Combating Misinformation/ Disinformation in Online Social Media: A Multidisciplinary View

Recently, the viral propagation of mis/disinformation has raised significant concerns from both academia and industry. This problem is particularly difficult because on the one hand, rapidly evolving technology makes it much cheaper and easier to manipulate and propagate social media information. On the other hand, the complexity of human psychology and sociology makes the understanding, prediction and prevention of users' involvement in mis/disinformation propagation very difficult. This themed series on "Multi-Disciplinary Dis/Misinformation Analysis and Countermeasures" aims to bring the attention and efforts from researchers in relevant disciplines together to tackle this challenging problem. In addition, on October 20th, 2021, and March 7th 2022, some of the guest editorial team members organized two panel discussions on "Social Media Disinformation and its Impact on Public Health During the COVID-19 Pandemic," and on "Dis/Misinformation Analysis and Countermeasures - A Computational Viewpoint." This article summarizes the key discussion items at these two panels and hopes to shed light on the future directions.

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.

Psychometric Properties of the Symptoms of Infection with Coronavirus-19: A Patient-Reported Outcome Measure for COVID-19 Signs and Symptoms

Background. The Symptoms of Infection with Coronavirus-19 (SIC), a patient reported outcome (PRO) measure, was developed to assess COVID-19 signs and symptoms. Qualitative and cross-sectional studies demonstrated its content validity and preliminary psychometric properties. This study provides additional evidence on the reliability, responsiveness, known-group validity, and meaningful change thresholds of the SIC using methods aligned with regulatory guidance and best practices. Methods. Data were from ENSEMBLE-2, a multicenter, randomized, doubleblind, placebo-controlled phase 3 trial to assess the efficacy and safety of Ad26.COV2.S for the prevention of SARS-CoV-2 infections in adults (aged 18+). The SIC was used in the trial to evaluate COVID-19 signs and symptoms and the Patient Global Impression of Severity (PGIS) was used as an anchor for validation. Intra-class correlations (ICCs) and Cronbach's alphas were computed to evaluate the test-retest reliability and internal consistency, and analyses of variance (ANOVAs) were performed to assess the known-group validity of the SIC. Responsiveness was evaluated using PGIS as an anchor variable and a 1- or 2-point improvement in PGIS was used to estimate the meaningful change thresholds of the SIC. Results. 183 participants with polymerase chain reaction (PCR) confirmed moderate to severe/critical COVID-19 were included (mean +/- SD age: 51.5 +/- 14.8 y;female: 44%;White 65%). ICCs showed strong test-retest reliabilities for most SIC domains (.60 and above). The internal consistency reliability of the SIC had a Cronbach's alpha > .70 for all but one domain (Neurological). Statistically significant differences (p values < 0.05) for the different PGIS severity levels were found for all but one domain (Sensory), supporting known-group validity. All domains showed responsiveness based on changes (improvement and worsening) in PGIS, supporting the ability of the SIC to detect changes in COVID-19 signs and symptoms. Based on mean changes in the PGIS, estimated meaningful change thresholds for SIC domains ranged from -.36 to -2.11. Conclusion. These results, based on data from ENSEMBLE-2, build upon prior cross-sectional analyses and provide additional supportive psychometric evidence on the SIC.

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.

Easy-to-Deploy Combined Nasal/Throat Swab Robot With Sampling Dexterity and Resistance to External Interference

Robots have been used extensively in the battle against the COVID-19 pandemic since its outbreak. One prominent direction is the use of robots for swab sampling, which not only solves the shortage of medical staffs, but also prevents them from being infected during face-to-face sampling. However, a massive deployment of sampling robots is still not achievable due to their high costs, safety concerns, deployment complexity, and so on. In this letter, we propose a flexible, safe, and easy-to-deploy swab robot in a compact bench-top system. The robot can perform nasal/throat swab sampling tasks as dexterous as a human manual operation. The bio-mimetic rigid interior and soft exterior design guarantee the sampling robot with both flexibility and safety. Furthermore, the integration of 3-D fiber Bragg grating (FBG) based shape sensor and multi-axis force sensor may enhance the control performance. A dedicated constrained compliance control (CCC) algorithm was developed to tackle the unexpected interactions during sampling, which ensures the validity and safety of the sampling under disturbance. Various experiments are conducted to validate our system and prove its feasibility, flexibility, high safety, and efficiency for both nasal/throat swab sampling tasks. The proposed system is promising to be massive duplicated for robotic swab sampling.

