COVID-19: Challenges to GIS with Big Data

The outbreak of the 2019 novel coronavirus disease(COVID-19)has caused more than 100,000 people infected and thousands of deaths.Currently,the number of infections and deaths is still increasing rapidly.COVID-19 seriously threatens human health,production,life,social functioning and international relations.In the fight against COVID-19,Geographic Information Systems(GIS)and big data technologies have played an important role in many aspects,including the rapid aggregation of multi-source big data,rapid visualization of epidemic information,spatial tracking of confirmed cases,prediction of regional transmission,spatial segmentation of the epidemic risk and prevention level,balancing and management of the supply and demand of material resources,and socialemotional guidance and panic elimination,which provided solid spatial information support for decision-making,measures formulation,and effectiveness assessment of COVID-19 prevention and control.GIS has developed and matured relatively quickly and has a complete technological route for data preparation,platform construction,model construction,and map production.However,for the struggle against the widespread epidemic,the main challenge is finding strategies to adjust traditional technical methods and improve speed and accuracy of information provision for social management.At the data level,in the era of big data,data no longer come mainly from the government but are gathered from more diverse enterprises.As a result,the use of GIS faces difficulties in data acquisition and the integration of heterogeneous data,which requires governments,businesses,and academic institutions to jointly promote the formulation of relevant policies.At the technical level,spatial analysis methods for big data are in the ascendancy.Currently and for a long time in the future,the development of GIS should be strengthened to form a data-driven system for rapid knowledge acquisition,which signifies ts that GIS should be used to reinforce the social operation parameterization of models and methods,especially when providing support for social management.

A social computing method for energy safety

Information and communication technologies enable the transformation of traditional energy systems into cyber-physical energy systems(CPESs),but such systems have also become popular targets of cyberattacks.Currently,available methods for evaluating the impacts of cyberattacks suffer from limited resilience,efficacy,and practical value.To mitigate their potentially disastrous consequences,this study suggests a two-stage,discrepancy-based optimization approach that considers both preparatory actions and response measures,integrating concepts from social computing.The proposed Kullback-Leibler divergence-based,distributionally robust optimization(KDR)method has a hierarchical,two-stage objective function that incorporates the operating costs of both system infrastructures(e.g.,energy resources,reserve capacity)and real-time response measures(e.g.,load shedding,demand-side management,electric vehicle charging station management).By incorporating social computing principles,the optimization framework can also capture the social behavior and interactions of energy consumers in response to cyberattacks.The preparatory stage entails day-ahead operational decisions,leveraging insights from social computing to model and predict the behaviors of individuals and communities affected by potential cyberattacks.The mitigation stage generates responses designed to contain the consequences of the attack by directing and optimizing energy use from the demand side,taking into account the social context and preferences of energy consumers,to ensure resilient,economically efficient CPES operations.Our method can determine optimal schemes in both stages,accounting for the social dimensions of the problem.An original disaster mitigation model uses an abstract formulation to develop a risk-neutral model that characterizes cyberattacks through KDR,incorporating social computing techniques to enhance the understanding and response to cyber threats.This approach can mitigate the impacts more effectively than several existing methods,even with limited data availability.To extend this risk-neutral model,we incorporate conditional value at risk as an essential risk measure,capturing the uncertainty and diverse impact scenarios arising from social computing factors.The empirical results affirm that the KDR method,which is enriched with social computing considerations,produces resilient,economically efficient solutions for managing the impacts of cyberattacks on a CPES.By integrating social computing principles into the optimization framework,it becomes possible to better anticipate and address the social and behavioral aspects associated with cyberattacks on CPESs,ultimately improving the overall resilience and effectiveness of the system’s response measures.

AI for science: Predicting infectious diseases

The global health landscape has been persistently challenged by the emergence and re-emergence of infectious diseases.Traditional epidemiological models,rooted in the early 2oth century,have provided foundational in-sights into disease dynamics.However,the intricate web of modern global interactions and the exponential growth of available data demand more advanced predictive tools.This is where AI for Science(AI4S)comes into play,offering a transformative approach by integrating artificial intelligence(Al)into infectious disease pre-diction.This paper elucidates the pivotal role of AI4s in enhancing and,in some instances,superseding tradi-tional epidemiological methodologies.By harnessing AI's capabilities,AI4S facilitates real-time monitoring,sophisticated data integration,and predictive modeling with enhanced precision.The comparative analysis highlights the stark contrast between conventional models and the innovative strategies enabled by AI4S.In essence,Al4S represents a paradigm shift in infectious disease research.It addresses the limitations of traditional models and paves the way for a more proactive and informed response to future outbreaks.As we navigate the complexities of global health challenges,Al4S stands as a beacon,signifying the next phase of evolution in disease prediction,characterized by increased accuracy,adaptability,and efficiency.

