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 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 Re-emerging Infectious Disease (RED) Alert tool

2018 ◽  
Vol 10 (1) ◽  
Author(s):  
Maneesha Chitanvis ◽  
Ashlynn Daughton ◽  
Forest M Altherr ◽  
Geoffery Fairchild ◽  
William Rosenberger ◽  
...  
Keyword(s):  
Public Health ◽  
Infectious Disease ◽  
Infectious Diseases ◽  
Disease Incidence ◽  
Emerging Infectious Disease ◽  
Control Measures ◽  
Public Health Response ◽  
Incidence Trends ◽  
Effective Public Health ◽  
Emergence Event

Objective: Although relying on verbal definitions of "re-emergence", descriptions that classify a “re-emergence” event as any significant recurrence of a disease that had previously been under public health control, and subjective interpretations of these events is currently the conventional practice, this has the potential to hinder effective public health responses. Defining re-emergence in this manner offers limited ability for ad hoc analysis of prevention and control measures and facilitates non-reproducible assessments of public health events of potentially high consequence. Re-emerging infectious disease alert (RED Alert) is a decision-support tool designed to address this issue by enhancing situational awareness by providing spatiotemporal context through disease incidence pattern analysis following an event that may represent a local (country-level) re-emergence. The tool’s analytics also provide users with the associated causes (socioeconomic indicators) related to the event, and guide hypothesis-generation regarding the global scenario.Introduction: Definitions of “re-emerging infectious diseases” typically encompass any disease occurrence that was a historic public health threat, declined dramatically, and has since presented itself again as a significant health problem. Examples include antimicrobial resistance leading to resurgence of tuberculosis, or measles re-appearing in previously protected communities. While the language of this verbal definition of “re-emergence” is sensitive enough to capture most epidemiologically relevant resurgences, its qualitative nature obfuscates the ability to quantitatively classify disease re-emergence events as such.Methods: Our tool automatically computes historic disease incidence and performs trend analyses to help elucidate events which a user may considered a true re-emergence in a subset of pertinent infectious diseases (measles, cholera, yellow fever, and dengue). The tool outputs data visualizations that illustrate incidence trends in diverse and informative ways. Additionally, we categorize location and incidence-specific indicators for re-emergence to provide users with associated indicators as well as justifications and documentation to guide users’ next steps. Additionally, the tool also houses interactive maps to facilitate global hypothesis-generation.Results: These outputs provide historic trend pattern analyses as well as contextualization of the user’s situation with similar locations. The tool also broadens users' understanding of the given situation by providing related indicators of the likely re-emergence, as well as the ability to investigate re-emergence factors of global relevance through spatial analysis and data visualization.Conclusions: The inability to categorically name a re-emergence event as such is due to lack of standardization and/or availability of reproducible, data-based evidence, and hinders timely and effective public health response and planning. While the tool will not explicitly call out a user scenario as categorically re-emergent or not, by providing users with context in both time and space, RED Alert aims to empower users with data and analytics in order to substantially enhance their contextual awareness; thus, better enabling them to formulate plans of action regarding re-emerging infectious disease threats at both the country and global level.

Examination of Hospital Workers’ Emotional Responses to an Infectious Disease Outbreak: Lessons From the 2015 MERS Co-V Outbreak in South Korea

2018 ◽  
Vol 13 (03) ◽  
pp. 504-510 ◽  
Author(s):  
Heejung Son ◽  
Wang Jun Lee ◽  
Hyun Soo Kim ◽  
Kkot Sil Lee ◽  
Myoungsoon You
Keyword(s):  
Public Health ◽  
Infectious Disease ◽  
South Korea ◽  
Disease Transmission ◽  
Negative Emotions ◽  
Emotional Responses ◽  
Disease Outbreaks ◽  
Disease Outbreak ◽  
Hospital Workers ◽  
Infectious Disease Outbreak

ABSTRACTHospital workers are critical for a successful response to an infectious disease outbreak and for preventing disease transmission to the community. Therefore, hospital crisis management should implement efforts to improve hospital workers’ preparedness in responding to public health emergencies caused by infectious diseases. Traditionally, preparedness and skill of hospital workers have been emphasized, but awareness of the importance of the emotional mindset of hospital workers in dealing with disease outbreaks has only recently increased; therefore, empirical approaches to examining emotional responses of hospital workers has been limited. This study analyzed qualitative data of the 2015 Middle East Respiratory Syndrome outbreak in South Korea. In particular, negative emotions and stress experienced by hospital workers who treated patients were characterized, as were the events that triggered such experiences. These events were categorized into four themes (eg,Mistake, Missing, Delay Due to Communication Failure). Identifying events that trigger negative emotions in hospital workers has important implications for hospitals’ management guidance in relation to an infectious disease outbreak. (Disaster Med Public Health Preparedness.2019;13:504-510)

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 The Integrated Rapid Infectious Disease Analysis (IRIDA) Platform

2018 ◽  
Author(s):  
Thomas C Matthews ◽  
Franklin R Bristow ◽  
Emma J Griffiths ◽  
Aaron Petkau ◽  
Josh Adam ◽  
...  
Keyword(s):  
Public Health ◽  
Infectious Disease ◽  
Real Time ◽  
Data Sharing ◽  
Disease Outbreak ◽  
Infectious Disease Outbreak ◽  
Outbreak Investigations ◽  
User Friendly ◽  
Disease Analysis ◽  
Web Platform

