Transferability of Graph Neural Networks for Time Series Applications

Transfer learning enabled machine learning tasks with scarce data to achieve superhuman performance in multiple domains like computer vision and natural language processing. However, knowledge transfer's success was mostly on grid structured data and using convolutional neural networks that assume local, hierarchical, and stationary data. Time series data in several applications, specifically doesn't meet these assumptions. This renders traditional transfer learning irrelevant with the potential leading to negative transfer. After achieving superior performance on high-dimensional data like social networks and recommender systems, graph neural networks are currently applied to time series data. In this thesis, we investigate the transferability of graph neural networks on time series data compared to traditional time series algorithms. We also explore a new graph similarity approach and compare its effect on time series algorithms pretraining and negative transfer for pandemic time series forecasting

Data based identification and prediction of nonlinear and complex dynamical systems

We thank Dr. R. Yang (formerly at ASU), Dr. R.-Q. Su (formerly at ASU), and Mr. Zhesi Shen for their contributions to a number of original papers on which this Review is partly based. This work was supported by ARO under Grant No. W911NF-14-1-0504. W.-X. Wang was also supported by NSFC under Grants No. 61573064 and No. 61074116, as well as by the Fundamental Research Funds for the Central Universities, Beijing Nova Programme.Peer reviewedPostprin

Approaching Economic Issues through Epidemiology–An Introduction to Business Epidemiology

In the tradition of transferring models and concepts from one science to another, our research explores the possibility of importing some concepts, definitions and approaches from human epidemiology to economic research, based on the extensive usage of medical terms and concepts in economy. The article explores some basic epidemiology concepts and their possible relevance to economic research, with the final goal to provide a new viewpoint over the economic phenomena, usable in economic crisis. The article introduces the concept of “business epidemiology” as a possible scientific approach to the economic crisis.epidemiology; business disease; company health; research methodology; financial contagion

Multimodal Graph Learning for Modeling Emerging Pandemics with Big Data

Accurate forecasting and analysis of emerging pandemics play a crucial role in effective public health management and decision-making. Traditional approaches primarily rely on epidemiological data, overlooking other valuable sources of information that could act as sensors or indicators of pandemic patterns. In this paper, we propose a novel framework called MGL4MEP that integrates temporal graph neural networks and multi-modal data for learning and forecasting. We incorporate big data sources, including social media content, by utilizing specific pre-trained language models and discovering the underlying graph structure among users. This integration provides rich indicators of pandemic dynamics through learning with temporal graph neural networks. Extensive experiments demonstrate the effectiveness of our framework in pandemic forecasting and analysis, outperforming baseline methods across different areas, pandemic situations, and prediction horizons. The fusion of temporal graph learning and multi-modal data enables a comprehensive understanding of the pandemic landscape with less time lag, cheap cost, and more potential information indicators

Big data and hydroinformatics

Big data is popular in the areas of computer science, commerce and bioinformatics, but is in an early stage in hydroinformatics. Big data is originated from the extreme large datasets that cannot be processed in tolerable elapsed time with the traditional data processing methods. Using the analogy from the object-oriented programming, big data should be considered as objects encompassing the data, its characteristics and the processing methods. Hydroinformatics can benefit from the big data technology with newly emerged data, techniques and analytical tools to handle large datasets, from which creative ideas and new values could be mined. This paper provides a timely review on big data with its relevance to hydroinformatics. A further exploration on precipitation big data is discussed because estimation of precipitation is an important part of hydrology for managing floods and droughts, and understanding the global water cycle. It is promising that fusion of precipitation data from remote sensing, weather radar, rain gauge and numerical weather modelling could be achieved by parallel computing and distributed data storage, which will trigger a leap in precipitation estimation as the available data from multiple sources could be fused to generate a better product than those from single sources

Forecasting COVID-19 Pandemic – A scientometric Review of Methodologies Based on Mathematics, Statistics, and Machine Learning

