Reliable Knowledge Discovery from Social Media Data in Health Crisis in Developing Countries (preprint)

Social media is a barometer to anticipate sentiment of the public about the state of affairs and ongoing pandemic engaged an additional user base who are confined to their stations. COVID-19 startled the world and the crisis exacerbates in the absence of sufficient data for policy making. The data from social media and a timely analysis can provide sufficient statistics for decision-making. This study explores Twitter data to discover knowledgeable statistics on public sentiments about COVID-19 vaccination in developing countries. The study inspects data collected from two extremely populated developing countries: India and Pakistan. Support Vector Machine (SVM) classifier achieves 74.3% accuracy on the manually labeled dataset. Furthermore, the sentiment analysis is correlated with other indigenous factors like regional literacy rate and COVID-19 calamities in the time interval. It is observed that the negative to positive sentiments correlates with a lower to higher regional literacy rate and a higher COVID-19 intensity causes positive sentiments towards vaccination. The correlations of results with indigenous factors may help to advocate the devised strategies to the right audience and social media knowledge discovery with machine learning techniques may help to recover from data scarcity challenges in a medical emergency like COVID-19 in developing countries. Please note: Abbreviations should be introduced at the first mention in the main text – no abbreviations lists. Suggested structure of main text (not enforced) is provided below.

Mortality in Patients of COVID-19 Infection: Biochemical Markers and its Cut-off Values for Predicting Outcome.

OBJECTIVE: To determine the efficacy and cut-off values of C-reactive protein (CRP), lactate dehydrogenase (LDH), serum ferritin, and D-dimer for predicting mortality of COVID-19 infection. STUDY DESIGN: Observational study. PLACE AND DURATION OF STUDY: Department of Medicine, Jinnah Hospital, Lahore from January to May 2021. METHODOLOGY: Serum CRP, LDH, ferritin, and D-dimer were measured in patients with moderate to severe COVID-19 infection at admission. Patients were followed for in-hospital disease outcome. ROC curve was used to determine area under curve (AUC) and cut-off values of biomarkers, followed by multi-variate analysis by logistic regression. RESULTS: In 386 patients, male to female ratio was 1.47/1 (230/156); and mean age was 54.03 ± 16.2 years. Disease was fatal in 135 (35%) patients. AUC for mortality was 0.730 for LDH, 0.737 for CRP, 0.747 for ferritin and 0.758 for D-dimer. Mortality was higher with LDH ≥400 U/ml, Odds Ratio (OR) 5.37 (95% CI 3.01-9.57: p = 0.001), CRP ≥30 ng/L, OR 4.30 (95% CI 2.11-8.74: p = <0.001), serum ferritin ≥200 ng/ml, OR 4.13 (95% CI 1.05-16.2: p = 0.02), and D-dimer ≥400 ng/ml, OR 2.72 (95% CI 1.06-7.01: p = 0.03) with 2 log likelihood of 131.54 for predicting disease outcome with 71.7% accuracy in multi-variate analysis. CONCLUSION: Elevated serum CRP, LDH, ferritin and D-dimer are associated with higher mortality in patients of COVID-19 infection. Serum CRP ≥30ng/ml, LDH ≥400 U/L, ferritin ≥200 ng/ml and D-dimer ≥400 ng/ml can predict fatal outcome in COVID-19 patients. Key Words: C-reactive protein (CRP), COVID-19 infection, D-dimer, Ferritin, Lactate dehydrogenase (LDH), Mortality.

The reduction in clinical and surgical exposure of trainees during COVID-19 and its impact on their training.

OBJECTIVE: To assess the impact of coronavirus disease on surgical training. Methods: The cross-sectional study was conducted at the General Surgery Department of Liaquat National Hospital, Karachi, from August 2019 to May 2020, and comprised surgical trainees from year 1 to 4. The subjects were interviewed and inquired about their opinion regarding the impact of coronavirus disease on their training. Data was prospectively collected in two equal phases of 5 months each, separating the phases on the basis of the application of preventive measures and changes relating to coronavirus disease. Data of cases from log books was divided into major and minor cases. RESULTS: Of the 24 surgical trainees available, 18(75%) participated; 12(66.6%) females and 6(33.3%) males. There was a significant difference between the two phases, with the number of surgical case going down drastically in the second phase (p=0.005), affecting the training process. CONCLUSIONS: Considering the ongoing pandemic, it may be worthwhile to look into the possibility of increasing the duration of training.

Cytokine storm and use of anakinra in a patient with COVID-19.

We present a case of a 50-year-old man with COVID-19 infection and acute respiratory distress syndrome as a result of a cytokine storm and use of anakinra, an interleukin 1-receptor antagonist that is normally used in the treatment of autoinflammatory disorders in adult patients. We saw a reduction in oxygen requirement and improvements in inflammatory markers and ferritin. Although we cannot determine its clinical efficacy from one case study, it may have a positive effect on the proinflammatory state that is associated with cytokine storm in COVID-19 infection.