China Sea from 1992 to 2012:evolution processes and statistical analysis

Automated identification and tracking of mesoscale ocean eddies has recently become one research hotspot in physical oceanography. Several methods have been developed and applied to survey the general kinetic and geometric characteristics of the ocean eddies in the South China Sea（SCS）. However, very few studies attempt to examine eddies＇ internal evolution processes. In this study, we reported a hybrid method to trace eddies＇ propagation in the SCS based on their internal structures, which are characterized by eddy centers, footprint borders, and composite borders. Eddy identification and tracking results were represented by a GIS-based spatiotemporal model. Information on instant states, dynamic evolution processes, and events of disappearance, reappearance, split, and mergence is stored in a GIS database. Results were validated by comparing against the ten Dongsha Cyclonic Eddies（DCEs） and the three long-lived anticyclonic eddies（ACEs） in the northern SCS, which were reported in previous literature. Our study confirmed the development of these eddies. Furthermore, we found more DCE-like and ACE-like eddies in these areas from 2005 to 2012 in our database. Spatial distribution analysis of disappearing, reappearing, splitting, and merging activities shows that eddies in the SCS tend to cluster to the northwest of Luzon Island, southwest of Luzon Strait, and around the marginal sea of Vietnam. Kuroshio intrusions and the complex sea floor topography in these areas are the possible factors that lead to these spatial clusters.

Bayesian Estimation and Model Selection for the Spatiotemporal Autoregressive Model with Autoregressive Conditional Heteroscedasticity Errors

The spatial and spatiotemporal autoregressive conditional heteroscedasticity(STARCH) models receive increasing attention. In this paper, we introduce a spatiotemporal autoregressive(STAR) model with STARCH errors, which can capture the spatiotemporal dependence in mean and variance simultaneously. The Bayesian estimation and model selection are considered for our model. By Monte Carlo simulations, it is shown that the Bayesian estimator performs better than the corresponding maximum-likelihood estimator, and the Bayesian model selection can select out the true model in most times. Finally, two empirical examples are given to illustrate the superiority of our models in fitting those data.

Voxel modeling and association of ubiquitous spatiotemporal information in natural language texts

The ubiquitous spatiotemporal information extracted from Internet texts limits its application in spatiotemporal association and analysis due to its unstructured nature and uncertainty.This study uses ST-Voxel modeling to solve the problem of structured modeling and the association of ubiquitous spatiotemporal information in natural language texts.It provides a new solution for associating ubiquitous spatiotemporal information on the Internet and discovering public opinion.The main contributions of this paper include:(1)It proposes a convolved method for ST-Voxel,which solves the voxel modeling problem of unstructured and uncertain spatiotemporal objects and spatiotemporal relation in natural language texts.Experiments show that this method can effectively model 5 types of spatiotemporal objects and 16 types of uncertain spatiotemporal relation founded in texts;(2)It realizes the unknown event discovery based on voxelized spatiotemporal information association.Experiments show that this method can effectively solve the aggregation of ubiquitous spatiotemporal information in multi-natural language texts,which is conducive to discovering spatiotemporal events.The selection of convolution parameters in voxel modeling is also discussed.A parameter selection method for balancing the discovery capability and discovery accuracy of spatiotemporal events is given.

A predictive model for ethylene-mediated auxin and cytokinin patterning in the Arabidopsis root

The interaction between auxin and cytokinin is important in many aspects of plant development.Experimental measurements of both auxin and cytokinin concentration and reporter gene expression clearly show the coexistence of auxin and cytokinin concentration patterning in Arabidopsis root development.However,in the context of crosstalk among auxin,cytokinin,and ethylene,little is known about how auxin and cytokinin concentration patterns simultaneously emerge and how they regulate each other in the Arabidopsis root.This work utilizes a wide range of experimental observations to propose a mechanism for simultaneous patterning of auxin and cytokinin concentrations.In addition to revealing the regulatory relationships between auxin and cytokinin,this mechanism shows that ethylene signaling is an important factor in achieving simultaneous auxin and cytokinin patterning,while also predicting other experimental observations.Combining the mechanism with a realistic in silico root model reproduces experimental observations of both auxin and cytokinin patterning.Predictions made by the mechanism can be compared with a variety of experimental observations,including those obtained by our group and other independent experiments reported by other groups.Examples of these predictions include patterning of auxin biosynthesis rate,changes in PIN1 and PIN2 patterns in pin3,4,7 mutants,changes in cytokinin patterning in the pls mutant,PLS patterning,and various trends in different mutants.This research reveals a plausible mechanism for simultaneous patterning of auxin and cytokinin concentrations in Arabidopsis root development and suggests a key role for ethylene pattern integration.

