Zoonotic Risk Technology Enters the Viral Emergence Toolkit

In the light of the urgency raised by the COVID-19 pandemic, global investment in wildlife virology is likely to increase, and new surveillance programmes will identify hundreds of novel viruses that might someday pose a threat to humans. To support the extensive task of laboratory characterization, scientists may increasingly rely on data-driven rubrics or machine learning models that learn from known zoonoses to identify which animal pathogens could someday pose a threat to global health. We synthesize the findings of an interdisciplinary workshop on zoonotic risk technologies to answer the following questions. What are the prerequisites, in terms of open data, equity and interdisciplinary collaboration, to the development and application of those tools? What effect could the technology have on global health? Who would control that technology, who would have access to it and who would benefit from it? Would it improve pandemic prevention? Could it create new challenges? This article is part of the theme issue ‘Infectious disease macroecology: parasite diversity and dynamics across the globe’.

Download Full-text

A Framework for Protecting Pregnant Women in the Era of COVID-19 Pandemic

International Journal of MCH and AIDS (IJMA) ◽

10.21106/ijma.419 ◽

2021 ◽

Vol 10 (1) ◽

pp. 109-112

Author(s):

Deepa Dongarwar ◽

Veronica Ajewole ◽

Kiydra Harris ◽

Emmanuella Oduguwa ◽

Theresa Ofili ◽

...

Keyword(s):

Open Access ◽

Severe Acute Respiratory Syndrome ◽

Best Practices ◽

Global Health ◽

Pregnant Women ◽

Original Work ◽

Creative Commons ◽

New Challenges ◽

Work First ◽

Open Access Article

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative agent for the coronavirus disease 2019 (COVID-19) pandemic, highlighted and compounded problems while posing new challenges for the pregnant population. Although individual organizations have provided disparate information, guidance, and updates on managing the pregnant population during the current COVID-19 pandemic, it is important to develop a collective model that highlights all the best practices needed to protect the pregnant population during the pandemic. To establish a standard for ensuring safety during the pandemic, we present a framework that describes best practices for the management of the pregnant population during the ongoing COVID-19pandemic. Copyright © 2021 Dongarwar, et al. Published by Global Health and Education Projects, Inc. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in this journal, is properly cited.

Download Full-text

Global Health Clinical Education

International Journal for Innovation Education and Research ◽

10.31686/ijier.vol2.iss10.259 ◽

2014 ◽

Vol 2 (10) ◽

pp. 185-193

Author(s):

Roberta Durham ◽

Lynn A. Van Hofwegen ◽

Megan E Levy

Keyword(s):

Global Health ◽

Clinical Education ◽

Clinical Course ◽

Interdisciplinary Collaboration ◽

Professional Students ◽

Community Needs ◽

Primary Health ◽

Health Professional Students ◽

Improve Health

Global health is the study and practice of improving health and health equity for all people worldwide through international and interdisciplinary collaboration. Studies suggest that health professions students benefit significantly through participating in global health clinical courses. This exploratory qualitative study conducted a community needs assessment as part of a global health clinical course for health professional students. The clinical course allowed students to plan and implement a week-long clinic providing primary health care to families in remote villages in Central America. Students engaged with the researcher to conduct a community assessment.As part of the community assessment we interviewed Kuna to identify from within the community the needs of families to improve health outcomes. This assessment was designed to provide empirical evidence to support future long-term, sustainable improvements in the health of communities. Findings indicate a chasm between what providers see as problems and villagers’ identified priorities. Despite many unanticipated challenges, this research produced some modest and tentative recommendations proposed both for the community and global health clinical education.

Download Full-text

SIS 2017. Statistics and Data Science: new challenges, new generations

10.36253/978-88-6453-521-0 ◽

2017 ◽

Cited By ~ 1

Keyword(s):

Data Science ◽

Open Data ◽

High Dimensional ◽

Pattern Selection ◽

Complex Data ◽

Data Modelling ◽

Testing Hypotheses ◽

New Challenges ◽

Validation Sampling

The 2017 SIS Conference aims to highlight the crucial role of the Statistics in Data Science. In this new domain of ‘meaning’ extracted from the data, the increasing amount of produced and available data in databases, nowadays, has brought new challenges. That involves different fields of statistics, machine learning, information and computer science, optimization, pattern recognition. These afford together a considerable contribute in the analysis of ‘Big data’, open data, relational and complex data, structured and no-structured. The interest is to collect the contributes which provide from the different domains of Statistics, in the high dimensional data quality validation, sampling extraction, dimensional reduction, pattern selection, data modelling, testing hypotheses and confirming conclusions drawn from the data.

