Impacts of information about COVID-19 on pig farmers'production willingness and behavior:Evidence from China

This paper examines the impacts of information about COVID-19 on pig farmers'production willingness by using endorsement experiments and follow-up surveys conducted in 2020 and 2021 in China.Our results show that,first,farmers were less willing to scale up production when they received information about COVID-19.The information in 2020 that the second wave of COVID-19 might occur without a vaccine reduced farmers'willingness to scale up by 13.4%,while the information in 2021 that COVID-19 might continue to spread despite the introduction of vaccine reduced farmers'willingness by 4.4%.Second,farmers whose production was affected by COVID-19 were considerably less willing to scale up,given the access to COVID-19 information.Third,farmers'production willingness can predict their actual production behavior.

COVID-19 and information and communication technology in radiation oncology:A new paradigm

Due to coronavirus disease 2019 pandemic caused by severe acute respiratory syndrome coronavirus 2,there has been a major reallocation of resources that has impacted the treatment of many diseases,including cancer.The growing use of information and communication technologies(ICT),together with a new approach to work aimed at ensuring the safety of health care professionals and patients alike,has allowed us to maintain the quality of care while ensuring biosecurity.The application of ICT to health care(eHealth)aims to significantly improve the quality,access to,and effectiveness of medical care.In fact,the expanded use of ICT has been recognized as a key,cost-effective priority for health care by the World Health Organisation.The medical speciality of radiation oncology is closely linked to technology and as a consequence of coronavirus disease 2019,ICT has been widely employed by radiation oncologists worldwide,providing new opportunities for interaction among professionals,including telemedicine and e-learning,while also minimizing treatment interruptions.Future research should concentrate on this emerging paradigm,which offers new opportunities,including faster and more diverse exchange of scientific knowledge,organizational improvements,and more efficient workflows.Moreover,these efficiencies will allow professionals to dedicate more time to patient care,with a better work-life balance.In the present editorial,we discuss the opportunities provided by these digital tools,as well as barriers to theirimplementation,and a vision of the future.

Learning Mathematics Online Using Information Technology Tools during COVID-19 in Kosove

The COVID-19 pandemic has brought significant challenges to higher education worldwide. Due to the COVID-19 pandemic, e-learning has begun to be widely used and applied in the teaching and learning processes. However, learning under technological circumstances has proven not always to be a proper solution in education. A highlight challenge, in this regard, is considered to be learning Mathematics online. While some support its positive impact, others greatly oppose it by arguing that neither teaching nor learning has proven successful. Thus, this study examines Kosovo selected universities to see the effectiveness of learning Mathematics online as a case study. Further, it compares the online and traditional learning methods and explores how teachers in higher education in Kosova Universities apply and integrate technology into learning mathematics. This study employed a methodology encompassing questionnaires for students. The results show that students are not overall satisfied with learning Mathematics online leading to the conclusion that online learning is not an effective educational method for learning Mathematics.

COVID-19 Times: Impact on Information Generation and Data Sharing

At the beginning of 2020, human activities were interrupted by a new virus, identified as SARS-CoV-2, which causes COVID-19 disease. The scientific area was no exception: for a certain period, researchers around the world were forced to leave their laboratories and work remotely. There was a global necessity for finding alternatives focused on generating knowledge and publishing data, so repositories of scientific information, such as databases, represented strong support. In the specific case of life sciences, different strategies allowed rapid compilation of data and its sharing worldwide. Therefore, in this work, the impact of the SARS-CoV-2 pandemic on the amount of peer-reviewed and published papers during COVID-19 times was analyzed along with the role of databases. Our results pointed out that an increase in the number of papers belonging to different knowledge fields took place, with the medical field being the most significant. On the other hand, the complete genome of the new virus was sequenced, and repositories were created with sufficient data for monitoring, preventing, and controlling its dissemination. This was the case for the generation of vaccines in addition to potential candidates for drugs against COVID-19. However, although in 2021, vaccines allowed us to gradually return to our activities, databases and the generation of other repositories remain a key point for facing new strains and adapting to a new reality. Finally, this paper discusses joint efforts to tackle the obstacles of the pandemic, not only from a medical but also from the point of view regarding the fight against misinformation.

