Link Prediction on Complex Networks: An Experimental Survey.

Complex networks have been used widely to model a large number of relationships. The outbreak of COVID-19 has had a huge impact on various complex networks in the real world, for example global trade networks, air transport networks, and even social networks, known as racial equality issues caused by the spread of the epidemic. Link prediction plays an important role in complex network analysis in that it can find missing links or predict the links which will arise in the future in the network by analyzing the existing network structures. Therefore, it is extremely important to study the link prediction problem on complex networks. There are a variety of techniques for link prediction based on the topology of the network and the properties of entities. In this work, a new taxonomy is proposed to divide the link prediction methods into five categories and a comprehensive overview of these methods is provided. The network embedding-based methods, especially graph neural network-based methods, which have attracted increasing attention in recent years, have been creatively investigated as well. Moreover, we analyze thirty-six datasets and divide them into seven types of networks according to their topological features shown in real networks and perform comprehensive experiments on these networks. We further analyze the results of experiments in detail, aiming to discover the most suitable approach for each kind of network.

Link Prediction on Complex Networks: An Experimental Survey

Complex networks have been used widely to model a large number of relationships. The outbreak of COVID-19 has had a huge impact on various complex networks in the real world, for example global trade networks, air transport networks, and even social networks, known as racial equality issues caused by the spread of the epidemic. Link prediction plays an important role in complex network analysis in that it can find missing links or predict the links which will arise in the future in the network by analyzing the existing network structures. Therefore, it is extremely important to study the link prediction problem on complex networks. There are a variety of techniques for link prediction based on the topology of the network and the properties of entities. In this work, a new taxonomy is proposed to divide the link prediction methods into five categories and a comprehensive overview of these methods is provided. The network embedding-based methods, especially graph neural network-based methods, which have attracted increasing attention in recent years, have been creatively investigated as well. Moreover, we analyze thirty-six datasets and divide them into seven types of networks according to their topological features shown in real networks and perform comprehensive experiments on these networks. We further analyze the results of experiments in detail, aiming to discover the most suitable approach for each kind of network.

Dynamic change of IgG titers in hospitalized patients with coronavirus disease 2019: a group-based trajectory analysis

Objective To study the dynamic trajectories of quantitative immunoglobulin G (IgG) titers of hospitalized coronavirus disease 2019 (COVID-19) patients and reveal the immune process of the organism after infection. Methods The clinical data and quantitative IgG titers at different time points of hospitalized COVID-19 patients in Wuhan Huoshenshan Hospital and Guanggu Branch of Maternity and Child Healthcare Hospital of Hubei Province from Feb. 5 to Apr. 15, 2020 were retrospectively analyzed. Group-based trajectory modeling was used to identify the subgroups from time-series data of patients’ antibody titers, and then the clinical characteristics and outcomes were compared among these trajectory groups. Results Totally, 734 patients who met the criteria were included. Three IgG trajectory groups were identified from the antibody data: group 1 (consistently low group, 60 cases［8.17%］), group 2 (moderate group, 38 cases ［5.18%］) and group 3 (high group, 636 cases［86.65%］). The hospitalization days and the virus clearance time of patients in the 3 groups were significantly different (both P＜0.001), those in group 1 were the shortest, while the all-cause mortality and disease deterioration rate had no significant difference in the 3 groups (both P＞0.05). Conclusion Patients with different IgG antibody developmental trajectories may have heterogeneous prognosis and immune process. Patients with consistently higher longitudinal antibody titers may require more medical attention.

Calcium channel blockers improve prognosis of patients with coronavirus disease 2019 and hypertension.

BACKGROUND: Hypertension is considered an important risk factor for the coronavirus disease 2019 (COVID-19). The commonly anti-hypertensive drugs are the renin-angiotensin-aldosterone system (RAAS) inhibitors, calcium channel blockers (CCBs), and beta-blockers. The association between commonly used anti-hypertensive medications and the clinical outcome of COVID-19 patients with hypertension has not been well studied. METHODS: We conducted a retrospective cohort study that included all patients admitted with COVID-19 to Huo Shen Shan Hospital and Guanggu District of the Maternal and Child Health Hospital of Hubei Province, Wuhan, China. Clinical and laboratory characteristics were extracted from electronic medical records. Hypertension and anti-hypertensive treatment were confirmed by medical history and clinical records. The primary clinical endpoint was all-cause mortality. Secondary endpoints included the rates of patients in common wards transferred to the intensive care unit and hospital stay duration. Logistic regression was used to explore the risk factors associated with mortality and prognosis. Propensity score matching was used to balance the confounders between different anti-hypertensive treatments. Kaplan-Meier curves were used to compare the cumulative recovery rate. Log-rank tests were performed to test for differences in Kaplan-Meier curves between different groups. RESULTS: Among 4569 hospitalized patients with COVID-19, 31.7% (1449/4569) had a history of hypertension. There were significant differences in mortality rates between hypertensive patients with CCBs (7/359) and those without (21/359) (1.95% vs. 5.85%, risk ratio [RR]: 0.32, 95% confidence interval [CI]: 0.13-0.76, χ2 = 7.61, P = 0.0058). After matching for confounders, the mortality rates were similar between the RAAS inhibitor (4/236) and non-RAAS inhibitor (9/236) cohorts (1.69% vs. 3.81%, RR: 0.43, 95% CI: 0.13-1.43, χ2 = 1.98, P = 0.1596). Hypertensive patients with beta-blockers (13/340) showed no statistical difference in mortality compared with those without (11/340) (3.82% vs. 3.24%, RR: 1.19, 95% CI: 0.53-2.69, χ2 = 0.17, P = 0.6777). CONCLUSIONS: In our study, we did not find any positive or negative effects of RAAS inhibitors or beta-blockers in COVID-19 patients with hypertension, while CCBs could improve prognosis.

