Learning Social Meta-knowledge for Nowcasting Human Mobility in Disaster

Human mobility nowcasting is a fundamental research problem for intelligent transportation planning, disaster responses and management, etc. In particular, human mobility under big disasters such as hurricanes and pandemics deviates from its daily routine to a large extent, which makes the task more challenging. Existing works mainly focus on traffic or crowd flow prediction in normal situations. To tackle this problem, in this study, disaster-related Twitter data is incorporated as a covariate to understand the public awareness and attention about the disaster events and thus perceive their impacts on the human mobility. Accordingly, we propose a Meta-knowledge-Memorizable Spatio-Temporal Network (MemeSTN), which leverages memory network and meta-learning to fuse social media and human mobility data. Extensive experiments over three real-world disasters including Japan 2019 typhoon season, Japan 2020 COVID-19 pandemic, and US 2019 hurricane season were conducted to illustrate the effectiveness of our proposed solution. Compared to the state-of-the-art spatio-temporal deep models and multivariate-time-series deep models, our model can achieve superior performance for nowcasting human mobility in disaster situations at both country level and state level. © 2023 ACM.

Human Mobility-based Individual-level Epidemic Simulation Platform

COVID-19 has spread worldwide, and over 140 million people have been confirmed infected, over 3 million people have died, and the numbers are still increasing dramatically. The consensus has been reached by scientists that COVID-19 can be transmitted in an airborne way, and human-to-human transmission is the primary cause of the fast spread of COVID-19. Thus, mobility should be restricted to control the epidemic, and many governments worldwide have succeeded in curbing the spread by means of control policies like city lockdowns. Against this background, we propose a novel fine-grained transmission model based on real-world human mobility data and develop a platform that helps the researcher or governors to explore the possibility of future development of the epidemic spreading and simulate the outcomes of human mobility and the epidemic state under different epidemic control policies. The proposed platform can also support users to determine potential contacts, discover regions with high infectious risks, and assess the individual infectious risk. The multi-functional platform aims at helping the users to evaluate the effectiveness of a regional lockdown policy and facilitate the process of screening and more accurately targeting the potential virus carriers. © 2022 held by the owner/author(s). Publication rights licensed to ACM.

MepoGNN: Metapopulation Epidemic Forecasting with Graph Neural Networks

Epidemic prediction is a fundamental task for epidemic control and prevention. Many mechanistic models and deep learning models are built for this task. However, most mechanistic models have difficulty estimating the time/region-varying epidemiological parameters, while most deep learning models lack the guidance of epidemiological domain knowledge and interpretability of prediction results. In this study, we propose a novel hybrid model called MepoGNN for multi-step multi-region epidemic forecasting by incorporating Graph Neural Networks (GNNs) and graph learning mechanisms into Metapopulation SIR model. Our model can not only predict the number of confirmed cases but also explicitly learn the epidemiological parameters and the underlying epidemic propagation graph from heterogeneous data in an end-to-end manner. Experiment results demonstrate our model outperforms the existing mechanistic models and deep learning models by a large margin. Furthermore, the analysis on the learned parameters demonstrates the high reliability and interpretability of our model and helps better understanding of epidemic spread. Our model and data have already been public on GitHub https://github.com/deepkashiwa20/MepoGNN.git. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

A Survey on Data-driven COVID-19 and Future Pandemic Management

The COVID-19 pandemic has resulted in more than 440 million confirmed cases globally and almost 6 million reported deaths as of March 2022. Consequently, the world experienced grave repercussions to citizens' lives, health, wellness, and the economy. In responding to such a disastrous global event, countermeasures are often implemented to slow down and limit the virus's rapid spread. Meanwhile, disaster recovery, mitigation, and preparation measures have been taken to manage the impacts and losses of the ongoing and future pandemics. Data-driven techniques have been successfully applied to many domains and critical applications in recent years. Due to the highly interdisciplinary nature of pandemic management, researchers have proposed and developed data-driven techniques across various domains. However, a systematic and comprehensive survey of data-driven techniques for pandemic management is still missing. In this article, we review existing data analysis and visualization techniques and their applications for COVID-19 and future pandemic management with respect to four phases (namely, Response, Recovery, Mitigation, and Preparation) in disaster management. Data sources utilized in these studies and specific data acquisition and integration techniques for COVID-19 are also summarized. Furthermore, open issues and future directions for data-driven pandemic management are discussed.