Effect of Online Social Networking on Emotional Status and Its Interaction with Offline Reality during the Early Stage of the COVID-19 Pandemic in China

Background:During the early stages of the COVID-19 pandemic in China,social interactions shifted to online spaces due to lockdowns and social distancing measures.As a result,the impact of online social networking on users’emotional status has become stronger than ever.This study examines the association between online social networking and Internet users’emotional status and how offline reality affects this relationship.Methods:The study utilizes cross-sectional online survey data(n=3004)and Baidu Migration big data from the first 3 months of the pandemic.Two dimensions of online networking are measured:social support and information sources.Results:First,individuals’online social support(β=0.16,p<0.05)and information sources(β=0.08,p<0.01)are both positively associated to their emotional status during the epidemic.Second,these positive associations are moderated by social status and provincial pandemic control interventions.With regards to the moderation effect of social status,the constructive impact of information sources on emotional well-being is more pronounced among individuals from vulnerable groups compared to those who are not.With regard to the moderation effect of provincial interventions,online social support has the potential to alleviate the adverse repercussions of high rates of confirmed COVID-19 cases and strict lockdown measures while simultaneously augmenting the favorable effects of recovery.Conclusion:The various dimensions of social networking exert distinct effects on emotional status through diverse mechanisms,all of which must be taken into account when designing and adapting pandemic-control interventions.

Information on Mapping and Vaccination Data in a Locality System

During the pandemic, technological innovation provided a platform with a range of uses, including in the healthcare industry. Technology is currently being used in vaccination drives run by many governments across the world to help spread vaccines quickly and efficiently. The technology makes healthcare personnel more effective at their professions and greatly raises the standard of service in the industry. The researchers undertook this study to create a suitable and long-lasting immunization database with a mapping method to give a better perspective of the immunization status. To gather essential information for this study, the researchers spoke with the local health officer in the targeted area. The obtained data then served as the basis for the system’s capabilities and features, becoming the target problems addressed by the developers. The investigation found that the majority of procedures and interactions are carried out manually and recorded on an unprotected, antiquated Excel spreadsheet. The researchers’ technology also shows to be a superior way to deal with the problems and difficulties while making their health-related transactions and operations quicker, safer, and much more effective.

Evidence-based approach to providing informed consent for hip fracture surgery during the COVID-19 era

BACKGROUND Hip fractures are the most common reason for inpatient orthopaedic trauma admission.Urgent surgical intervention for hip fractures has remained a clinical priority throughout the coronavirus disease 2019(COVID-19)pandemic.Despite this,there is a paucity of clinical guidance addressing the informed consent process for hip fracture surgery in COVID-19 positive patients.This is of paramount medicolegal importance in a high-risk patient population.AIM To quantify the additional perioperative risks for COVID-19 positive patients undergoing hip fracture surgery and provide clinicians with an evidence-based framework to establish an informed consent process.METHODS Two hundred and fifty nine consecutive patients undergoing surgical intervention for hip fractures in four hospitals in the United Kingdom were recruited.51 patients were confirmed positive for COVID-19.Predefined outcomes were analyzed over a 30-d postoperative period.COVID-19 positive and COVID-19 negative patients were compared after adjustment for confounding factors.RESULTS COVID-19 positive patients had more intensive care admissions(27%vs 5%,P<0.001),longer inpatient stays(median 23 d vs 9 d,P<0.001)and a higher 30-d mortality(29%vs 10%,P=0.001)than COVID-19 negative patients.Postoperative complications were evident in 74.5%of COVID-19 positive patients.35.3%of COVID-19 positive patients suffered postoperative lower respiratory tract infections with 13.7%developing acute respiratory distress syndrome(ARDS)and 9.8%experiencing symptomatic thromboembolic events.CONCLUSION The COVID-19 pandemic has created uncertainty in the medical community worldwide and poses unique challenges in providing informed consent for surgery.COVID-19 positive patients undergoing hip fracture surgery should be consented for the additional risk of postoperative complications(including lower respiratory tract infection,ARDS,deep vein thrombosis and pulmonary embolism),increased requirement for intensive care admission,longer inpatient stay and higher risk of mortality.Further,clinicians must be transparent about the potential for unknown risks as research into the long-term surgical outcomes of COVID-19 positive patients continues to evolve.

Policy and research frame of the coronavirus disease 2019(COVID-19)pandemic:reflections on urban informality