Video Copy Detection Based on Spatiotemporal Fusion Model

Content-based video copy detection is an active research field due to the need for copyright pro- tection and business intellectual property protection. This paper gives a probabilistic spatiotemporal fusion approach for video copy detection. This approach directly estimates the location of the copy segment with a probabilistic graphical model. The spatial and temporal consistency of the video copy is embedded in the local probability function. An effective local descriptor and a two-level descriptor pairing method are used to build a video copy detection system to evaluate the approach. Tests show that it outperforms the popular voting algorithm and the probabilistic fusion framework based on the Hidden Markov Model, improving F-score （F1） by 8%.

Air Pollution Prediction Via Graph Attention Network and Gated Recurrent Unit

PM2.5 concentration prediction is of great significance to environmental protection and human health.Achieving accurate prediction of PM2.5 concentration has become an important research task.However,PM2.5 pollutants can spread in the earth’s atmosphere,causing mutual influence between different cities.To effectively capture the air pollution relationship between cities,this paper proposes a novel spatiotemporal model combining graph attention neural network(GAT)and gated recurrent unit(GRU),named GAT-GRU for PM2.5 concentration prediction.Specifically,GAT is used to learn the spatial dependence of PM2.5 concentration data in different cities,and GRU is to extract the temporal dependence of the long-term data series.The proposed model integrates the learned spatio-temporal dependencies to capture long-term complex spatio-temporal features.Considering that air pollution is related to the meteorological conditions of the city,the knowledge acquired from meteorological data is used in the model to enhance PM2.5 prediction performance.The input of the GAT-GRU model consists of PM2.5 concentration data and meteorological data.In order to verify the effectiveness of the proposed GAT-GRU prediction model,this paper designs experiments on real-world datasets compared with other baselines.Experimental results prove that our model achieves excellent performance in PM2.5 concentration prediction.

Ensemble Based Learning with Accurate Motion Contrast Detection

Recent developments in computer vision applications have enabled detection of significant visual objects in video streams.Studies quoted in literature have detected objects from video streams using Spatiotemporal Particle Swarm Optimization(SPSOM)and Incremental Deep Convolution Neural Networks(IDCNN)for detecting multiple objects.However,the study considered opticalflows resulting in assessing motion contrasts.Existing methods have issue with accuracy and error rates in motion contrast detection.Hence,the overall object detection performance is reduced significantly.Thus,consideration of object motions in videos efficiently is a critical issue to be solved.To overcome the above mentioned problems,this research work proposes a method involving ensemble approaches to and detect objects efficiently from video streams.This work uses a system modeled on swarm optimization and ensemble learning called Spatiotemporal Glowworm Swarm Optimization Model(SGSOM)for detecting multiple significant objects.A steady quality in motion contrasts is maintained in this work by using Chebyshev distance matrix.The proposed system achieves global optimization in its multiple object detection by exploiting spatial/temporal cues and local constraints.Its experimental results show that the proposed system scores 4.8%in Mean Absolute Error(MAE)while achieving 86%in accuracy,81.5%in precision,85%in recall and 81.6%in F-measure and thus proving its utility in detecting multiple objects.

Crosstalk Complexities between Auxin, Cytokinin, and Ethylene in Arabidopsis Root Development： From Experiments to Systems Modeling, and Back Again

Understanding how hormones and genes interact to coordinate plant growth in a changing environment is a major challenge in plant developmental biology. Auxin, cytokinin, and ethylene are three important hormones that regulate many aspects of plant development. This review critically evaluates the crosstalk between the three hormones in Arabidopsis root development. We integrate a variety of experimental data into a crosstalk network, which reveals multiple layers of complexity in auxin, cytokinin, and ethylene crosstalk. In particular, data integration reveals an additional, largely overlooked link between the ethylene and cytokinin pathways, which acts through a phosphorelay mechanism. This proposed link addresses outstanding questions on whether ethylene application promotes or inhibits receptor kinase activity of the ethylene receptors. Elucidating the complexity in auxin, cytokinin, and ethylene crosstalk requires a combined experimental and systems modeling approach. We evaluate important modeling efforts for establishing how crosstalk between auxin, cytokinin, and ethylene regulates patterning in root develop- ment. We discuss how a novel methodology that iteratively combines experiments with systems modeling analysis is essential for elucidating the complexity in crosstalk of auxin, cytokinin, and ethylene in root development. Finally, we discuss the future challenges from a combined experimental and modeling perspective.

The nonparametric estimation of long memory spatio-temporal random field models

This paper considers the local linear estimation of a multivariate regression function and its derivatives for a stationary long memory(long range dependent) nonparametric spatio-temporal regression model.Under some mild regularity assumptions, the pointwise strong convergence, the uniform weak consistency with convergence rates and the joint asymptotic distribution of the estimators are established. A simulation study is carried out to illustrate the performance of the proposed estimators.

