scholarly journals Factors Influencing Intentions to Care For Emerging Infectious Disease Patients among National and Public Hospitals Nurses

2021 ◽  
Vol 28 (1) ◽  
pp. 11-22
Author(s):  
Hea-Jin Moon ◽  
Ju Young Park
Keyword(s):  
Infectious Disease ◽  
Social Support ◽  
Infectious Diseases ◽  
Pearson Correlation ◽  
Emerging Infectious Diseases ◽  
Emerging Infectious Disease ◽  
Public Hospitals ◽  
Self Report ◽  
Moral Sensitivity ◽  
Epidemiological Characteristics

Purpose: The purpose of this study was to identify the effect of nurses' nursing professionalism, moral sensitivity, and social support on intention to care for patients with emerging infectious diseases.Methods: A structured self-report questionnaire was used to measure nursing professionalism, moral sensitivity, social support, and intention to care for patients with emerging infectious diseases. Data were collected from April 9~20, 2019. Participants were 200 nurse nurses working in national and public hospitals. Data were analyzed using Pearson correlation coefficients, and Multiple regression with the SPSS/WIN 24.0 program.Results: The perceived behavioral control (β=.48, p<.001), control beliefs (β=-.26, p<.001), moral sensitivity (β=.23, p<.001), normative beliefs (β=.17, p=.002), subjective norms (β=.17, p=.001), and attitude toward behavior (β=.10, p=.036) were a significant predictor of the intention to care for emerging infectious disease patients (Adj. R<sup>2</sup>=.65).Conclusion: In order to confidently carry out nursing activities for patients with emerging infectious diseases, sufficient education on the epidemiological characteristics of emerging infectious diseases must be provided and education programs developed and applied with simulation similar to those of actual care for emerging infectious disease patients.

scholarly journals Human factors in emerging infectious diseases

2021 ◽  
Vol 2 (1) ◽  
pp. 21-29 ◽  
Author(s):  
Scott B. Halstead
Keyword(s):  
Infectious Disease ◽  
Infectious Diseases ◽  
Human Factors ◽  
Emerging Infectious Diseases ◽  
Emerging Infectious Disease ◽  
Emerging Disease ◽  
Human Behaviour ◽  
Microbial World ◽  
Underlying Causes ◽  
Methods Of Control

When the underlying causes and mechanisms of emerging infectious disease problems are studied carefully, human behaviour is often involved. Even more often, the only methods of control or prevention available are to change human behaviour. Several major recent emerging disease problems can be cited. It is sometimes emphasized that it is human carelessness, human excesses, human ignorance or human habits of conquest or leisure which contribute directly to the biological niches that microorganisms are all too capable of exploiting. We must look at ourselves as the engines of microbial opportunism. It is not likely that we will ever conquer the microbial world;we must look instead to control the human factors that contribute to emergence.

scholarly journals Australian Biosecurity CRC for Emerging Infectious Diseases (AB-CRC)

2003 ◽  
Vol 24 (2) ◽  
pp. 38
Author(s):  
John S Mackenzie ◽  
Lisa Adams
Keyword(s):  
Infectious Disease ◽  
Infectious Diseases ◽  
Federal Government ◽  
Emerging Infectious Diseases ◽  
Geographic Range ◽  
Emerging Infectious Disease ◽  
Emerging Diseases ◽  
Economic Resources ◽  
Research Centre ◽  
Cooperative Research

The Australian Biosecurity Cooperative Research Centre for Emerging Infectious Disease (AB-CRC) was a successful applicant under the Federal Government?s 2002 CRC programme, and will be formally established from July 2003. The aim of the AB-CRC is to protect Australia?s health, livestock, wildlife and economic resources by developing new capabilities to monitor, assess, predict and respond to emerging and exotic disease threats which impact on national and regional biosecurity. Emerging diseases are defined as those which are novel, previously unrecognised diseases, or those which are increasing in incidence or geographic range. The threats may be natural, accidental (such as an infected traveller) or deliberate (as in bioterrorism).

scholarly journals DBatVir: the database of bat-associated viruses

Database ◽  
2014 ◽  
Vol 2014 ◽  
Author(s):  
Lihong Chen ◽  
Bo Liu ◽  
Jian Yang ◽  
Qi Jin
Keyword(s):  
Public Health ◽  
Infectious Disease ◽  
Infectious Diseases ◽  
Emerging Infectious Diseases ◽  
Emerging Infectious Disease ◽  
Additional Information ◽  
Zoonotic Viruses ◽  
User Friendly ◽  
Reservoir Hosts ◽  
Viral Sequences