How has the informality of urban slums exposed a gap in policy formulation and research questions in the wake of the coronavirus disease 2019(COVID-19)pandemic?This paper seeks to identify the appropriate questions and policy frame that would assist future researchers and policymakers on the subject of pandemics in densely populated urban settlements.The authors argue that the nexus between asking the appropriate questions and developing appropriate policy response measures during a pandemic can significantly impact the outcome of the response.The paper examines how the government of Kenya's response to the COVID-19 pandemic reveals a deep-rooted socio-economic and cultural inequality when"blanket"policies are adopted without taking into consideration the tnique dynamics characterizing the society.The findings show that the effectiveness of implementing COVID-19 containment policies such as lockdowns,the cession of movement,working from home,distance learning,and social distancing are affected by other factors such as the nature of jobs,one's income levels,where someone lives,cultural beliefs,access to water,sanitation,intemet,and medical facilities.This means that a significant number of people within the society experience a double tragedy from the pandemic and impact of government response measures.Yet most of the existing literature has focused on the causes,spread,and impact of the pandemic on health institutions,economies,and public health with little emphasis on the impact on policy measures especially on the vulnerable segments of the society.This paper,therefore,looks at the question of how the various public health intervention strategies disrupt or construct the livelihood of the already complex informal settlement.It provides policymakers and researchers with a number of questions that can frame policy and research during a pandemic with important consideration to urban informality.

Blockchain and Artificial Intelligence Applications to Defeat COVID-19 Pandemic

The rapid emergence of novel virus named SARS-CoV2 and unchecked dissemination of this virus around the world ever since its outbreak in 2020,provide critical research criteria to assess the vulnerabilities of our current health system.The paper addresses our preparedness for the management of such acute health emergencies and the need to enhance awareness,about public health and healthcare mechanisms.In view of this unprecedented health crisis,distributed ledger and AI technology can be seen as one of the promising alternatives for fighting against such epidemics at the early stages,and with the higher efficacy.At the implementation level,blockchain integration,early detection and avoidance of an outbreak,identity protection and safety,and a secure drug supply chain can be realized.At the opposite end of the continuum,artificial intelligence methods are used to detect corona effects until they become too serious,avoiding costly drug processing.The paper explores the application of blockchain and artificial intelligence in order to fight with COVID-19 epidemic scenarios.This paper analyzes all possible newly emerging cases that are employing these two technologies for combating a pandemic like COVID-19 along with major challenges which cover all technological and motivational factors.This paper has also discusses the potential challenges and whether further production is required to establish a health monitoring system.

Street Stall Economy in China in the COVID-19 Era:Dilemmas and the International Experience of Promoting the Normalization of Street Stall Economy

Compared with those major policies that need to be practiced over the years,the street stall economy is more like a special means after the epidemic,resulting in a“short and brilliant”heat.Nevertheless,the street stall economy revives is facing several dilemmas.This paper reveals the dilemma of the prosperity and development of the stall economy before and after the epidemic,followed by the international experience and enlightenment of promoting the normalization of street stall economy,ranging from street vendor’s legal status and road administrative promotion to street food safety and environmental protection.To sum up,employment is the foundation of people’s livelihood and the source of wealth,hence,stall economy plays an indispensable role to create a win-win working world and promote the formation of a sustainable economic.

Information overload:How hot topics distract from news——COVID-19 spread in the US

Information dissemination and the associated change of individual behavior can significantly slow the spread of an epidemic.However,major social events which attract public attention will disturb information spread and affect epidemic transmission in ways that have not been readily quantified.We investigate the interplay between disease spreading and diseaserelated information dissemination in a two-layer network.We employ the SIR-UAU model with a time dependent coefficient to denote information dissemination.We found that major social events are equivalent to perturbations of information dissemination in certain time intervals and will consequently weaken the effect of information dissemination,and increase prevalence of infection.Our simulation results agree well with the trends observed from real-world data sets.We found that two specific major events explain the trend of the coronavirus epidemic in the US:the online propaganda and international agenda setting of Donald Trump early in 2020 and the 2020 US Presidential Election.

Visual method of analyzing COVID-19 case information using spatio-temporal objects with multi-granularity

Coronavirus disease 2019(COVID-19)is continuing to spread globally and still poses a great threat to human health.Since its outbreak,it has had catastrophic effects on human society.A visual method of analyzing COVID-19 case information using spatio-temporal objects with multi-granularity is proposed based on the officially provided case information.This analysis reveals the spread of the epidemic,from the perspective of spatio-temporal objects,to provide references for related research and the formulation of epidemic prevention and control measures.The case information is abstracted,descripted,represented,and analyzed in the form of spatio-temporal objects through the construction of spatio-temporal case objects,multi-level visual expressions,and spatial correlation analysis.The rationality of the method is verified through visualization scenarios of case information statistics for China,Henan cases,and cases related to Shulan.The results show that the proposed method is helpful in the research and judgment of the development trend of the epidemic,the discovery of the transmission law,and the spatial traceability of the cases.It has a good portability and good expansion performance,so it can be used for the visual analysis of case information for other regions and can help users quickly discover the potential knowledge this information contains.

