Spotting Virus from Satellites: Modeling the Circulation of West Nile Virus Through Graph Neural Networks

• URL: http://arxiv.org/abs/2209.05251v2
• Date: Thu, 6 Jul 2023 09:16:57 GMT
• Title: Spotting Virus from Satellites: Modeling the Circulation of West Nile Virus Through Graph Neural Networks
• Authors: Lorenzo Bonicelli, Angelo Porrello, Stefano Vincenzi, Carla Ippoliti, Federica Iapaolo, Annamaria Conte, Simone Calderara
• Abstract summary: West Nile Virus (WNV) represents one of the most common mosquito-borne zoonosis viral infections. We build upon Graph Neural Networks (GNN) to aggregate features from neighbouring places. We inject time-related information directly into the model to take into account the seasonality of virus spread.
• Score: 10.235799644961816
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The occurrence of West Nile Virus (WNV) represents one of the most common mosquito-borne zoonosis viral infections. Its circulation is usually associated with climatic and environmental conditions suitable for vector proliferation and virus replication. On top of that, several statistical models have been developed to shape and forecast WNV circulation: in particular, the recent massive availability of Earth Observation (EO) data, coupled with the continuous advances in the field of Artificial Intelligence, offer valuable opportunities. In this paper, we seek to predict WNV circulation by feeding Deep Neural Networks (DNNs) with satellite images, which have been extensively shown to hold environmental and climatic features. Notably, while previous approaches analyze each geographical site independently, we propose a spatial-aware approach that considers also the characteristics of close sites. Specifically, we build upon Graph Neural Networks (GNN) to aggregate features from neighbouring places, and further extend these modules to consider multiple relations, such as the difference in temperature and soil moisture between two sites, as well as the geographical distance. Moreover, we inject time-related information directly into the model to take into account the seasonality of virus spread. We design an experimental setting that combines satellite images - from Landsat and Sentinel missions - with ground truth observations of WNV circulation in Italy. We show that our proposed Multi-Adjacency Graph Attention Network (MAGAT) consistently leads to higher performance when paired with an appropriate pre-training stage. Finally, we assess the importance of each component of MAGAT in our ablation studies.

Related papers

• MambaDS: Near-Surface Meteorological Field Downscaling with Topography Constrained Selective State Space Modeling [68.69647625472464]
  Downscaling, a crucial task in meteorological forecasting, enables the reconstruction of high-resolution meteorological states for target regions. Previous downscaling methods lacked tailored designs for meteorology and encountered structural limitations. We propose a novel model called MambaDS, which enhances the utilization of multivariable correlations and topography information.
  arXiv  Detail & Related papers  (2024-08-20T13:45:49Z)
• Generating Fine-Grained Causality in Climate Time Series Data for Forecasting and Anomaly Detection [67.40407388422514]
  We design a conceptual fine-grained causal model named TBN Granger Causality. Second, we propose an end-to-end deep generative model called TacSas, which discovers TBN Granger Causality in a generative manner. We test TacSas on climate benchmark ERA5 for climate forecasting and the extreme weather benchmark of NOAA for extreme weather alerts.
  arXiv  Detail & Related papers  (2024-08-08T06:47:21Z)
• VN-Net: Vision-Numerical Fusion Graph Convolutional Network for Sparse Spatio-Temporal Meteorological Forecasting [12.737085738169164]
  VN-Net is the first attempt to introduce GCN method to utilize multi-modal data for better handling sparse-temporal meteorological forecasting. VN-Net outperforms state-of-the-art by a significant margin on mean absolute error (MAE) and root mean square error (RMSE) for temperature, relative humidity, and forecasting.
  arXiv  Detail & Related papers  (2024-01-26T12:41:57Z)
• SatBird: Bird Species Distribution Modeling with Remote Sensing and Citizen Science Data [68.2366021016172]
  We present SatBird, a satellite dataset of locations in the USA with labels derived from presence-absence observation data from the citizen science database eBird. We also provide a dataset in Kenya representing low-data regimes. We benchmark a set of baselines on our dataset, including SOTA models for remote sensing tasks.
  arXiv  Detail & Related papers  (2023-11-02T02:00:27Z)
• Forecasting West Nile Virus with Graph Neural Networks: Harnessing Spatial Dependence in Irregularly Sampled Geospatial Data [0.0]
  We apply a spatially aware graph neural network model to forecast the presence of West Nile virus in Illinois. More generally, we show that graph neural networks applied to irregularly sampled geospatial data can exceed the performance of a range of baseline methods.
  arXiv  Detail & Related papers  (2022-12-21T21:08:45Z)
• Environmental Sensor Placement with Convolutional Gaussian Neural Processes [65.13973319334625]
  It is challenging to place sensors in a way that maximises the informativeness of their measurements, particularly in remote regions like Antarctica. Probabilistic machine learning models can suggest informative sensor placements by finding sites that maximally reduce prediction uncertainty. This paper proposes using a convolutional Gaussian neural process (ConvGNP) to address these issues.
  arXiv  Detail & Related papers  (2022-11-18T17:25:14Z)
• Autonomous Mosquito Habitat Detection Using Satellite Imagery and Convolutional Neural Networks for Disease Risk Mapping [0.0]
  Mosquito vectors are known for disease transmission that cause over one million deaths globally each year. Modern approaches, such as drones, UAVs, and other aerial imaging technology are costly when implemented and are only most accurate on a finer spatial scale. The proposed convolutional neural network(CNN) approach can be applied for disease risk mapping and further guide preventative efforts on a more global scale.
  arXiv  Detail & Related papers  (2022-03-09T00:54:59Z)
• Forecasting large-scale circulation regimes using deformable convolutional neural networks and global spatiotemporal climate data [86.1450118623908]
  We investigate a supervised machine learning approach based on deformable convolutional neural networks (deCNNs) We forecast the North Atlantic-European weather regimes during extended boreal winter for 1 to 15 days into the future. Due to its wider field of view, we also observe deCNN achieving considerably better performance than regular convolutional neural networks at lead times beyond 5-6 days.
  arXiv  Detail & Related papers  (2022-02-10T11:37:00Z)
• Health Status Prediction with Local-Global Heterogeneous Behavior Graph [69.99431339130105]
  Estimation of health status can be achieved with various kinds of data streams continuously collected from wearable sensors. We propose to model the behavior-related multi-source data streams with a local-global graph. We take experiments on StudentLife dataset, and extensive results demonstrate the effectiveness of our proposed model.
  arXiv  Detail & Related papers  (2021-03-23T11:10:04Z)
• Ensemble Forecasting of the Zika Space-TimeSpread with Topological Data Analysis [13.838100337224075]
  Zika virus is primarily transmitted through bites of infected mosquitoes of the species Aedes aegypti and Aedes albopictus. The abundance of mosquitoes and mosquitoes, as a result, the prevalence of Zika virus infections are common in areas which have high precipitation, high temperature, and high population density. We introduce new concept of cumulative Betti numbers and then integrate the cumulative Betti numbers as topological descriptors into three machine learning models.
  arXiv  Detail & Related papers  (2020-09-24T16:42:19Z)
• Quantifying the Effects of Contact Tracing, Testing, and Containment Measures in the Presence of Infection Hotspots [18.227721607607183]
  Multiple lines of evidence strongly suggest that infection hotspots, where a single individual infects many others, play a key role in the transmission dynamics of COVID-19. We introduce a temporal point process modeling framework that specifically represents visits to the sites where individuals get in contact and infect each other. Under our model, the number of infections caused by an infectious individual naturally emerges to be overdispersed.
  arXiv  Detail & Related papers  (2020-04-15T17:18:32Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.