scholarly journals Inferring who-infected-whom-where in the 2016 Zika outbreak in Singapore—a spatio-temporal model

2019 ◽  
Vol 16 (155) ◽  
pp. 20180604 ◽  
Author(s):  
Kiesha Prem ◽  
Max S. Y. Lau ◽  
Clarence C. Tam ◽  
Marc Z. J. Ho ◽  
Lee-Ching Ng ◽  
...  
Keyword(s):  
Data Augmentation ◽  
Herd Immunity ◽  
Pathogen Transmission ◽  
Contact Tracing ◽  
Virus Infections ◽  
Temporal Model ◽  
Spatio Temporal ◽  
Distant Relative ◽  
Spatial Transmission ◽  
Temporal Spread

Singapore experienced its first known Zika outbreak in 2016. Given the lack of herd immunity, the suitability of the climate for pathogen transmission, and the year-round presence of the vector— Aedes aegypti —Zika had the potential to become endemic, like dengue. Guillain–Barré syndrome and microcephaly are severe complications associated elsewhere with Zika and the risk of these complications makes understanding its spread imperative. We investigated the spatio-temporal spread of locally transmitted Zika in Singapore and assessed the relevance of non-residential transmission of Zika virus infections, by inferring the possible infection tree (i.e. who-infected-whom-where ) and comparing inferences using geographically resolved data on cases' home, their work, or their home and work. We developed a spatio-temporal model using time of onset and both addresses of the Zika-confirmed cases between July and September 2016 to estimate the infection tree using Bayesian data augmentation. Workplaces were involved in a considerable fraction (64.2%) of infections, and homes and workplaces may be distant relative to the scale of transmission, allowing ambulant infected persons may act as the ‘vector’ infecting distant parts of the country. Contact tracing is a challenge for mosquito-borne diseases, but inferring the geographically structured transmission tree sheds light on the spatial transmission of Zika to immunologically naive regions of the country.

scholarly journals Spatio-Temporal Spread Pattern of COVID-19 in Italy

Mathematics ◽  
2021 ◽  
Vol 9 (19) ◽  
pp. 2454
Author(s):  
Nicoletta D’Angelo ◽  
Antonino Abbruzzo ◽  
Giada Adelfio
Keyword(s):  
Temporal Evolution ◽  
Virus Infections ◽  
Mobility Patterns ◽  
Modeling Framework ◽  
Spatial Trend ◽  
Italian Government ◽  
Italian Provinces ◽  
Spread Pattern ◽  
Spatio Temporal ◽  
Temporal Spread

This paper investigates the spatio-temporal spread pattern of COVID-19 in Italy, during the first wave of infections, from February to October 2020. Disease mappings of the virus infections by using the Besag–York–Mollié model and some spatio-temporal extensions are provided. This modeling framework, which includes a temporal component, allows the studying of the time evolution of the spread pattern among the 107 Italian provinces. The focus is on the effect of citizens’ mobility patterns, represented here by the three distinct phases of the Italian virus first wave, identified by the Italian government, also characterized by the lockdown period. Results show the effectiveness of the lockdown action and an inhomogeneous spatial trend that characterizes the virus spread during the first wave. Furthermore, the results suggest that the temporal evolution of each province’s cases is independent of the temporal evolution of the other ones, meaning that the contagions and temporal trend may be caused by some province-specific aspects rather than by the subjects’ spatial movements.

scholarly journals A comparison of multiple neighborhood matrix specifications for spatio-temporal model fitting: a case study on COVID-19 data

2021 ◽  
Author(s):  
Álvaro Briz-Redón ◽  
Adina Iftimi ◽  
Juan Francisco Correcher ◽  
Jose De Andrés ◽  
Manuel Lozano ◽  
...  
Keyword(s):  
Model Fitting ◽  
Temporal Model ◽  
Spatio Temporal

scholarly journals Theory and Experimental Validation of a Spatio-temporal Model of Chemotherapy Transport to Enhance Tumor Cell Kill

2016 ◽  
Vol 12 (6) ◽  
pp. e1004969 ◽  
Author(s):  
Zhihui Wang ◽  
Romica Kerketta ◽  
Yao-Li Chuang ◽  
Prashant Dogra ◽  
Joseph D. Butner ◽  
...  
Keyword(s):  
Tumor Cell ◽  
Experimental Validation ◽  
Cell Kill ◽  
Temporal Model ◽  
Spatio Temporal ◽  
Enhance Tumor Cell

Based on spatio-temporal graph convolution networks with residual connection for intelligence behavior recognition

