ABSTRACT
This paper collects real-time epidemic data released by the World Health Organization and various Internet authorities, predict the development of the epidemic through the classical model (SIR model) in the field of communication disease, bring historical data into the model, verify the parameters of the model and establish a new model, compare multiple sets of data, obtain the system that is closest to the real data, and speculate on the development direction and turning point of the subsequent NEW CROWN epidemic. The use of scientific and technical means to reason and analyze the overall situation of the new crown epidemic situation provides a solid backing for the prevention and control of the epidemic. © 2022 IEEE.
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Aim: To evaluate the improvement of glycemic control and stress adaptation in patients with GDM by mobile phone WeChat management during novel coronavirus pneumonia. Methods: In this study, 75 women with GDM were included, of whom 35 were included in mobile WeChat group management as the GDM-M group and 40 as the GDM group. Results: After mobile WeChat group management for 4 weeks, E and NE were lower. MDA was lower, and SOD was higher. HOMA-IR was lower. E, NE, and cortisol were related to HOMA-IR positively, MDA was positively related to HOMA-IR, and SOD was negatively related to HOMA-IR. E and cortisol were positively related to MDA but negatively related to SOD. Conclusion: The stress adaptation disorder and insulin resistance in patients with GDM who have completed mobile WeChat group management can be improved during novel coronavirus pneumonia. Mobile WeChat management played a positive role in improving the insulin resistance of women with GDM under special circumstances, which may reduce the risk of maternal and fetal complications.
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Background and Aims: The hypercoagulability occurring in COVID-19 patients is detected only by Rotational thromboelastometry (ROTEM). However, the benefit of performing ROTEM in the management of disease and predicting the outcome of COVID-19 patients is yet to be established. Material and Methods: The data of 23 critically ill and 11 stable COVID-19 adult patients were extracted from the hospital information system admitted between July and August 2020 and patient charts and analyzed retrospectively. The critically ill patients were divided as a survivor and non-survivor groups. The Intrinsic pathway part of ROTEM (INTEM) and Fibrinogen part of ROTEM (FIBTEM) were performed on day 0 for both critically ill and stable patients, and on day 10 for critically ill patients. The statistical package for social science (SPSS) version 26 was used for statistical analysis. Results: The median FIBTEM amplitude at 5 min (A5) and maximum clot firmness (MCF) were elevated in both stable and critically ill patients (24 vs 27 mm, P = 0.46 and 27.5 vs 40 mm, P = 0.011) with a significant difference in FIBTEM MCF. But there was no significant difference between number of survivors and non-survivors with FIBTEM MCF >25 at day 0 and day 10. Conclusion: The Hypercoagulability state as detected by ROTEM parameters at day 0 and day 10 had no association with the outcome (mortality) of critically ill COVID-19 patients. Hence it cannot be used as a prognostic test. The increasing age, comorbidities and D-dimer values were associated with a poor prognosis in COVID-19 patients.
ABSTRACT
Since late 2019, the beginning of coronavirus disease 2019 (COVID-19) pandemic, transmission dynamics models have achieved great development and were widely used in predicting and policy making. Here, we provided an introduction to the history of disease transmission, summarized transmission dynamics models into three main types: compartment extension, parameter extension and population-stratified extension models, highlight the key contribution of transmission dynamics models in COVID-19 pandemic: estimating epidemiological parameters, predicting the future trend, evaluating the effectiveness of control measures and exploring different possibilities/scenarios. Finally, we pointed out the limitations and challenges lie ahead of transmission dynamics models.