A Review of Big Data Analytics on Post-COVID Health Issues

New technology developments and its processes generate large quantities of data every day. Organizations are faced a number of problems for managing these big data. IoT (Internet of Things) devices are now used for collecting data rapidly and safely in high capacity storage devices and internet clouds. Big data has many characteristics including unstructured which does not conform to a data model. In an organization about 85% data is in unstructured format. The semi-structured data does not conform to a data model but it has some structure. The structured data is in an organized form and can be easily used by a computer program. From the year 2019 a new big data formation happened with COVID-19 pandemic in the world society. Corona virus and COVID-19 pandemic have fully affected the people and this pandemic situation has frightened the people worldwide. It has to find excellent big data analytical methods to control and solve the future problems related to this pandemic. This paper presents big data quality dimensions, big data analytics in COVID-19 pandemic, and physical and mental post-COVID health issues. © 2022 IEEE.

Risk of severe COVID-19 infection in persons with diabetes during the first and second waves in Denmark: A nationwide cohort study.

Objective: Coronavirus disease-2019 (COVID-19) increases risk of hospitalization and death in diabetes and diabetes-related conditions. We examined the temporal trends in COVID-19-related hospitalization and mortality in the total Danish population by diabetes and diabetes-related conditions in the two first waves of COVID-19 in Denmark. Materials and methods: We identified all persons with diabetes in the whole Danish population using national registries. COVID-19-related risks of hospitalization and death were assessed using Cox regression analysis in wave 1 (1 March-31 August 2020) and wave 2 (1 September 2020-28 February 2021) of the pandemic for persons with (n=321,933) and without diabetes (n=5,479,755). Analyses were stratified according to status of hypertension, obesity, cardiovascular and microvascular disease. Results: The cumulative incidence of COVID-19 hospitalization increased from wave 1 to wave 2 in both persons without (from 4 to 10 in 10,000) and with diabetes (from 16 to 54 per 10,000). The relative risk of hospitalization, however, increased more in patients with diabetes compared to persons without (age-, sex- and co-morbidity-adjusted HR [aHR] 1.40 (95% CI 1.27, 1.55) versus 1.76 (1.65, 1.87), p<0.001 for interaction with wave). The mortality rate, according to the whole population, increased similarly in persons without and with diabetes from wave 1 to wave 2 (from 0.63 to 1.5 versus from 4.3 to 10 in 10,000; aHR 1.65; 1.34, 2.03 and 1.64; 1.43, 1.88). However, when mortality was restricted to the hospitalized population, the crude mortality fell from 26.8% to 19.6% in persons with diabetes, while only a minor decrease was seen in persons without diabetes (from 16.7% to 15.5%). Conclusion: The risk of COVID-19-related hospitalization increased more in persons with than without diabetes from wave 1 to wave 2 of the COVID-19 pandemic in the Danish population. However, mortality according to the whole population did not change, due to reduced mortality among hospitalized persons with diabetes.

Symptom Criteria for COVID-19 Testing of Heath Care Workers.

BACKGROUND: Symptom criteria for COVID-19 testing of heath care workers (HCWs) limitations on testing availability have been challenging during the COVID-19 pandemic. An evidence-based symptom criteria for identifying HCWs for testing, based on the probability of positive COVID-19 test results, would allow for a more appropriate use of testing resources. METHODS: This was an observational study of outpatient COVID-19 testing of HCWs. Prior to testing, HCWs were asked about the presence of 10 symptoms. Their responses were then compared to their subsequent pharyngeal swab COVID-19 polymerase chain reaction test results. These data were used to derive and evaluate a symptom-based testing criteria. RESULTS: A total of 961 HCWs were included in the analysis, of whom 225 (23%) had positive test results. Loss of taste or smell was the symptom with the largest positive likelihood ratio (3.33). Dry cough, regardless of the presence or absence of other symptoms, was the most sensitive (74%) and the least specific (32%) symptom. The existing testing criteria consisting of any combination of one or more of three symptoms (fever, shortness of breath, dry cough) was 93% sensitive and 9% specific (area unce the curve [AUC] = 0.63, 95% confidence interval [CI] = 0.59 to 0.67). The derived testing criteria consisting of any combination of one or more of two symptoms (fever, loss of taste or smell) was 89% sensitive and 48% specific (AUC = 0.75, 95% CI = 0.71 to 0.78). The hybrid testing criteria consisting of any combination of one or more of four symptoms (fever, shortness of breath, dry cough, loss of taste or smell) was 98% sensitive and 8% specific (AUC = 0.77, 95% CI = 0.73 to 0.80). CONCLUSION: An evidence-based approach to COVID-19 testing that at least includes fever and loss of taste or smell should be utilized when determining which HCWs should be tested.