2021 ◽  
pp. 002072092199660
Author(s):  
Yinong Zhang ◽  
Shanshan Guan ◽  
Cheng Xu ◽  
Hongzhe Liu
Keyword(s):  
Human Behavior ◽  
Behavior Recognition ◽  
Convolutional Network ◽  
Skeleton Model ◽  
Basic Technology ◽  
Temporal Model ◽  
Temporal Graph ◽  
Spatio Temporal ◽  
Human Behavior Recognition ◽  
Behavior Characteristics

In the era of intelligent education, human behavior recognition based on computer vision is an important branch of pattern recognition. Human behavior recognition is a basic technology in the fields of intelligent monitoring and human-computer interaction in education. The dynamic changes of human skeleton provide important information for the recognition of educational behavior. Traditional methods usually use manual information to label or traverse rules only, resulting in limited representation capabilities and poor generalization performance of the model. In this paper, a kind of dynamic skeleton model with residual is adopted—a spatio-temporal graph convolutional network based on residual connections, which not only overcomes the limitations of previous methods, but also can learn the spatio-temporal model from the skeleton data. In the big bone NTU-RGB + D dataset, the network model not only improved the representation ability of human behavior characteristics, but also improved the generalization ability, and achieved better recognition effect than the existing model. In addition, this paper also compares the results of behavior recognition on subsets of different joint points, and finds that spatial structure division have better effects.

Tuberculosis in Children 10 Years of Age and Younger: Three Decades of Experience During the Chemotherapeutic Era

PEDIATRICS ◽  
1991 ◽  
Vol 88 (2) ◽  
pp. 236-241
Author(s):  
Rosa Lee Nemir ◽  
Donna O'Hare
Keyword(s):  
Medical Profession ◽  
Contact Tracing ◽  
Virus Infections ◽  
Antituberculosis Therapy ◽  
Immunodeficiency Virus ◽  
Close Observation ◽  
Chest Clinic ◽  
Long Term Follow Up ◽  
Tuberculosis In Children

The 863 patients, aged 10 years and younger, treated at the Children's Chest Clinic of Bellevue Hospital during three decades (1953 through 1981) clearly indicated the success of antituberculosis therapy. There were no deaths from tuberculosis. Early treatment is associated with a reduction in the serious forms of disease, eg, meningitis, miliary disease, and bone infections, and with preventing death. Medication was well tolerated: only 1.1% of the patients had adverse reactions, all of which were reversible. Consistent compliance with medication of only 62% of patients is a challenge to the medical profession. Only 22.5% of mycobacterial cultures were positive. Long-term follow-up of patients was rewarding: seven pregnancies with healthy mothers and babies, and no reactivation of tuberculosis by later infections, even those such as measles or pneumonia. The severity of disease was related largely to patient's age (3 years and younger) and intimacy of contact, the highest rate being when the mother was the contact. The long-term experience emphasizes the value of early identification, therapeutic compliance, and comprehensive contact tracing in the future elimination of tuberculosis. Prophylactic therapy and close observation should be considered for contacts, especially those exposed to human immunodeficiency virus infections and addicted to drugs.

scholarly journals Comparison between VAR, GSTAR, FFNN-VAR and FFNN-GSTAR Models for Forecasting Oil Production

MATEMATIKA ◽  
2018 ◽  
Vol 34 (1) ◽  
pp. 103-111 ◽  
Author(s):  
Suhartono Suhartono ◽  
Dedy Dwi Prastyo ◽  
Heri Kuswanto ◽  
Muhammad Hisyam Lee
Keyword(s):  
Forecast Accuracy ◽  
Oil Production ◽  
Training Data ◽  
Temporal Data ◽  
Monthly Data ◽  
Vector Autoregressive ◽  
Temporal Models ◽  
Accurate Forecast ◽  
Temporal Model ◽  
Spatio Temporal

Monthly data about oil production at several drilling wells is an example of spatio-temporal data. The aim of this research is to propose nonlinear spatio-temporal model, i.e. Feedforward Neural Network - Vector Autoregressive (FFNN-VAR) and FFNN - Generalized Space-Time Autoregressive (FFNN-GSTAR), and compare their forecast accuracy to linear spatio-temporal model, i.e. VAR and GSTAR. These spatio-temporal models are proposed and applied for forecasting monthly oil production data at three drilling wells in East Java, Indonesia. There are 60 observations that be divided to two parts, i.e. the first 50 observations for training data and the last 10 observations for testing data. The results show that FFNN-GSTAR(11) and FFNN-VAR(1) as nonlinear spatio-temporal models tend to give more accurate forecast than VAR(1) and GSTAR(11) as linear spatio-temporal models. Moreover, further research about nonlinear spatio-temporal models based on neural networks and GSTAR is needed for developing new hybrid models that could improve the forecast accuracy.

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