scholarly journals A hybrid SIR model applied to “Covid- 19” pandemic

2020 ◽  
Author(s):  
samuel Ismael BILLONG IV ◽  
Georges Edouard Kouamou ◽  
Thomas Bouetou
Keyword(s):  
Prediction Models ◽  
Temporal Dynamics ◽  
Equation System ◽  
Sir Model ◽  
Diffusion Equations ◽  
Statistical Prediction ◽  
Accurate Information ◽  
Test Case ◽  
Developed Country ◽  
Spatio Temporal

Abstract Modeling has become a tool capable of guiding public policies, especially in the area of health. Specifically, modeling in epidemiology makes it possible to follow the evolution of infections and to understand the behavior of viruses. Unfortunately, the traditional SIR models and the statistical prediction models commonly used suffer from the lack of accurate information and the unavailability of a large amount of data, also they do not take into account the interactions within the population. This paper proposes a hybrid SIR model which takes into consideration the spatio-temporal dynamics of individuals. The model is based on the discrete stochastic diffusion equations. To build the equation system, the 2D diffusion equations are coupled to the human displacement probability law pattern through a discretization made by the finite volume method for complex geometries. Beyond the health consequences it causes, COVID-19 (coronavirus) is a test case used to validate the proposed model. We used the case of a developed country before confinement to fit to the chosen displacement pattern, and to analyze the sensitivity of the parameters of the model taking into account the accuracy of the statistics provided.

scholarly journals A Spatial-Temporal Feature Extraction and Unsupervised Recognition of Video Anomalies in Real Time

2021 ◽  
pp. 66-72
Keyword(s):  
Anomaly Detection ◽  
Real Time ◽  
Prediction Models ◽  
Temporal Dynamics ◽  
Ground Truth ◽  
Reconstruction Error ◽  
Method Performance ◽  
Novel Approach ◽  
Spatio Temporal ◽  
Time Accuracy

Anomaly detection is an area of video analysis has a great importance in automated surveillance. Although it has been extensively studied, there has been little work started using CNN networks. Hence, in this thesis we presented a novel approach for learning motion features and modeling normal Spatio-temporal dynamics for anomaly detection. In our technique, we capture variations in scale of the patterns of motion in an image object by using optical flow dense estimation technique and train our auto encoder model using convolution long short term memories (ConvLSTM2D) as we are processing video frames and we predict the anomaly in real time using Euclidean distance between the generated and the ground truth frame and we achieved a real time accuracy of nearly 98% for the youtube videos which are not used for either testing or training. Error between the network’s output and the target output is used to classify a video volume as normal or abnormal. In addition to the use of reconstruction error, we also use prediction error for anomaly detection. The prediction models show comparable performance with state of the art methods. In comparison with the proposed method, performance is improved in one dataset. Moreover, running time is significantly faster.

Spatial and temporal dynamics of Pacific capelin Mallotus catervarius in the Gulf of Alaska: implications for ecosystem-based fisheries management

2020 ◽  
Vol 637 ◽  
pp. 117-140 ◽  
Author(s):  
DW McGowan ◽  
ED Goldstein ◽  
ML Arimitsu ◽  
AL Deary ◽  
O Ormseth ◽  
...  
Keyword(s):  
Temporal Dynamics ◽  
Marine Ecosystem ◽  
Gulf Of Alaska ◽  
Data Series ◽  
Limited Information ◽  
Important Species ◽  
Multiple Data ◽  
Spatial And Temporal Dynamics ◽  
Spawning Areas ◽  
Spatio Temporal

Pacific capelin Mallotus catervarius are planktivorous small pelagic fish that serve an intermediate trophic role in marine food webs. Due to the lack of a directed fishery or monitoring of capelin in the Northeast Pacific, limited information is available on their distribution and abundance, and how spatio-temporal fluctuations in capelin density affect their availability as prey. To provide information on life history, spatial patterns, and population dynamics of capelin in the Gulf of Alaska (GOA), we modeled distributions of spawning habitat and larval dispersal, and synthesized spatially indexed data from multiple independent sources from 1996 to 2016. Potential capelin spawning areas were broadly distributed across the GOA. Models of larval drift show the GOA’s advective circulation patterns disperse capelin larvae over the continental shelf and upper slope, indicating potential connections between spawning areas and observed offshore distributions that are influenced by the location and timing of spawning. Spatial overlap in composite distributions of larval and age-1+ fish was used to identify core areas where capelin consistently occur and concentrate. Capelin primarily occupy shelf waters near the Kodiak Archipelago, and are patchily distributed across the GOA shelf and inshore waters. Interannual variations in abundance along with spatio-temporal differences in density indicate that the availability of capelin to predators and monitoring surveys is highly variable in the GOA. We demonstrate that the limitations of individual data series can be compensated for by integrating multiple data sources to monitor fluctuations in distributions and abundance trends of an ecologically important species across a large marine ecosystem.