Spatiotemporal analysis of the impact of urban landscape forms on PM_(2.5) in China from 2001 to 2020

Urban landscape forms can be effective in reducing increasing PM_(2.5) concentrations due to urbanization in China,making it crucially important to accurately quantify the spatiotemporal impact of urban landscape forms on PM_(2.5) variations.Three landscape indices and six control variables were selected to assess these impacts in 362 Chinese cities during different time scales from 2001 to 2020,using a spatiotemporal geographically weighted regression model,random forest models and partial dependence plots.The results show that there are spatiotemporal differences in the impacts of landscape indices on PM_(2.5).the proportion of urban green infrastructure(PLAND-UGI)and the fractal dimension of urban green infrastructure(FRACT-UGI)exacerbate PM_(2.5) concentrations in the northwest,the proportion of impervious surfaces(PLAND-Impervious)mitigates air pollution in northwest and southwest China,and shannon’s diversity index(SHDI)has seasonal differences in the northwest.PLAND-UGI is the landscape index with the largest contribution(30%)and interpretable range.The relationship between FRACT and PM_(2.5) was more complex than for other landscape indices.The results of this study contribute to a deeper understanding of the spatial and temporal differences in the impact of urban landscape patterns on PM_(2.5),contributing to clean urban development and sustainable development.

Spatiotemporal emotion recognition based on 3D time-frequency domain feature matrix

The research of emotion recognition based on electroencephalogram(EEG)signals often ignores the related information between the brain electrode channels and the contextual emotional information existing in EEG signals,which may contain important characteristics related to emotional states.Aiming at the above defects,a spatiotemporal emotion recognition method based on a 3-dimensional(3 D)time-frequency domain feature matrix was proposed.Specifically,the extracted time-frequency domain EEG features are first expressed as a 3 D matrix format according to the actual position of the cerebral cortex.Then,the input 3 D matrix is processed successively by multivariate convolutional neural network(MVCNN)and long short-term memory(LSTM)to classify the emotional state.Spatiotemporal emotion recognition method is evaluated on the DEAP data set,and achieved accuracy of 87.58%and 88.50%on arousal and valence dimensions respectively in binary classification tasks,as well as obtained accuracy of 84.58%in four class classification tasks.The experimental results show that 3 D matrix representation can represent emotional information more reasonably than two-dimensional(2 D).In addition,MVCNN and LSTM can utilize the spatial information of the electrode channels and the temporal context information of the EEG signal respectively.

Policy spillover and regional linkage characteristics of the real estate market in China's urban agglomerations

The main purpose of this study is to determine the spillover effect of real estate regulatory policies released by core cities on the surrounding cities in major urban agglomerations based on regional linkage characteristics of China's real estate market.In this study,real estate transaction data of 157 cities were selected from 11 major urban agglomerations.Agglomeration's housing transaction volatility and spillover effect caused by the core city's regulatory policies were simulated by integrating spatial and temporal analysis model,event analysis,and symbolic time series analysis.The findings showed that(1)the regional linkage of the real estate market in the Harbin-Changchun and Middle-South Liaoning,Middle Reaches of the Yangtze River,Yangtze River Delta,Pearl River Delta,and West Side of the Straits agglomerations were remarkably tight and the core cities'policy spillover effect was significant,of which the house purchase limitation and credit limitation policies had the widest influence;(2)the regional linkage of the real estate market in the Beijing-Tianjin-Hebei agglomeration,Shandong Peninsula,Guanzhong Plain,and Chengdu-Chongqing agglomerations was relatively weaker,but the core cities'policies of mar-ket regulation and taxation had certain spillover effect;(3)there were significant differ-ences in the spillover effects of different types of policies in different urban agglomerations;(4)generally,the core cities'policy spillover often reduced the changing characteristics of the real estate market and made it more ordered with more certainty in the whole agglomeration,with the exception of the Beijing-Tianjin-Hebei,West Side of the Straits,and Chengdu-Chongqing agglomerations.

Explicating the mechanisms of land cover change in the New Eurasian Continental Bridge Economic Corridor region in the 21st century

Land cover change has presented clear spatial differences in the New Eurasian Continental Bridge Economic Corridor(NECBEC)region in the 21 st century.A spatiotemporal dynamic probability model and a driving force analysis model of land cover change were developed to analyze explicitly the dynamics and driving forces of land cover change in the NECBEC region.The results show that the areas of grassland,cropland and built-up land increased by 114.57 million ha,8.41 million ha and 3.96 million ha,and the areas of woodland,other land,and water bodies and wetlands decreased by 74.09 million ha,6.26 million ha,and 46.59 million ha in the NECBEC region between 2001 and 2017,respectively.Woodland and other land were mainly transformed to grassland,and grassland was mainly transformed to woodland and cropland.Built-up land had the largest annual rate of increase and 50%of this originated from cropland.Moreover,since the Belt and Road Initiative(BRI)commenced in 2013,there has been a greater change in the dynamics of land cover change,and the gaps in the socio-economic development level have gradually decreased.The index of socio-economic development was the highest in western Europe,and the lowest in northern Central Asia.The impacts of socio-economic development on cropland and built-up land were greater than those for other land cover types.In general,in the context of rapid socio-economic development,the rate of land cover change in the NECBEC has clearly shown an accelerating trend since 2001,especially after the launch of the BRI in 2013.