Mining long-COVID symptoms from Reddit: characterizing post-COVID syndrome from patient reports.

Our objective was to mine Reddit to discover long-COVID symptoms self-reported by users, compare symptom distributions across studies, and create a symptom lexicon. We retrieved posts from the /r/covidlonghaulers subreddit and extracted symptoms via approximate matching using an expanded meta-lexicon. We mapped the extracted symptoms to standard concept IDs, compared their distributions with those reported in recent literature and analyzed their distributions over time. From 42 995 posts by 4249 users, we identified 1744 users who expressed at least 1 symptom. The most frequently reported long-COVID symptoms were mental health-related symptoms (55.2%), fatigue (51.2%), general ache/pain (48.4%), brain fog/confusion (32.8%), and dyspnea (28.9%) among users reporting at least 1 symptom. Comparison with recent literature revealed a large variance in reported symptoms across studies. Temporal analysis showed several persistent symptoms up to 15 months after infection. The spectrum of symptoms identified from Reddit may provide early insights about long-COVID.

Preventive behaviours and family inequalities during the COVID-19 pandemic: a cross-sectional study in China.

BACKGROUND: The coronavirus disease 2019 (COVID-19) pandemic is an international public health threat, and people's participation in disease-related preventive behaviours is the key to controlling infectious diseases. This study aimed to assess the differences in adopting preventive behaviours among populations to explore potential individual and household factors and inequalities within families. METHODS: This online survey was conducted in April 2020. The directional stratified convenient sampling method was used to select 4704 participants from eight provinces in eastern, central, and western China. The questionnaire included demographic information, household variables, and five target prevention behaviours. The chi-squared test, binary multilevel model, and Mantel-Haenszel hierarchical analysis were used for data analysis in the study. RESULTS: Approximately 71.2% of the participants had appropriate outdoor prevention, and 32.9% of the participants had indoor protection in place. Sharing behaviours (P < 0.001) and education level (P < 0.001) were positively associated with adopting preventive measures. The inhibiting effect of household crowding and stimulating effect of high household income on preventive behaviours were determined in this study. Household size was negatively associated with living area (ß = -0.057, P < 0.05) and living style (ß = -0.077, P < 0.05). Household income was positively associated with age (ß = 0.023, P < 0.05), and relationship with friends (ß = 0.053, P < 0.05). Vulnerable groups, such as older adults or women, are more likely to have inadequate preventive behaviours. Older adults (OR = 1.53, 95% CI 1.09-2.15), women (OR = 1.37, 95% CI 1.15-1.64), and those with more than 2 suspected symptoms (OR = 1.85, 95% CI 1.07-3.19) were more likely to be affected by the inhibiting effect of household crowding, while the stimulating effect of high household income was limited in these groups. CONCLUSIONS: Inequalities in COVID-19 prevention behaviours exist between families and inadequate adoption of prevention by vulnerable groups are noteworthy. This study expands the research perspective by emphasizing the role of household factors in preventive behaviours and by focusing on family inequalities. The government should use traditional media as a platform to enhance residents' public health knowledge. Targeted additional wage subsidies, investments in affordable housing, financial support for multigenerational households, and temporary relocation policies may deserve more attention. Communities could play a critical role in COVID-19 prevention.

A Randomized, Single-blind, Group sequential, Active-controlled Study to evaluate the clinical efficacy and safety of α-Lipoic acid for critically ill patients with coronavirus disease 2019(COVID-19) (preprint)

Object: To evaluate the clinical efficacy and safety of -Lipoic acid (ALA) for critically ill patients with coronavirus disease 2019 (COVID-19). Methods: A randomized, single-blind, group sequential, active-controlled trial was performed at JinYinTan Hospital, Wuhan, China. Between February 2020 and March 2020, 17 patients with critically ill COVID-19 were enrolled in our study. Eligible patients were randomly assigned in a 1:1 ratio to receive either ALA (1200 mg/d, intravenous infusion) once daily plus standard care or standard care plus equal volume saline infusion (placebo) for 7 days. All patients were monitored within the 7 days therapy and followed up to day 30 after therapy. The primary outcome of this study was the Sequential Organ Failure Estimate (SOFA) score, and the secondary outcome was the all-cause mortality within 30 days. Result: Nine patients were randomized to placebo group and 8 patients were randomized to ALA group. SOFA score was similar at baseline, increased from 4.3 to 6.0 in the placebo group and increased from 3.8 to 4.0 in the ALA group (P=0.36) after 7 days. The 30-day all-cause mortality tended to be lower in the ALA group (3/8, 37.5%) compared to that in the placebo group (7/9, 77.8%, P=0.09). Conclusion: In our study, ALA use is associated with lower SOFA score increase and lower 30-day all-cause mortality as compared with the placebo group. Although the mortality rate was two-folds higher in placebo group than in ALA group, only borderline statistical difference was evidenced due to the limited patient number. Future studies with larger patient cohort are warranted to validate the role of ALA in critically ill patients with COVID-19. Keywords: Pneumonia; COVID-19; SARS-CoV-2 ; -Lipoic acid