Spatio‐temporal dynamics of water quality and eutrophication in Lake Taihu, China

Ecohydrology ◽  
2021 ◽  
Author(s):  
Qiongfang Li ◽  
Yuting Zhu ◽  
Qihui Chen ◽  
Yu Li ◽  
Jing Chen ◽  
...  
Keyword(s):  
Water Quality ◽  
Temporal Dynamics ◽  
Lake Taihu ◽  
Spatio Temporal

scholarly journals Investigating the Spatio-Temporal Variation of Soil Moisture and Agricultural Drought towards Supporting Water Resources Management in the Red River Basin of Vietnam

2021 ◽  
Vol 13 (9) ◽  
pp. 4926
Author(s):  
Nguyen Duc Luong ◽  
Nguyen Hoang Hiep ◽  
Thi Hieu Bui
Keyword(s):  
Soil Moisture ◽  
River Basin ◽  
Temporal Dynamics ◽  
Regional Scale ◽  
Dry Season ◽  
Red River ◽  
Agricultural Drought ◽  
Severe Drought ◽  
Red River Basin ◽  
Spatio Temporal

The increasing serious droughts recently might have significant impacts on socioeconomic development in the Red River basin (RRB). This study applied the variable infiltration capacity (VIC) model to investigate spatio-temporal dynamics of soil moisture in the northeast, northwest, and Red River Delta (RRD) regions of the RRB part belongs to territory of Vietnam. The soil moisture dataset simulated for 10 years (2005–2014) was utilized to establish the soil moisture anomaly percentage index (SMAPI) for assessing intensity of agricultural drought. Soil moisture appeared to co-vary with precipitation, air temperature, evapotranspiration, and various features of land cover, topography, and soil type in three regions of the RRB. SMAPI analysis revealed that more areas in the northeast experienced severe droughts compared to those in other regions, especially in the dry season and transitional months. Meanwhile, the northwest mainly suffered from mild drought and a slightly wet condition during the dry season. Different from that, the RRD mainly had moderately to very wet conditions throughout the year. The areas of both agricultural and forested lands associated with severe drought in the dry season were larger than those in the wet season. Generally, VIC-based soil moisture approach offered a feasible solution for improving soil moisture and agricultural drought monitoring capabilities at the regional scale.

scholarly journals Photovoltaic Power Forecasting: Assessment of the Impact of Multiple Sources of Spatio-Temporal Data on Forecast Accuracy

Energies ◽  
2021 ◽  
Vol 14 (5) ◽  
pp. 1432
Author(s):  
Xwégnon Ghislain Agoua ◽  
Robin Girard ◽  
Georges Kariniotakis
Keyword(s):  
Satellite Images ◽  
Forecast Accuracy ◽  
Absolute Error ◽  
Data Sources ◽  
Test Case ◽  
Temporal Data ◽  
Multiple Sources ◽  
Forecasting Error ◽  
Spatio Temporal ◽  
The Impact

The efficient integration of photovoltaic (PV) production in energy systems is conditioned by the capacity to anticipate its variability, that is, the capacity to provide accurate forecasts. From the classical forecasting methods in the state of the art dealing with a single power plant, the focus has moved in recent years to spatio-temporal approaches, where geographically dispersed data are used as input to improve forecasts of a site for the horizons up to 6 h ahead. These spatio-temporal approaches provide different performances according to the data sources available but the question of the impact of each source on the actual forecasting performance is still not evaluated. In this paper, we propose a flexible spatio-temporal model to generate PV production forecasts for horizons up to 6 h ahead and we use this model to evaluate the effect of different spatial and temporal data sources on the accuracy of the forecasts. The sources considered are measurements from neighboring PV plants, local meteorological stations, Numerical Weather Predictions, and satellite images. The evaluation of the performance is carried out using a real-world test case featuring a high number of 136 PV plants. The forecasting error has been evaluated for each data source using the Mean Absolute Error and Root Mean Square Error. The results show that neighboring PV plants help to achieve around 10% reduction in forecasting error for the first three hours, followed by satellite images which help to gain an additional 3% all over the horizons up to 6 h ahead. The NWP data show no improvement for horizons up to 6 h but is essential for greater horizons.

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