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Spatio-Temporal Multi-step Prediction of Influenza Outbreaks
[article]
2021
arXiv
pre-print
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In this study, we performed the spatio-temporal multi-step prediction of influenza outbreaks. ...
The methodology considering the spatio-temporal features improves the multi-step prediction of flu outbreaks. ...
Conclusions In this study, we performed the spatio-temporal multi-step prediction of influenza outbreaks.The methodology considering the spatio-temporal features improves the multi-step prediction of flu ...
arXiv:2102.08137v1
fatcat:75frwdjgkjb5jbuijdugnrg6w4
A novel data-driven model for real-time influenza forecasting
[article]
2017
bioRxiv
pre-print
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The key contributions of our approach are both applying deep learning methods and incorporation of environmental and spatio-temporal factors to improve the performance of the influenza forecasting models ...
The results offer a promising direction in terms of both data-driven forecasting methods and capturing the influence of spatio-temporal and environmental factors for influenza forecasting methods. ...
based on the flu count symbol at this time-step t. 3) Spatio-temporal adjustment factor: Geographical proximity, in general, has a strong effect on the influenza outbreak in a particular region. ...
doi:10.1101/185512
fatcat:3unqxjjwkrbatjmqtbw5gbeonm
A Novel Data-Driven Model for Real-Time Influenza Forecasting
2019
IEEE Access
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INDEX TERMS Influenza forecasting, LSTM, recurrent neural networks, spatio-temporal data, time series forecasting. ...
The results offer a promising direction in terms of both using the data-driven forecasting methods and capturing the influence of spatio-temporal and environmental factors to improve influenza forecasting ...
SPATIO-TEMPORAL ADJUSTMENT FACTOR Geographical proximity, in general, strongly affects influenza outbreak in a particular region. ...
doi:10.1109/access.2018.2888585
fatcat:fpxj4ke6gje5rmnazvjdn3y52m
STAN: Spatio-Temporal Attention Network for Pandemic Prediction Using Real World Evidence
[article]
2020
arXiv
pre-print
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2020 pre-print arXiv:2008.04215v1 fatcat:ofl3myehdvhrjbpf237ojz6t3q
Materials and Methods: We proposed a spatio-temporal attention network (STAN) for pandemic prediction. ...
It uses an attention-based graph convolutional network to capture geographical and temporal trends and predict the number of cases for a fixed number of days into the future. ...
In this work, we propose a new Spatio-Temporal Attention Network for pandemic prediction using real world evidence, named STAN. ...
arXiv:2008.04215v2
fatcat:ymge66tqhzeajf23szqhs2i23i
Graph Message Passing with Cross-location Attentions for Long-term ILI Prediction
[article]
2019
arXiv
pre-print
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Early prediction of epidemic outbreaks plays a pivotal role in disease intervention and control. ...
Most existing work has either limited long-term prediction performance or lacks a comprehensive ability to capture spatio-temporal dependencies in data. ...
Long-term Epidemic Prediction Long-term prediction (aka multi-step prediction), that is, predicting several steps ahead, is a challenge in time series prediction. ...
arXiv:1912.10202v2
fatcat:i26dbx3c75dl7etf3ywezbfiqe
Global Spatio-temporal Patterns of Influenza in the Post-pandemic Era
2015
Scientific Reports
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We study the global spatio-temporal patterns of influenza dynamics. ...
discuss the mechanisms that are likely to generate these events taking into account the role of multi-strain dynamics. ...
Figure 1 . 1 Spatio-temporal patterns of H1N1pdm and H3N2. ...
doi:10.1038/srep11013
pmid:26046930
pmcid:PMC4457022
fatcat:6sw3yb4d2fccjpqc52aqhqc5o4
A Predictive Risk Model for A(H7N9) Human Infections Based on Spatial-Temporal Autocorrelation and Risk Factors: China, 2013–2014
2015
International Journal of Environmental Research and Public Health
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This study investigated the spatial distribution, spatial autocorrelation, temporal cluster, spatial-temporal autocorrelation and probable risk factors of H7N9 outbreaks in humans from March 2013 to December ...
the high risk areas (the predictive risk > 0.70) of the predictive risk map, whereas 44.6% (45/101) of which overlaid on the high risk areas (the predictive risk > 0.70) for the traditional model, and ...
Conflicts of Interest: The authors declare no conflict of interest. ...
doi:10.3390/ijerph121214981
pmid:26633446
pmcid:PMC4690917
fatcat:4dnn4l4b6nha3c2eolalygrmem
Optimizing spatial allocation of seasonal influenza vaccine under temporal constraints
2019
PLoS Computational Biology
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In this paper, we study the VaccIntDesign problem in the context of seasonal influenza spread in the United States. ...
However, since seasonal influenza spread has a typical spatial trend, and due to the temporal constraints enforced by the availability schedule, the when and where problems become equally, if not more, ...
