scholarly journals Reciprocal Trust as an Ethical Response to the COVID-19 Pandemic

2021 ◽  
Author(s):  
Hui Yun Chan
Keyword(s):  
Infectious Disease ◽  
Infectious Diseases ◽  
Disease Outbreak ◽  
Infectious Disease Outbreak ◽  
Ethical Practices ◽  
Considerable Research ◽  
Pandemic Response ◽  
Restrictive Measures ◽  
Ethical Response

AbstractThe COVID-19 pandemic has generated a range of responses from countries across the globe in managing and containing infections. Considerable research has highlighted the importance of trust in ethically and effectively managing infectious diseases in the population; however, considerations of reciprocal trust remain limited in debates on pandemic response. This paper aims to broaden the perspective of good ethical practices in managing an infectious disease outbreak by including the role of reciprocal trust. A synthesis of the approaches drawn from South Korea and Taiwan reveals reciprocal trust as an important ethical response to the COVID-19 pandemic. Reciprocal trust offers the opportunity to reconcile the difficulties arising from restrictive measures for protecting population health and individual rights.

scholarly journals Detecting Emerging Infectious Diseases: An Overview of the Laboratory Response Network for Biological Threats

2019 ◽  
Vol 134 (2_suppl) ◽  
pp. 16S-21S ◽  
Author(s):  
Julie Villanueva ◽  
Beth Schweitzer ◽  
Marcella Odle ◽  
Tricia Aden
Keyword(s):  
Public Health ◽  
Infectious Disease ◽  
Infectious Diseases ◽  
Emerging Infectious Diseases ◽  
Data Entry ◽  
Disease Outbreak ◽  
Infectious Disease Outbreak ◽  
Public Health Emergencies ◽  
Response Network ◽  
Biosecurity Practices

The Laboratory Response Network (LRN) was established in 1999 to ensure an effective laboratory response to high-priority public health threats. The LRN for biological threats (LRN-B) provides a laboratory infrastructure to respond to emerging infectious diseases. Since 2012, the LRN-B has been involved in 3 emerging infectious disease outbreak responses. We evaluated the LRN-B role in these responses and identified areas for improvement. LRN-B laboratories tested 1097 specimens during the 2014 Middle East Respiratory Syndrome Coronavirus outbreak, 180 specimens during the 2014-2015 Ebola outbreak, and 92 686 specimens during the 2016-2017 Zika virus outbreak. During the 2014-2015 Ebola outbreak, the LRN-B uncovered important gaps in biosafety and biosecurity practices. During the 2016-2017 Zika outbreak, the LRN-B identified the data entry bottleneck as a hindrance to timely reporting of results. Addressing areas for improvement may help LRN-B reference laboratories improve the response to future public health emergencies.

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 An approach to and web-based tool for infectious disease outbreak intervention analysis

2017 ◽  
Vol 7 (1) ◽  
Author(s):  
Ashlynn R. Daughton ◽  
Nicholas Generous ◽  
Reid Priedhorsky ◽  
Alina Deshpande
Keyword(s):  
Public Health ◽  
Infectious Disease ◽  
Infectious Diseases ◽  
Disease Outbreak ◽  
Control Measures ◽  
Infectious Disease Outbreak ◽  
Web Based ◽  
The Public ◽  
Health Community ◽  
Parameter Ranges

Abstract Infectious diseases are a leading cause of death globally. Decisions surrounding how to control an infectious disease outbreak currently rely on a subjective process involving surveillance and expert opinion. However, there are many situations where neither may be available. Modeling can fill gaps in the decision making process by using available data to provide quantitative estimates of outbreak trajectories. Effective reduction of the spread of infectious diseases can be achieved through collaboration between the modeling community and public health policy community. However, such collaboration is rare, resulting in a lack of models that meet the needs of the public health community. Here we show a Susceptible-Infectious-Recovered (SIR) model modified to include control measures that allows parameter ranges, rather than parameter point estimates, and includes a web user interface for broad adoption. We apply the model to three diseases, measles, norovirus and influenza, to show the feasibility of its use and describe a research agenda to further promote interactions between decision makers and the modeling community.

scholarly journals COMMUNICATION IN SOCIAL DISTANCING: THE ROLE OF TECHNOLOGY IN COMMUNICATION TO OVERCOME SOCIAL ISOLATION

2021 ◽  
Vol 6 (1) ◽  
pp. 46-53
Author(s):  
Cherlydea Gladys, Yohanes Muljono, Bambang Irwanto
Keyword(s):  
Infectious Disease ◽  
Social Interaction ◽  
Social Isolation ◽  
Disease Outbreak ◽  
Infectious Disease Outbreak ◽  
Social Distancing ◽  
Communication Processes ◽  
Information And Communication ◽  
The Impact

Social distancing aims to reduce interactions between people in the wider community, where individuals may have been infected or are carriers but have not been identified so as not to be isolated. The impact of social distancing is social isolation due to reduced social interaction. Related to social distancing issues as the impact of infectious disease outbreak has the potential to cause social isolation, it is known that the development of information and communication technology can overcome social and spatial barriers of social interaction by enabling easier communication processes. The results revealed that the development of technology has an important role as a medium of communication in social distancing problems as the impact of infectious disease outbreak has the potential to cause social isolation.

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