Download Full-text

Loch Prospector: Metadata Visualization for Lakes of Open Data

10.31219/osf.io/2s76d ◽

2020 ◽

Author(s):

Neha Makhija ◽

Mansi Jain ◽

Nikolaos Tziavelis ◽

Laura Di Rocco ◽

Sara Di Bartolomeo ◽

...

Keyword(s):

Data Management ◽

Data Science ◽

Open Data ◽

Data Availability ◽

Great Promise ◽

Management Techniques ◽

New Challenges ◽

Integration Data ◽

New Algorithms ◽

Structural Aspects

Data lakes are an emerging storage paradigm that promotes data availability over integration. A prime example are repositories of Open Data which show great promise for transparent data science. Due to the lack of proper integration, Data Lakes may not have a common consistent schema and traditional data management techniques fall short with these repositories. Much recent research has tried to address the new challenges associated with these data lakes. Researchers in this area are mainly interested in the structural properties of the data for developing new algorithms, yet typical Open Data portals offer limited functionality in that respect and instead focus on data semantics.We propose Loch Prospector, a visualization to assist data management researchers in exploring and understanding the most crucial structural aspects of Open Data — in particular, metadata attributes — and the associated task abstraction for their work. Our visualization enables researchers to navigate the contents of data lakes effectively and easily accomplish what were previously laborious tasks. A copy of this paper with all supplemental material is available at osf.io/zkxv9

Download Full-text

No more business as usual: agile and effective responses to emerging pathogen threats require open data and open analytics

10.1101/2020.02.21.959973 ◽

2020 ◽

Cited By ~ 1

Author(s):

◽

Anton Nekrutenko ◽

Sergei L Kosakovsky Pond

Keyword(s):

Global Health ◽

Open Data ◽

Reproducible Research ◽

Emerging Pathogen ◽

The Public ◽

Public Health Emergencies ◽

The Past ◽

Current State ◽

Access To Data ◽

Business As Usual

The current state of much of the Wuhan pneumonia virus (COVID-19) research shows a regrettable lack of data sharing and considerable analytical obfuscation. This impedes global research cooperation, which is essential for tackling public health emergencies, and requires unimpeded access to data, analysis tools, and computational infrastructure. Here we show that community efforts in developing open analytical software tools over the past ten years, combined with national investments into scientific computational infrastructure, can overcome these deficiencies and provide an accessible platform for tackling global health emergencies in an open and transparent manner. Specifically, we use all COVID-19 genomic data available in the public domain so far to (1) underscore the importance of access to raw data and to (2) demonstrate that existing community efforts in curation and deployment of biomedical software can reliably support rapid, reproducible research during global health crises. All our analyses are fully documented at https://github.com/galaxyproject/SARS-CoV-2.

Download Full-text

SLOD-BI

International Journal of Data Warehousing and Mining ◽

10.4018/ijdwm.2015100101 ◽

2015 ◽

Vol 11 (4) ◽

pp. 1-28 ◽

Cited By ~ 7

Author(s):

Rafael Berlanga ◽

Lisette García-Moya ◽

Victoria Nebot ◽

María José Aramburu ◽

Ismael Sanz ◽

...

Keyword(s):

Social Networks ◽

Social Media ◽

Business Intelligence ◽

Open Data ◽

External World ◽

Web Based ◽

Data Infrastructure ◽

Semantic Data ◽

New Challenges ◽

The Voice

The tremendous popularity of web-based social media is attracting the attention of the industry to take profit from the massive availability of sentiment data, which is considered of a high value for Business Intelligence (BI). So far, BI has been mainly concerned with corporate data with little or null attention to the external world. However, for BI analysts, taking into account the Voice of the Customer (VoC) and the Voice of the Market (VoM) is crucial to put in context the results of their analyses. Recent advances in Sentiment Analysis have made possible to effectively extract and summarize sentiment data from these massive social media. As a consequence, VoC and VoM can be now listened from web-based social media (e.g., blogs, reviews forums, social networks, and so on). However, new challenges arise when attempting to integrate traditional corporate data and external sentiment data. This paper deals with these issues and proposes a novel semantic data infrastructure for BI aimed at providing new opportunities for integrating traditional and social BI. This infrastructure follows the principles of the Linked Open Data initiative.

