scholarly journals An Overview of Nursing Practice and Optimization Strategies for Epidemic Infectious Diseases — Taking COVID-19 As an Example

2021 ◽  
Vol 2 (1) ◽  
Author(s):  
Jingxin Tao ◽  
Dongni Qiu ◽  
Lijuan Liu

This paper combines the author's experience in the nursing practice of patients with COVID-19 at the Xiaogan Central Hospital in Hubei Province, and analyses the response strategies for epidemic infectious diseases. It is hoped that this paper will provide some guidance and help in the response to epidemic infectious diseases.

2020 ◽  
Vol 2 (1) ◽  
pp. 01-11
Author(s):  
Bin Zhao

Background: An infectious disease caused by a novel coronavirus called COVID-19 has raged across the world since December 2019. The novel coronavirus first appeared in Wuhan, China, and quickly spread to Asia and now many countries around the world are affected by the epidemic. The deaths of many patients, including medical staff, caused social panic, media attention, and high attention from governments and world organizations. Today, with the joint efforts of the government, the doctors and all walks of life, the epidemic in Hubei Province has been brought under control, preventing its spread from affecting the lives of the people. Because of its rapid spread and serious consequences, this sudden novel coronary pneumonia epidemic has become an important social hot spot event. Through the analysis of the novel coronary pneumonia epidemic situation, we can also have a better understanding of sudden infectious diseases in the future, so that we can take more effective response measures, establish a truly predictable and provide reliable and sufficient information for prevention and control model. Methods: We establish different models according to the different developments of the epidemic situation, different time points, and different response measures taken by the government. To be specific, during the period of 2020.1.23-2020.2.7, the traditional SIR model is adopted; during the period of 2020.2.8-2020.3.30, according to the scientific research results, it was considered that the novel coronary pneumonia has a latent period, so in the later phase of epidemic development, the government has effectively isolated patients, thus we adopt the SEIQR model accordingly. During the period of 2020.3.31-2020.5.16, because more asymptomatic infected people were found, we use the SEIQLR model to fit. Finally, through a SEIR simulator, considering the susceptible number, the latent number, the infected number, the cured number, death number and other factors, we simulate the change of various numbers of people from the beginning to the next 180 days of novel coronary pneumonia. Findings: The results based on the analysis of differential equations and kinetic models show that through the prediction of the model established in the first phase, the epidemic situation of novel coronary pneumonia in Hubei Province was controlled at the end of March, which is in line with the actual situation. The rest of Hubei province, except for Wuhan, lifted control of the departure channel from 0:00 am on March 25, and Wuhan was also unblocked on April 8. Through the establishment of the second-phase model, it is found that the epidemic situation will reach its peak in mid-February. For example, the quarantine admission of the hospital declined after mid-February, which is inseparable from the measures to build square cabin hospitals in early February so that more and more patients can be admitted. The model established in the third phase shows that the epidemic had been completely controlled by the end of May, which is also in line with the reality. Because in mid-May, the Wuhan government conducted a nucleic acid test on all the citizens to screen for asymptomatic infected persons to fundamentally control the spread of novel coronary pneumonia. Interpretation: Hubei Province, as the center of the initial outbreak of novel coronary pneumonia, people were forced to be isolated at home during the Spring Festival, the most important Chinese holiday, and the whole society was in a state of suspension of work and study. The Chinese government had taken many measures in response to the epidemic, such as shutting down the city, vigorously building square cabin hospitals, and prohibiting people from gathering. At the beginning of May this year, the epidemic in Hubei Province was finally effectively controlled. For ordinary citizens, we should not cause unnecessary panic about the unknown novel coronavirus. Instead, we should fully understand and be familiar with this virus. In addition to the relevant medical knowledge, we should also understand the spread of infectious diseases through appropriate mathematical models. By mathematical models, we can understand the degree of harm of infectious diseases, when to control it, how to stop it, and use scientific views to reveal the original face of the novel coronavirus to the public without causing social panic.


Author(s):  
Qingxian Cai ◽  
Deliang Huang ◽  
Pengcheng Ou ◽  
Hong Yu ◽  
Zhibin Zhu ◽  
...  

2020 ◽  
Vol 71 (15) ◽  
pp. 818-824 ◽  
Author(s):  
Weiming Tang ◽  
Huipeng Liao ◽  
Gifty Marley ◽  
Zaisheng Wang ◽  
Weibin Cheng ◽  
...  