Human Mobility-based Individual-level Epidemic Simulation Platform

COVID-19 has spread worldwide, and over 140 million people have been confirmed infected, over 3 million people have died, and the numbers are still increasing dramatically. The consensus has been reached by scientists that COVID-19 can be transmitted in an airborne way, and human-to-human transmission is the primary cause of the fast spread of COVID-19. Thus, mobility should be restricted to control the epidemic, and many governments worldwide have succeeded in curbing the spread by means of control policies like city lockdowns. Against this background, we propose a novel fine-grained transmission model based on realworld human mobility data and develop a platform that helps the researcher or governors to explore the possibility of future development of the epidemic spreading and simulate the outcomes of human mobility and the epidemic state under different epidemic control policies. The proposed platform can also support users to determine potential contacts, discover regions with high infectious risks, and assess the individual infectious risk. The multi-functional platform aims at helping the users to evaluate the effectiveness of a regional lockdown policy and facilitate the process of screening and more accurately targeting the potential virus carriers.

Data-Driven In-Crisis Community Identification for Disaster Response and Management

Since 2019, the world has been seriously impacted by the global pandemic, COVID-19, with millions of people adversely affected. This is coupled with a trend in which the intensity and frequency of natural disasters such as hurricanes, wildfires, and earthquakes have increased over the past decades. Larger and more diverse communities have been negatively influenced by these disasters and they might encounter crises socially and/or economically, further exacerbated when the natural disasters and pandemics co-occurred. However, conventional disaster response and management rely on human surveys and case studies to identify these in-crisis communities and their problems, which might not be effective and efficient due to the scale of the impacted population. In this paper, we propose to utilize the data-driven techniques and recent advances in artificial intelligence to automate the in-crisis community identification and improve its scalability and efficiency. Thus, immediate assistance to the in-crisis communities can be provided by society and timely disaster response and management can be achieved. A novel framework of the in-crisis community identification has been presented, which can be divided into three subtasks: (1) community detection, (2) in-crisis status detection, and (3) community demand and problem identification. Furthermore, the open issues and challenges toward automated in-crisis community identification are discussed to motivate future research and innovations in the area.

The use of e-commerce and the COVID-19 outbreak: A panel data analysis in Japan

This study analyzes the changes in Japanese consumers' psychological intentions toward the use of e-commerce and the corresponding reasons before and after the COVID-19 outbreak using panel data. Several insights emerge on changes in consumers’ fundamental behaviors. First, the share of e-commerce use differs across the types of goods. Grocery goods showed a significant increase immediately after the COVID-19 outbreak. In contrast, machinery/PC applications and book/DVD/software showed only a slight increase immediately after the outbreak. However, only a slight decrease was observed during the third survey. Overall, consumers recognized the importance of e-commerce immediately after the pandemic, with no subsequent significant decline. Second, there may be a positive relationship between the time spent at home and the importance of e-commerce One reason is that an increase in stay-at-home duration decreases the opportunity to shop at retail stores. Consequently, trips to stores decreased during the initial spread of COVID-19. The modes of transportation for shopping and in the living area (infection status) were not significant factors of e-commerce usage. Finally, if consumers recognize its usefulness, they continued to consider e-commerce as important. However, their attitudes toward e-commerce improved after the COVID-19 outbreak because they wanted to avoid infection risks and follow social distancing and safety protocols. © 2021 Elsevier Ltd

Countrywide Origin-Destination Matrix Prediction and Its Application for COVID-19

Modeling and predicting human mobility are of great significance to various application scenarios such as intelligent transportation system, crowd management, and disaster response. In particular, in a severe pandemic situation like COVID-19, human movements among different regions are taken as the most important point for understanding and forecasting the epidemic spread in a country. Thus, in this study, we collect big human GPS trajectory data covering the total 47 prefectures of Japan and model the daily human movements between each pair of prefectures with time-series Origin-Destination (OD) matrix. Then, given the historical observations from past days, we predict the countrywide OD matrices for the future one or more weeks by proposing a novel deep learning model called Origin-Destination Convolutional Recurrent Network (ODCRN). It integrates the recurrent and 2-dimensional graph convolutional components to deal with the highly complex spatiotemporal dependencies in sequential OD matrices. Experiment results over the entire COVID-19 period demonstrate the superiority of our proposed methodology over existing OD prediction models. Last, we apply the predicted countrywide OD matrices to the SEIR model, one of the most classic and widely used epidemic simulation model, to forecast the COVID-19 infection numbers for the entire Japan. The simulation results also demonstrate the high reliability and applicability of our countrywide OD prediction model for a pandemic scenario like COVID-19. © 2021, Springer Nature Switzerland AG.