AbstractWhole genome sequencing (WGS) is a powerful tool for public health infectious disease investigations owing to its higher resolution, greater efficiency, and cost-effectiveness over traditional genotyping methods. Implementation of WGS in routine public health microbiology laboratories is impeded by a lack of user-friendly automated and semi-automated pipelines, restrictive jurisdictional data sharing policies, and the proliferation of non-interoperable analytical and reporting systems. To address these issues, we developed the Integrated Rapid Infectious Disease Analysis (IRIDA) platform (irida.ca), a user-friendly, decentralized, open-source bioinformatics and analytical web platform to support real-time infectious disease outbreak investigations using WGS data. Instances can be independently installed on local high-performance computing infrastructure, enabling private and secure data management and analyses according to organizational policies and governance. IRIDA’s data management capabilities enable secure upload, storage and sharing of all WGS data and metadata. The core platform currently includes pipelines for quality control, assembly, annotation, variant detection, phylogenetic analysis, in silico serotyping, multi-locus sequence typing, and genome distance calculation. Analysis pipeline results can be visualized within the platform through dynamic line lists and integrated phylogenomic clustering for research and discovery, and for enhancing decision-making support and hypothesis generation in epidemiological investigations. Communication and data exchange between instances are provided through customizable access controls. IRIDA complements centralized systems, empowering local analytics and visualizations for genomics-based microbial pathogen investigations. IRIDA is currently transforming the Canadian public health ecosystem and is freely available at https://github.com/phac-nml/irida and www.irida.ca.Impact StatementWhole genome sequencing (WGS) is revolutionizing infectious disease analysis and surveillance due to its cost effectiveness, utility, and improved analytical power. To date, no “one-size-fits-all” genomics platform has been universally adopted, owing to differences in national (and regional) health information systems, data sharing policies, computational infrastructures, lack of interoperability and prohibitive costs. The Integrated Rapid Infectious Disease Analysis (IRIDA) platform is a user-friendly, decentralized, open-source bioinformatics and analytical web platform developed to support real-time infectious disease outbreak investigations using WGS data. IRIDA empowers public health, regulatory and clinical microbiology laboratory personnel to better incorporate WGS technology into routine operations by shielding them from the computational and analytical complexities of big data genomics. IRIDA is now routinely used as part of a validated suite of tools to support outbreak investigations in Canada. While IRIDA was designed to serve the needs of the Canadian public health system, it is generally applicable to any public health and multi-jurisdictional environment. IRIDA enables localized analyses but provides mechanisms and standard outputs to enable data sharing. This approach can help overcome pervasive challenges in real-time global infectious disease surveillance, investigation and control, resulting in faster responses, and ultimately, better public health outcomes.DATA SUMMARYData used to generate some of the figures in this manuscript can be found in the NCBI BioProject PRJNA305824.

scholarly journals Face mask use among individuals who are not medically diagnosed with COVID-19: A lack of evidence for and against and implications around public health recommendations.

2020 ◽  
Author(s):  
Keshini Madara Marasinghe
Keyword(s):  
Public Health ◽  
Scientific Evidence ◽  
Face Mask ◽  
Disease Outbreak ◽  
Evidence Based ◽  
Healthy Individuals ◽  
Infectious Disease Outbreak ◽  
The Public ◽  
Public Health Professionals ◽  
Benefit Risk Assessment

Abstract IntroductionSince the beginning of the COVID-19 outbreak, public health professionals have been constantly making decisions on face mask use among individuals who are not medically diagnosed with COVID-19 or “healthy individuals” to limit the spread of COVID-19. While some countries have strongly recommended face masks for “healthy individuals”, other countries have recommended against it. Public health recommendations that have been provided to this population since the beginning of the outbreak have been controversial, contradicting, and inconsistent around the world. The purpose of this paper is to understand available evidence around the effectiveness or ineffectiveness of face mask use in limiting the spread of COVID-19 among individuals who have not yet been diagnosed with COVID-19 and most importantly, to understand the state of knowledge that the public health recommendations that have been provided since the beginning of the COVID-19 outbreak are based on.MethodsA systematic review was conducted to identify studies that investigated the use of face masks to limit the spread of COVID-19 among “healthy individuals”.ResultsNo studies were found, demonstrating a lack of evidence for and against face mask use suggesting implications around public health recommendations provided to “healthy individuals” since the beginning of the COVID-19 outbreak.ConclusionsThree and a half months into the COVID-19 outbreak (December 2019 – 2nd week of April 2020), there are no peer-reviewed scientific studies that have investigated the effectiveness or ineffectiveness of face mask use among “healthy individuals”. Yet, very strong public health recommendations have been provided on whether they should or should not wear face masks to limit the spread of COVID-19. A lack of scientific evidence heavily questions the basis of these public health recommendations provided at a very early, yet a crucial stage of an outbreak. This finding and a further look at public health recommendations conclude that there is a clear need for more concentrated research around face mask use among healthy individuals as well as public health recommendations that are evidence-based; precautionary in the absence of evidence; based on benefit-risk assessment; transparent; and globally aligned in order to provide the most successful guidelines during an infectious disease outbreak.

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.

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