Bio-inspired Soft (BIS) Hand for Tele-operated COVID-19 Oropharyngeal (OP) Swab Sampling

The lasting difficulty imposed by the COVID-19 pandemic has made profound challenges to our daily lives. The prominent task to contend with the COVID-19 is the diagnosis of the SARS-CoV-2 virus. The timely diagnosis guides further anti-infection measurements such as effective quarantine and dedicated therapy. Existing diagnosis majorly relies on manual face-to-face Oropharyngeal (OP) and Nasopharyngeal (NP) sampling, which results in high infection risk and heavy working burden to medical staff. Robotic sampling is a promising solution for this challenge because robots are intrinsically immune to viruses and working without fatigue. Following this target, we demonstrate our novel teleoperated OP swab sampling system with excellent safety guarantees, quick deployability, and task efficacy. A bio-inspired soft hand integrated with a soft wrist is developed, which can pick and manipulate the disposable swab as dexterous as a human hand. Stereo imaging is provided to the operator by a terminal binocular camera. The design, working mechanism, and control of our sampling system are discussed. Results show that our proposed robotic sampling system promises safe and effective OP swab sampling to protect medical staff and convenient the sampling process.

[Epidemiological characteristics of imported COVID-19 cases in Beijing].

Objective: To analyze the epidemiological characteristics of imported COVID-19 cases and the effect of vaccination on virus load and disease severity of the cases in Beijing. Methods: The data of the imported COVID-19 cases in Beijing were collected from the National Notifiable Infectious Disease Reporting System of China Information for Disease Control and Prevention and Epidemiology investigation. The data were processed and analyzed by Excel 2010 and SPSS 22.0. Results: From June 1 to September 30, 2021, a total of 171 imported COVID-19 cases were reported in Beijing, of which 66.67% (114/171) were asymptomatic. The cases were mainly from the Philippines, the United Arab Emirates, the United Kingdom and the Russian Federation, accounting for 67.84% (116/171). The male to female ratio of the cases was 2∶1 (114∶57). The median age M (Q1, Q3) of the cases was 28 (23, 36) years. The cases of Chinese accounted for 80.12% (137/171). The sequencing of the whole genome of the virus in 47 imported COVID-19 cases showed that the proportion of Delta variant was 76.60% (36/47). The COVID-19 vaccination coverage rate in the cases was 60.82% (104/171), but the full vaccination coverage rate was 53.80% (92/171). In the imported COVID-19 cases, 13.53% (23/170) were screened to be SARS-CoV-2 nucleic acid positive on the day when they arrived in Beijing, and all the cases were positive for 2019-nCoV nucleic acid within 28 days. The severity of the disease was higher in the unvaccinated group than in the partially vaccinated group and fully vaccinated group (P<0.001). In the unvaccinated group, there were 1 severe case and 1 critical case. The median Ct values M (Q1, Q3) of N gene and ORFlab gene in unvaccinated group were 32.51 (23.23, 36.06) and 32.78 (24.00, 36.38), respectively. There was no significant difference in the median of double-gene Ct value between the partially vaccinated group and the fully vaccinated group. Conclusions: During the study period, most of the imported COVID-19 cases in Beijing were asymptomatic. No matter vaccinated or not, the viral loads in the COVID-19 cases were similar, but the vaccination could reduce the severity of the disease.

Sibling relationship qualities during the COVID-19 pandemic: A brief report of protective effect

We explored child sibling relationship qualities during the COVID-19 pandemic in a Chinese setting. Using a web-based survey, 154 parents reported on their kindergarten children's (age range = 3 to 6 years) sibling relationships. The descriptive data and correlations analysis showed that the siblings had a good degree of interaction and acceptance. Regression analysis results revealed that the parents/guardians perceived significant differences in sibling relationship qualities. Specifically, female child siblings were more interactive and emotionally secure compared to male child siblings. This sex effect was stronger for female first born children. Further research is needed to examine the quality of sibling relationships in other critical life situations to assist in improving the quality of sibling relationships.