Abstract Emerging infectious diseases remain a significant threat to public health. Most emerging infectious disease agents in humans are of zoonotic origin. Bats are important reservoir hosts of many highly lethal zoonotic viruses and have been implicated in numerous emerging infectious disease events in recent years. It is essential to enhance our knowledge and understanding of the genetic diversity of the bat-associated viruses to prevent future outbreaks. To facilitate further research, we constructed the database of bat-associated viruses (DBatVir). Known viral sequences detected in bat samples were manually collected and curated, along with the related metadata, such as the sampling time, location, bat species and specimen type. Additional information concerning the bats, including common names, diet type, geographic distribution and phylogeny were integrated into the database to bridge the gap between virologists and zoologists. The database currently covers &gt;4100 bat-associated animal viruses of 23 viral families detected from 196 bat species in 69 countries worldwide. It provides an overview and snapshot of the current research regarding bat-associated viruses, which is essential now that the field is rapidly expanding. With a user-friendly interface and integrated online bioinformatics tools, DBatVir provides a convenient and powerful platform for virologists and zoologists to analyze the virome diversity of bats, as well as for epidemiologists and public health researchers to monitor and track current and future bat-related infectious diseases. Database URL: http://www.mgc.ac.cn/DBatVir/

scholarly journals Infectious disease outbreak prediction using media articles with machine learning models

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Juhyeon Kim ◽  
Insung Ahn
Keyword(s):  
Infectious Disease ◽  
Infectious Diseases ◽  
Human Health ◽  
Emerging Infectious Diseases ◽  
Disease Outbreak ◽  
Emerging Infectious Disease ◽  
Health Conditions ◽  
News Article ◽  
Support Vector ◽  
Infectious Disease Outbreak

AbstractWhen a newly emerging infectious disease breaks out in a country, it brings critical damage to both human health conditions and the national economy. For this reason, apprehending which disease will newly emerge, and preparing countermeasures for that disease, are required. Many different types of infectious diseases are emerging and threatening global human health conditions. For this reason, the detection of emerging infectious disease pattern is critical. However, as the epidemic spread of infectious disease occurs sporadically and rapidly, it is not easy to predict whether an infectious disease will emerge or not. Furthermore, accumulating data related to a specific infectious disease is not easy. For these reasons, finding useful data and building a prediction model with these data is required. The Internet press releases numerous articles every day that rapidly reflect currently pending issues. Thus, in this research, we accumulated Internet articles from Medisys that were related to infectious disease, to see if news data could be used to predict infectious disease outbreak. Articles related to infectious disease from January to December 2019 were collected. In this study, we evaluated if newly emerging infectious diseases could be detected using the news article data. Support Vector Machine (SVM), Semi-supervised Learning (SSL), and Deep Neural Network (DNN) were used for prediction to examine the use of information embedded in the web articles: and to detect the pattern of emerging infectious disease.

scholarly journals Emerging Infectious Diseases in Pregnant Women in a Non-Endemic Area: Almost One Out of Four Is at Risk

Pathogens ◽  
2021 ◽  
Vol 10 (1) ◽  
pp. 56
Author(s):  
Giulia Modi ◽  
Beatrice Borchi ◽  
Susanna Giaché ◽  
Irene Campolmi ◽  
Michele Trotta ◽  
...  
Keyword(s):  
Infectious Disease ◽  
At Risk ◽  
Infectious Diseases ◽  
Pregnant Women ◽  
Health Care Professionals ◽  
Emerging Infectious Diseases ◽  
Plasmodium Falciparum Malaria ◽  
Emerging Infectious Disease ◽  
Sub Saharan Africa ◽  
Sub Saharan

We report the results of a targeted testing strategy for five emerging infectious diseases (Chagas disease, human T-lymphotropic virus 1 infection, malaria, schistosomiasis, and Zika virus infection) in pregnant women accessing an Italian referral centre for infectious diseases in pregnancy for unrelated reasons. The strategy is based on a quick five-question questionnaire which allows the identification of pregnant women at risk who should be tested for a specific disease. One hundred and three (24%) out of 429 pregnant women evaluated in a 20 month period were at risk for at least one emerging infectious disease. Three (2.9%, all from sub-Saharan Africa) out of 103 at-risk women resulted in being affected (one case of Plasmodium falciparum malaria, two cases of schistosomiasis) and were appropriately managed. Prevalence of emerging infectious disease was particularly high in pregnant women from Africa (three out of 25 pregnant women tested, 12%). The proposed strategy could be used by health care professionals managing pregnant women in non-endemic setting, to identify those at risk for one of the five infection which could benefit for a targeted test and treatment.

scholarly journals A comparison of the relationship between parental efficacy and social support systems of single teen mothers across different family forms in South African low socioeconomic communities

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Samantha L. Coert ◽  
Babatope O. Adebiyi ◽  
Edna Rich ◽  
Nicolette V. Roman
Keyword(s):  
Social Support ◽  
South Africa ◽  
Parenting Practices ◽  
Support Systems ◽  
Pearson Correlation ◽  
Self Report ◽  
Teen Mothers ◽  
Parental Efficacy ◽  
Social Support Systems ◽  
The Relationship