Introduction: The COVID-19 pandemic is being regarded as a worldwide public health issue. The virus has disseminated to 228 nations, resulting in a staggering 772 million global infections and a significant death toll of 6.9 million. Since its initial occurrence in late 2019, many approaches have been employed to anticipate and project the future spread of COVID-19. This study provides a concentrated examination and concise evaluation of the forecasting methods utilised for predicting COVID-19. To begin with, A comprehensive scientometric analysis has been conducted using COVID-19 data obtained from the Scopus and Web of Science databases, utilising bibliometric research. Subsequently, a thorough examination and classification of the existing literature and utilised approaches has been conducted. First of its kind, this review paper analyses all kinds of methodologies used for COVID-19 forecasting including Mathematical, Statistical, Artificial Intelligence - Machine Learning, Ensembles, Transfer Learning and hybrid methods.  Data has been collected regarding different COVID-19 characteristics that are being taken into account for prediction purposes, as well as the methodology used to develop the model. Additional statistical analysis has been conducted using existing literature to determine the patterns of COVID-19 forecasting in relation to the prevalence of methodologies, programming languages, and data sources. This review study may be valuable for researchers, specialists, and decision-makers concerned in administration of the Corona Virus pandemic. It can assist in developing enhanced forecasting models and strategies for pandemic management

Applications of Artificial Intelligence in Battling Against Covid-19: A Literature Review

© 2020 Elsevier Ltd. All rights reserved.Colloquially known as coronavirus, the Severe Acute Respiratory Syndrome CoronaVirus 2 (SARS-CoV-2), that causes CoronaVirus Disease 2019 (COVID-19), has become a matter of grave concern for every country around the world. The rapid growth of the pandemic has wreaked havoc and prompted the need for immediate reactions to curb the effects. To manage the problems, many research in a variety of area of science have started studying the issue. Artificial Intelligence is among the area of science that has found great applications in tackling the problem in many aspects. Here, we perform an overview on the applications of AI in a variety of fields including diagnosis of the disease via different types of tests and symptoms, monitoring patients, identifying severity of a patient, processing covid-19 related imaging tests, epidemiology, pharmaceutical studies, etc. The aim of this paper is to perform a comprehensive survey on the applications of AI in battling against the difficulties the outbreak has caused. Thus we cover every way that AI approaches have been employed and to cover all the research until the writing of this paper. We try organize the works in a way that overall picture is comprehensible. Such a picture, although full of details, is very helpful in understand where AI sits in current pandemonium. We also tried to conclude the paper with ideas on how the problems can be tackled in a better way and provide some suggestions for future works.Peer reviewe

MSGNN: Multi-scale Spatio-temporal Graph Neural Network for Epidemic Forecasting

Infectious disease forecasting has been a key focus and proved to be crucial in controlling epidemic. A recent trend is to develop forecast-ing models based on graph neural networks (GNNs). However, existing GNN-based methods suffer from two key limitations: (1) Current models broaden receptive fields by scaling the depth of GNNs, which is insuffi-cient to preserve the semantics of long-range connectivity between distant but epidemic related areas. (2) Previous approaches model epidemics within single spatial scale, while ignoring the multi-scale epidemic pat-terns derived from different scales. To address these deficiencies, we devise the Multi-scale Spatio-temporal Graph Neural Network (MSGNN) based on an innovative multi-scale view. To be specific, in the proposed MSGNN model, we first devise a novel graph learning module, which directly captures long-range connectivity from trans-regional epidemic signals and integrates them into a multi-scale graph. Based on the learned multi-scale graph, we utilize a newly designed graph convolution module to exploit multi-scale epidemic patterns. This module allows us to facilitate multi-scale epidemic modeling by mining both scale-shared and scale-specific pat-terns. Experimental results on forecasting new cases of COVID-19 in United State demonstrate the superiority of our method over state-of-arts. Further analyses and visualization also show that MSGNN offers not only accurate, but also robust and interpretable forecasting result.Comment: 29 page