Information fusion for future COVID-19 prevention:continuous mechanism of big data intelligent innovation for the emergency management of a public epidemic outbreak

Information fusion is very effective and necessary to respond to a public epidemic outbreak such as COVID-19.Big data intelligent,as a product of information fusion,plays an important role in the prevention and control of COVID-19.The continuous mechanism of big data intelligent innovation(BDII)is fundamental to effectively prevent and control a public epidemic outbreak.In this study,the continuous mechanism of BDII was fused into a complex network,and a three-dimensional collaborative epidemic prevention model was constructed.Furthermore,adiabatic elimination principle was applied to explore the order parameter of the continuous mechanism.Finally,empirical analysis was conducted based on three-stage epidemic prevention strategies to reveal the effect of continuous epidemic prevention under different big data intelligent emergency management policy levels.The results of this study are as follows.Through the mutual influence and coupling of the subsystems,the continuous mechanism of BDII can be realized to manage a public epidemic outbreak emergency.The big data intelligent subsystem is integrated into the subsystems of public epidemic outbreak management and science and technology innovation.The big data intelligent emergency management policies play a positive role in the overall BDII for the continuous epidemic prevention of a public epidemic outbreak.The convention of BDII transformation is the continuous mechanism of BDII as the order parameter of a public epidemic outbreak.In the early stage of epidemic prevention,the convention is excessively pursued,while the neglect of BDII configuration is not conducive to the long-term collaborative governance of a public epidemic outbreak.The study provides practical guidelines for the formulation of fusion innovation policies,application of big data intelligent,and theoretical basis for the emergency management of a public epidemic outbreak in the medical field.

How do digital technologies improve supply chain resilience in the COVID-19 pandemic?Evidence from Chinese manufacturing firms

Digital technologies (DTs) can assist businesses in coping with supply chain (SC) disruptions caused by unpredictability, such as pandemics. However, the current knowledge of the relationship between DTs and supply chain resilience (SCR) is insufficient. This study draws on information processing theory to develop a serial mediation model to address this deficiency. We analyze a sample set consisting of 264 Chinese manufacturers. The empirical results reveal that digital supply chain platforms (DSCPs), as well as supply chain traceability (SCT) and supply chain agility (SCA), fully mediate the favorable association between DTs and SCR. Specifically, the four significant indirect paths indicated that firms can improve SCR only if they use DTs to directly or indirectly improve SCT and SCA (through DSCPs). Our study contributes to the literature on resilience by examining the possible mechanism of mediation through which DTs influence SCR. The findings also offer essential insights for firms to modify their digital strategies and thrive in a turbulent environment.

Time to forge ahead:The Internet of Things for healthcare

Situated at the intersection of technology and medicine,the Internet of Things(IoT)holds the promise of addressing some of healthcare's most pressing challenges,from medical error,to chronic drug shortages,to overburdened hospital systems,to dealing with the COVID-19 pandemic.However,despite considerable recent technological advances,the pace of successful implementation of promising IoT healthcare initiatives has been slow.To inspire more productive collaboration,we present here a simple—but surprisingly underrated—problemoriented approach to developing healthcare technologies.To further assist in this effort,we reviewed the various commercial,regulatory,social/cultural,and technological factors in the development of the IoT.We propose that fog computing—a technological paradigm wherein the burden of computing is shifted from a centralized cloud server closer to the data source—offers the greatest promise for building a robust and scalable healthcare IoT ecosystem.To this end,we explore the key enabling technologies that underpin the fog architecture,from the sensing layer all the way up to the cloud.It is our hope that ongoing advances in sensing,communications,cryptography,storage,machine learning,and artificial intelligence will be leveraged in meaningful ways to generate unprecedented medical intelligence and thus drive improvements in the health of many people.

Quantitative spatiotemporal impact of dynamic population density changes on the COVID-19 pandemic in China’s mainland

The coronavirus disease 2019(COVID-19)and its mutant viruses are still wreaking global havoc over the last two years,but the impact of human activity on the transmission of the pandemic is difficult to ascertain.Estimating human dynamic spatiotemporal distribution can help in our understanding of how to mitigate COVID-19 spread,which can help in maintaining urban health within a county and between counties within a country.This distribution can be computed using the Volunteered Geographic Information(VGI)of the citizens in conjunction with other variables,such as climatic conditions,and used to analyze how human’s daily density distribution quantitatively affects COVID-19 transmission.Based on the estimated population density,when the population density increases daily by 1 person/km^(2) in a county or prefectural-level administrative unit with an average size of 26,000 km^(2),the county would have an additional 3.6 confirmed cases and 0.054 death cases after 5 days,which is the illness onset time for a new COVID-19 case.After 14 days,which is the maximum incubation period of the COVID-19 virus,there would be 5 new confirmed cases and 0.092 death cases.However,in neighboring regions,there can be 0.96 fewer people infected with COVID-19 on average per day as a result of strong intervention of local and neighboring authorities.The primary innovation and contribution are that this is the first quantitative assessment of the impacts of dynamic population density on the COVID-19 pandemic.Additionally,the direct and indirect effects of the impact are estimated using spatial panel models.The models that control the unobserved factors improve the reliability of the estimation,as validated by random experiments and the use of the Baidu migration dataset.