Abstract Background Evaluating whether an infectious disease has reached a turning point is important for planning additional intervention efforts. This study aimed to analyze the changing patterns and the tempogeographic features of the coronavirus disease 2019 (COVID-19) epidemic in China, to provide further evidence for real-time responses. Methods Daily data on COVID-19 cases between 31 December 2019 and 26 February 2020 were collected and analyzed for Hubei and non-Hubei regions in China. Observed trends for new and cumulative cases were analyzed through joinpoint regression analysis. Spatial analysis was applied to show the geographic distribution and changing patterns of the epidemic. Results By 26 February 2020, 78 630 confirmed COVID-19 cases had been reported in China. In Hubei, an increasing trend (slope = 221) was observed for new cases between 24 January and 7 February 2020, after which a decline commenced (slope = −868). However, as the diagnosis criteria changed, a sudden increase (slope = 5530) was observed on 12 February, which sharply decreased afterward (slope = −4898). In non-Hubei regions, the number of new cases increased from 20 January to 3 February and started to decline afterward (slope = −53). The spatial analysis identified Chongqing, Guangzhou, Shenzhen, Changsha, Nanchang, Wenzhou, Shanghai, Xinyang, Jining, and Beijing as the hotspots outside of Hubei Province in China. Conclusions The joinpoint regression analysis indicated that the epidemic might be under control in China, especially for regions outside of Hubei Province. Further improvement in the response strategies based on these new patterns is needed.


Allergy ◽  
2020 ◽  
Vol 75 (7) ◽  
pp. 1742-1752 ◽  
Author(s):  
Qingxian Cai ◽  
Deliang Huang ◽  
Pengcheng Ou ◽  
Hong Yu ◽  
Zhibin Zhu ◽  
...  

2021 ◽  
Vol 3 (4) ◽  
pp. 01-08
Author(s):  
Abdul Alhassan

Background: In our healthcare setting transmission of hepatitis B is a big challenge to handle for both patients and healthcare providers especially those in frequent contact with blood. Objectives: To determine Hepatitis B knowledge, attitude, and vaccination status among nurses of Tamale Central Hospital. Methods: This study was conducted using a descriptive cross-sectional survey. Data analysis was done with SPSS version 20. Bivariate analysis was done using chi-square to determine the relationships. Results: The study recorded a response rate of 92.5%. Majority (59.6%) had high, 36.4% had moderate and 4.0% had low knowledge hepatitis B. Hepatitis knowledge level was associated with: age X2 (4) = 17.789, P= 0.001, sex, X2(2) = 13.203, P = 0.001, educational level, X2(6) = 17.552, P = 0.007, nursing category, X2(4) = 19.226, P = 0.001, and duration of nursing practice X2(2) = 19.492, P ≤ 0.001. About 42.9% had positive attitude toward hepatitis B prevention and attitude level towards hepatitis B was associated with: marital status X2(1) = 11.090, P = 0.001, residential address X2 (2) = 11.411, P = 0.003 and duration of nursing practice X2(1) = 4.769, P = 0.029. About 84.8% of nurses started vaccination against hepatitis B and 77.3% completed at least three doses of the vaccination. The only factor with a statistically significant relationship with vaccination completeness status was respondent marital status X2(1) = 8.063, P = 0.005. Conclusion: Nurses' knowledge of hepatitis B was very good. And more than half of the nurses had a negative attitude towards hepatitis B prevention. In terms of vaccination more than three-fourth of them started and completed three doses of hepatitis B vaccination.


1991 ◽  
Vol 34 (3) ◽  
pp. 671-678 ◽  
Author(s):  
Joan E. Sussman

This investigation examined the response strategies and discrimination accuracy of adults and children aged 5–10 as the ratio of same to different trials was varied across three conditions of a “change/no-change” discrimination task. The conditions varied as follows: (a) a ratio of one-third same to two-thirds different trials (33% same), (b) an equal ratio of same to different trials (50% same), and (c) a ratio of two-thirds same to one-third different trials (67% same). Stimuli were synthetic consonant-vowel syllables that changed along a place of articulation dimension by formant frequency transition. Results showed that all subjects changed their response strategies depending on the ratio of same-to-different trials. The most lax response pattern was observed for the 50% same condition, and the most conservative pattern was observed for the 67% same condition. Adult response patterns were most conservative across condition. Differences in discrimination accuracy as measured by P(C) were found, with the largest difference in the 5- to 6-year-old group and the smallest change in the adult group. These findings suggest that children’s response strategies, like those of adults, can be manipulated by changing the ratio of same-to-different trials. Furthermore, interpretation of sensitivity measures must be referenced to task variables such as the ratio of same-to-different trials.


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