Constructing public health evidence knowledge graph for decision-making support from COVID-19 literature of modelling study

The needs of mitigating COVID-19 epidemic prompt policymakers to make public health-related decision under the guidelines of science.Tremendous unstructured COVID-19 publications make it challenging for policymakers to obtain relevant evidence.Knowledge graphs(KGs)can formalize unstructured knowledge into structured form and have been used in supporting decision-making recently.Here,we introduce a novel framework that can ex-tract the COVID-19 public health evidence knowledge graph(CPHE-KG)from papers relating to a modelling study.We screen out a corpus of 3096 COVID-19 modelling study papers by performing a literature assessment process.We define a novel annotation schema to construct the COVID-19 modelling study-related IE dataset(CPHIE).We also propose a novel multi-tasks document-level information extraction model SS-DYGIE++based on the dataset.Leveraging the model on the new corpus,we construct CPHE-KG containing 60,967 entities and 51,140 rela-tions.Finally,we seek to apply our KG to support evidence querying and evidence mapping visualization.Our SS-DYGIE++(SpanBERT)model has achieved a F1 score of 0.77 and 0.55 respectively in document-level entity recognition and coreference resolution tasks.It has also shown high performance in the relation identification task.With evidence querying,our KG can present the dynamic transmissions of COVID-19 pandemic in different countries and regions.The evidence mapping of our KG can show the impacts of variable non-pharmacological interventions to COVID-19 pandemic.Analysis demonstrates the quality of our KG and shows that it has the potential to support COVID-19 policy making in public health.

Adaptively temporal graph convolution model for epidemic prediction of multiple age groups

Introduction:Multivariate time series prediction of infectious diseases is significant to public health,and the deep learning method has attracted increasing attention in this research field.Material and methods:An adaptively temporal graph convolution(ATGCN)model,which leams the contact patterns of multiple age groups in a graph-based approach,was proposed for COVID-19 and influenza prediction.We compared ATGCN with autoregressive models,deep sequence learning models,and experience-based ATGCN models in short-term and long-term prediction tasks.Results:Results showed that the ATGCN model performed better than the autoregressive models and the deep sequence learning models on two datasets in both short-term(12.5%and 10%improvements on RMSE)and longterm(12.4%and 5%improvements on RMSE)prediction tasks.And the RMSE of ATGCN predictions fluctuated least in different age groups of COVID-19(0.029±0.003)and influenza(0.059±0.008).Compared with the Ones-ATGCN model or the Pre-ATGCN model,the ATGCN model was more robust in performance,with RMSE of 0.0293 and 0.06 on two datasets when horizon is one.Discussion:Our research indicates a broad application prospect of deep learning in the field of infectious disease prediction.Transmission characteristics and domain knowledge of infectious diseases should be further applied to the design of deep learning models and feature selection.Conclusion:The ATGCN model addressed the multivariate time series forecasting in a graph-based deep learning approach and achieved robust prediction on the confirmed cases of multiple age groups,indicating its great potentials for exploring the implicit interactions of multivariate variables.

Control strategies and their effects on the COVID-19 pandemic in 2020 in representative countries

COVID-19 is the most severe pandemic globally since the 1918 influenza pandemic.Effectively responding to this once-in-a-century global pandemic is a worldwide challenge that the international community needs to jointly face and solve.This study reviews and discusses the key measures taken by major countries in 2020 to fight against COVID-19,such as lockdowns,social distancing,wearing masks,hand hygiene,using Fangcang shelter hospitals,large-scale nucleic acid testing,close-contacts tracking,and pandemic information monitoring,as well as their prevention and control effects.We hope it can help improve the efficiency and effectiveness of pandemic prevention and control in future.