Understanding COVID-19 Public Sentiment Towards Public Health Policies Using Social Media Data

With the outbreak of COVID-19, an overabundance of information related to the virus was released through social media. The disunity amongst US states and the federal government has exacerbated the task of identifying conflicting guidelines and information unaffected by political polarization. Analyzing the public's response to public health policies provides insight into the progression of the pandemic in the US. We gather Twitter, Facebook, and Reddit data and conduct sentiment analysis to determine public sentiment in response to different COVID-19 policies. We found that users had more positive sentiments toward mask policies than to shelter-in-place and fall school reopening policies. Also, we found significant sentiment differences regarding school reopening and mask policies when comparing among several states. Finally, we found that swing states had more conflicting sentiments for policies within their states than did non-swing states. We recommend further research to determine which factors may influence these differences in sentiment.

COVID-19 prevention measures reduce dengue spread in Yunnan Province, China, but do not reduce established outbreak.

ABSTRACTThe COVID-19 pandemic and measures against it provided a unique opportunity to understand the transmission of other infectious diseases and to evaluate the efficacy of COVID-19 prevention measures on them. Here we show a dengue epidemic in Yunnan, China, during the pandemic of COVID-19 was dramatically reduced compared to non-pandemic years and, importantly, spread was confined to only one city, Ruili. Three key features characterized this dengue outbreak: (i) the urban-to-suburban spread was efficiently blocked; (ii) the scale of epidemic in urban region was less affected; (iii) co-circulation of multiple strains was attenuated. These results suggested that countermeasures taken during COVID-19 pandemic are efficient to prevent dengue transmission between cities and from urban to suburban, as well to reduce the co-circulation of multiple serotypes or genotypes. Nevertheless, as revealed by the spatial analysis, once the dengue outbreak was established, its distribution was very stable and resistant to measures against COVID-19, implying the possibility to develop a precise prediction method.

The Warning and Governance of Online Public Opinion Research

Scientific, timely and effective health communication is crucial for stabilizing public emotions and taking effective prevention and control actions during the crisis of 2019-nCoV. This article uses Qualitative Comparative Analysis (QCA) and takes the health rumors list during the epidemic as a research sample to determine 6 specific explanatory condition variables and analyze the combination factors that affect the spread of high-health rumors in health. From the perspective of public opinion, including evolution mechanism, generation mechanism, and early warning mechanism, it aims to propose the perfection and solution of network public opinion governance mechanism under public health emergencies.

Risk factors for disease progression in patients with mild to moderate coronavirus disease 2019-a multi-centre observational study.

OBJECTIVES: Since December 2019, the novel coronavirus disease 2019 (COVID-19) that emerged in Wuhan city has spread rapidly around the world. The risk for poor outcome dramatically increases once a patient progresses to the severe or critical stage. The present study aims to investigate the risk factors for disease progression in individuals with mild to moderate COVID-19. METHODS: We conducted a cohort study that included 1007 individuals with mild to moderate COVID-19 from three hospitals in Wuhan. Clinical characteristics and baseline laboratory findings were collected. Patients were followed up for 28 days for observation of disease progression. The end point was the progression to a more severe disease stage. RESULTS: During a follow up of 28 days, 720 patients (71.50%) had recovered or were symptomatically stable, 222 patients (22.05%) had progressed to severe disease, 22 patients (2.18%) had progressed to the critically ill stage and 43 patients (4.27%) had died. Multivariate Cox proportional hazards models identified that increased age (hazard ratio (HR) 2.56, 95% CI 1.97-3.33), male sex (HR 1.79, 95% CI 1.41-2.28), presence of hypertension (HR 1.44, 95% CI 1.11-1.88), diabetes (HR 1.82, 95% CI 1.35-2.44), chronic obstructive pulmonary disease (HR 2.01, 95% CI 1.38-2.93) and coronary artery disease (HR 1.83, 95% CI 1.26-2.66) were risk factors for disease progression. History of smoking was protective against disease progression (HR 0.56, 95% CI 0.34-0.91). Elevated procalcitonin (HR 1.72, 95% CI 1.02-2.90), urea nitrogen (HR 1.72, 95% CI 1.21-2.43), α-hydroxybutyrate dehydrogenase (HR 3.02, 95% CI 1.26-7.21) and D-dimer (HR 2.01, 95% CI 1.12-3.58) at baseline were also associated with risk for disease progression. CONCLUSIONS: This study identified a panel of risk factors for disease progression in individuals with mild to moderate COVID-19.