A study on predicting crisis information dissemination in epidemic-level public health events

Crisis information dissemination plays a key role in the development of emergency responses to epidemic-level public health events.Therefore,clarifying the causes of crisis information dissemination and making accurate predictions to effectively control such situations have attracted extensive attention.Based on media richness theory and persuasion theory,this study constructs an index system of crisis information dissemination impact factors from two aspects:the crisis information publisher and the published crisis information content.A multi-layer perceptron is used to analyze the weight of the index system,and the prediction is transformed into a pattern classification problem to test crisis information dissemination.In this study,COVID-19 is considered a representative event.An experiment is conducted to predict the crisis information dissemination of COVID-19 in two megacities.Data related to COVID-19 from these two megacities are acquired from the well-known Chinese social media platform Weibo.The experimental results show that not only the identity but also the social influence of the information publisher has a significant impact on crisis information dissemination in epidemic-level public health events.Furthermore,the proposed model achieves more than 95%test accuracy,precision rate,recall value and f1-score in the prediction task.The study provides decision-making support for government departments and a guide for correctly disseminating crisis information and public opinion during future epidemic-level public health events.

Early Warning and Monitoring of Coronavirus Disease 2019 Using Baidu Search Index and Baidu Information Index in Guangxi,China

Coronavirus disease 2019(COVID-19)is an emerging infectious disease,and it is important to detect early and monitor the disease trend for policymakers to make informed decisions.We explored the predictive utility of Baidu Search Index and Baidu Information Index for early warning of COVID-19 and identified search keywords for further monitoring of epidemic trends in Guangxi.A time-series analysis and Spearman correlation between the daily number of cases and both the Baidu Search Index and Baidu Information Index were performed for seven keywords related to COVID-19 from January 8 to March 9,2020.The time series showed that the temporal distributions of the search terms“coronavirus,”“pneumonia”and“mask”in the Baidu Search Index were consistent and had 2 to 3 days'lead time to the reported cases;the correlation coefficients were higher than 0.81.The Baidu Search Index volume in 14 prefectures of Guangxi was closely related with the number of reported cases;it was not associated with the local GDP.The Baidu Information Index search terms“coronavirus”and“pneumonia”were used as frequently as 192,405.0 and 110,488.6 per million population,respectively,and they were also significantly associated with the number of reported cases(rs>0.6),but they fluctuated more than for the Baidu Search Index and had 0 to 14 days'lag time to the reported cases.The Baidu Search Index with search terms“coronavirus,”“pneumonia”and“mask”can be used for early warning and monitoring of the epidemic trend of COVID-19 in Guangxi,with 2 to 3 days'lead time.

面向突发公共卫生事件的相关信息发布特征分析

突发公共卫生事件期间相关信息发布的机构呈多样化特征,包括政府、政府间国际组织、媒体、自媒体等。文章旨在揭示相关部门或机构信息发布的类型与特点,并提出进一步完善的对策和建议。为此,采用案例研究的方法,选取新冠肺炎疫情期间国家卫健委及湖北卫健委、美国疾控中心(CDC)和新加坡卫生部(MOH)官网、以及世界卫生组织(WHO)的相关疫情信息发布加以分析。研究发现,面对突发公共卫生事件,疫情通报、健康科普、防控动态、媒体新闻是这些部门和机构信息发布的主要类型。信息发布具有疫情通报模式固定、通报内容随疫情发展而演变等。研究表明,信息发布应加强对公众信息需求的考量、构建与公众更有效的双向沟通机制、并提供多样化的疫情发布格式等。同时,面向突发公共卫生事件的信息发布应及时、规范、形成多管齐下的动态发布机制、建立与公众双向沟通的渠道、加强对公众信息需求的调研和关注,确保疫情信息发布的精准,有效满足公众的信息需求。

新型冠状病毒肺炎疫情下我国互联网诊疗的发展

梳理我国互联网诊疗的相关政策,综述我国互联网诊疗发展的现状和趋势,挖掘目前互联网诊疗行业可能存在的信息技术、医疗保险支付、行业监管、发展方向4个方面的主要问题,针对这些问题提出相关的政策建议。