Acknowledgments We thank our external collaborators and members of the Network Systems Science & Advanced Computing Division for their suggestions and comments.
Author Contributions ...
doi:10.1371/journal.pcbi.1007111
pmid:31525184
pmcid:PMC6762205
fatcat:32ziiiodzrdebcicdx3sxgvolq
Understanding and predicting the global spread of emergent infectious diseases
2014
Public Health Forum
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Sophisticated computer simulations have become a key tool for understanding and predicting disease spread on a global scale. ...
AbstractThe emergence and global spread of human infectious diseases has become one of the most serious public health threats of the 21st century. ...
First, the complex nature of spatio-temporal patterns of disease dynamics and computer generated pandemic scenarios are still poorly understood at a fundamental level. ...
doi:10.1016/j.phf.2014.07.001
fatcat:hiijq5jsq5fqtdunhhpj46g35e
Design and implementation of a Space-Time Intelligence System for disease surveillance
2005
Journal of Geographical Systems
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We present several spatial, temporal, spatio-temporal and epidemiological queries emergent from the data model. ...
In this paper, we discuss issues in designing data structures, indexing, and queries for spatio-temporal data within the context of health surveillance. ...
aggregations of point objects, and Peuquet and Duan (1995) formulated an event-based spatio-temporal data model (ESTDM) that maintains spatio-temporal data as a sequence of temporal events associated ...
doi:10.1007/s10109-005-0147-6
fatcat:eqjdq6335ndchfn54s5fnuyo3a
Use of Artificial Intelligence on spatio-temporal data to generate insights during COVID-19 pandemic: A Review
[article]
2020
medRxiv
pre-print
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As it evolved from a local epidemic isolated to certain regions of China, to the deadliest pandemic since the influenza outbreak of 1918, scientists all over the world have only amplified their efforts ...
In this review, we present a comprehensive analysis of the use of AI techniques for spatio-temporal modeling and forecasting and impact modeling on diverse populations as it relates to COVID-19. ...
A study conducted for Germany has developed a memory based integro-differential network model to predict spatio-temporal outbreak dynamics such as number of infections, hospitalization rates, and demands ...
doi:10.1101/2020.11.22.20232959
fatcat:dsn5cwrhfjflxkjy75lwi7pcnq
Identifying climate drivers of infectious disease dynamics: recent advances and challenges ahead
2017
Proceedings of the Royal Society of London. Biological Sciences
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Several scales and types of spatio-temporal variation can be leveraged.
(a) Spatial variation The geographical range limits of infections can powerfully indicate the effects of climate. ...
All of these data sources have uncertainties and biases; consequently, the appropriate source depends on the question of interest, pathogen biology and the spatio-temporal scales of the infectious disease ...
doi:10.1098/rspb.2017.0901
pmid:28814655
pmcid:PMC5563806
fatcat:5l5wtv3efjhovjpby6q6wc4x4a
Improved state-level influenza nowcasting in the United States leveraging Internet-based data and network approaches
2019
Nature Communications
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historical flu trends within each state, and (2) a network-based approach leveraging spatio-temporal synchronicities observed in historical influenza activity across states. ...
The ensemble considerably outperforms each component method in addition to previously proposed state-specific methods for influenza tracking, with higher correlations and lower prediction errors. ...
The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
Author contributions ...
doi:10.1038/s41467-018-08082-0
pmid:30635558
pmcid:PMC6329822
fatcat:yji7tgm4ovaizetlgu6vexachq
Data-Centric Epidemic Forecasting: A Survey
[article]
2022
arXiv
pre-print
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of mechanistic models with the effectiveness and flexibility of statistical approaches. ...
The spread of diseases is subject to multiple confounding factors spanning human behavior, pathogen dynamics, weather and environmental conditions. ...
STAN: spatio-temporal attention network for pandemic prediction using real-world evidence. ...
arXiv:2207.09370v2
fatcat:x5a7uvmwrbgd7fiskufmn5nlmi
Optimized Forecasting Method for Weekly Influenza Confirmed Cases
2020
International Journal of Environmental Research and Public Health
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To overcome the limitations of the original ANFIS, we use two metaheuristics, called flower pollination algorithm (FPA) and sine cosine algorithm (SCA), to enhance the prediction of the ANFIS. ...
Influenza epidemic is a serious threat to the entire world, which causes thousands of death every year and can be considered as a public health emergency that needs to be more addressed and investigated ...
[5] proposed a Gaussian process (GP) regression model to predict the spatio-temporal propagation of influenza in the USA. Achrekar et al. [6] used Twitter data to predict influenza trends. ...
doi:10.3390/ijerph17103510
pmid:32443409
fatcat:d6g5ga7ccffrtfyarulmf5v6bi
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