Abstract Background Teenage parenting is recognised as one of the greatest health and social problems in South Africa. Research in South Africa has shown that by the age of 18 years, more than 30% of teens have given birth at least once. Teen mothers may feel disempowered because they are ‘othered’ and consequently, may develop forms of resistance which in most cases may inhibit their ability to parent. Social support is therefore, an imperative intervention for successful teen parenting but this is not clearly understood in South Africa. This study aimed to compare the relationship between parental efficacy and social support systems of single teen mothers across different family forms. Methods A quantitative methodology with a cross-sectional comparative correlation design was conducted with 160 single teen mothers who resided with a family in a low socio-economic community. The participants completed a self-report questionnaire that comprised of the Social Provisions Scale, and the Parenting Sense of Competence scale. Descriptive statistics and Pearson correlation were used to investigate the data. Results A significant positive relationship between social support and parental efficacy was found. When comparing different family forms, single teen mothers’ residing with one parent reported greater levels of parental efficacy and single teen mothers’ residing with two parents, re-counted high levels of social support under the subscales; guide, reliable and nurture. However, when computing for guardian-skip generation, results show that there is no significant relationship between parental efficacy and social support. As well as no correlation across subscales of social support. Conclusion The positive relationships between social support and parental efficacy are important for planning and applying parenting programmes amongst single teen mothers and facilitating awareness regarding the importance of social support and family forms when considering parenting practices.

scholarly journals Analysis of Spatiotemporal Characteristics of Pandemic SARS Spread in Mainland China

2016 ◽  
Vol 2016 ◽  
pp. 1-12 ◽  
Author(s):  
Chunxiang Cao ◽  
Wei Chen ◽  
Sheng Zheng ◽  
Jian Zhao ◽  
Jinfeng Wang ◽  
...  
Keyword(s):  
Infectious Diseases ◽  
Mainland China ◽  
Emerging Infectious Diseases ◽  
Epidemiological Data ◽  
Transmission Dynamics ◽  
Bayesian Maximum Entropy ◽  
Covariance Model ◽  
Spatial And Temporal Scales ◽  
Epidemiological Characteristics ◽  
Spatiotemporal Transmission

Severe acute respiratory syndrome (SARS) is one of the most severe emerging infectious diseases of the 21st century so far. SARS caused a pandemic that spread throughout mainland China for 7 months, infecting 5318 persons in 194 administrative regions. Using detailed mainland China epidemiological data, we study spatiotemporal aspects of this person-to-person contagious disease and simulate its spatiotemporal transmission dynamics via the Bayesian Maximum Entropy (BME) method. The BME reveals that SARS outbreaks show autocorrelation within certain spatial and temporal distances. We use BME to fit a theoretical covariance model that has a sine hole spatial component and exponential temporal component and obtain the weights of geographical and temporal autocorrelation factors. Using the covariance model, SARS dynamics were estimated and simulated under the most probable conditions. Our study suggests that SARS transmission varies in its epidemiological characteristics and SARS outbreak distributions exhibit palpable clusters on both spatial and temporal scales. In addition, the BME modelling demonstrates that SARS transmission features are affected by spatial heterogeneity, so we analyze potential causes. This may benefit epidemiological control of pandemic infectious diseases.

scholarly journals An artificially simulated outbreak of a respiratory infectious disease

2020 ◽  
Author(s):  
Zuiyuan Guo ◽  
Shuang Xu ◽  
Libo Tong ◽  
Botao Dai ◽  
Yuandong Liu ◽  
...  
Keyword(s):  
Infectious Disease ◽  
Infectious Diseases ◽  
Attack Rate ◽  
General Pattern ◽  
Basic Reproductive Number ◽  
Reproductive Number ◽  
Developmental Trend ◽  
Collision Model ◽  
Military Camp ◽  
Epidemiological Characteristics

Abstract Background Outbreaks of respiratory infectious diseases often occur in crowded places. To understand the pattern of spread of an outbreak of a respiratory infectious disease and provide a theoretical basis for targeted implementation of scientific prevention and control, we attempted to establish a stochastic model to simulate an outbreak of a respiratory infectious disease at a military camp. This model fits the general pattern of disease transmission and further enriches theories on the transmission dynamics of infectious diseases. Methods We established an enclosed system of 500 people exposed to adenovirus type 7 (ADV 7) in a military camp. During the infection period, the patients transmitted the virus randomly to susceptible people. The spread of the epidemic under militarized management mode was simulated using a computer model named “the random collision model”, and the effects of factors such as the basic reproductive number ( R 0 ), time of isolation of the patients (TOI), interval between onset and isolation (IOI), and immunization rates (IR) on the developmental trend of the epidemic were quantitatively analysed. Results Once the R 0 exceeded 1.5, the median attack rate increased sharply; when R 0 =3, with a delay in the TOI, the attack rate increased gradually and eventually remained stable. When the IOI exceeded 2.3 days, the median attack rate also increased dramatically. When the IR exceeded 0.5, the median attack rate approached zero. The median generation time was 8.26 days, (95% confidence interval [CI]: 7.84-8.69 days). The partial rank correlation coefficients between the attack rate of the epidemic and R 0 , TOI, IOI, and IR were 0.61, 0.17, 0.45, and -0.27, respectively. Conclusions The random collision model not only simulates how an epidemic spreads with superior precision but also allows greater flexibility in setting the activities of the exposure population and different types of infectious diseases, which is conducive to furthering exploration of the epidemiological characteristics of epidemic outbreaks.

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