Download Full-text

Using local knowledge in emerging infectious disease research

10.31235/osf.io/eygkw ◽

2020 ◽

Author(s):

Hampton Gray Gaddy

Keyword(s):

Infectious Disease ◽

Global Health ◽

Local Knowledge ◽

Emerging Infectious Diseases ◽

Proof Of Concept ◽

Disease Emergence ◽

Anthropological Theory ◽

Animal Pathogens ◽

Research Initiatives ◽

Source Of Information

Emerging infectious diseases (EIDs) are a growing global health threat. The best research protocol to date on predicting and preventing infectious disease emergence states that urgent research must commence to identify unknown human and animal pathogens. This short communication proposes that the ethnobiological knowledge of indigenous and impoverished communities can be a source of information about some of those unknown pathogens. I present the ecological and anthropological theory behind this proposal, as well as a few case studies that serve as a limited proof of concept. This paper also serves as a call to arms for the medical anthropology community. It gives a brief primer on the EID crisis and how anthropology research may be vital to limiting its havoc on global health. Local knowledge is not likely to play a major role in EID research initiatives, but the use of the incorporation of EID awareness into standard medical anthropological practice would have myriad benefits, even if no EIDs were discovered this way.

Download Full-text

s.Four New Challenges, 20 Global Health and Human Rights in the Age of Populism

Foundations of Global Health & Human Rights ◽

10.1093/law/9780197528297.003.0021 ◽

2020 ◽

Author(s):

Gostin Lawrence O ◽

Constantin Andrés ◽

Meier Benjamin Mason

Keyword(s):

Human Rights ◽

World War Ii ◽

Global Health ◽

Right Wing ◽

International Affairs ◽

Health And Human Rights ◽

Globalizing World ◽

The World ◽

New Challenges ◽

To Come

This chapter examines the threat of populism to global health and human rights. Out of the ashes of World War II, institutions of global health and human rights have brought the world together in unprecedented cooperation, giving rise to the successes and opportunities detailed throughout this text. However, the current populist age threatens these successes and raises obstacles to future progress. In violent contrast with the shared goals of a globalizing world, populism seeks to retrench nations inward, with right-wing populist nationalism directly challenging institutions of global health, violating the rights of vulnerable populations, and spurring isolationism in international affairs. Such retrenchment could lead to a rejection of both global governance and human rights as a basis for global health. Yet, with hope for the future, there remains enduring strength in institutions of global health and human rights, with these institutional bulwarks capable of facing the challenges to come.

Download Full-text

SLOD-BI

Big Data ◽

10.4018/978-1-4666-9840-6.ch083 ◽

2016 ◽

pp. 1784-1813

Author(s):

Rafael Berlanga ◽

Lisette García-Moya ◽

Victoria Nebot ◽

María José Aramburu ◽

Ismael Sanz ◽

...

Keyword(s):

Social Networks ◽

Social Media ◽

Sentiment Analysis ◽

Open Data ◽

External World ◽

Web Based ◽

Data Infrastructure ◽

Semantic Data ◽

New Challenges ◽

The Voice

The tremendous popularity of web-based social media is attracting the attention of the industry to take profit from the massive availability of sentiment data, which is considered of a high value for Business Intelligence (BI). So far, BI has been mainly concerned with corporate data with little or null attention to the external world. However, for BI analysts, taking into account the Voice of the Customer (VoC) and the Voice of the Market (VoM) is crucial to put in context the results of their analyses. Recent advances in Sentiment Analysis have made possible to effectively extract and summarize sentiment data from these massive social media. As a consequence, VoC and VoM can be now listened from web-based social media (e.g., blogs, reviews forums, social networks, and so on). However, new challenges arise when attempting to integrate traditional corporate data and external sentiment data. This paper deals with these issues and proposes a novel semantic data infrastructure for BI aimed at providing new opportunities for integrating traditional and social BI. This infrastructure follows the principles of the Linked Open Data initiative.

Download Full-text