Mapping the South African health landscape in response to COVID-19
- URL: http://arxiv.org/abs/2006.15216v1
- Date: Fri, 26 Jun 2020 21:14:06 GMT
- Title: Mapping the South African health landscape in response to COVID-19
- Authors: Nompumelelo Mtsweni, Herkulaas MvE Combrink, Vukosi Marivate
- Abstract summary: The future prospects of this project relate to a Progressive Web Application that will avail this information for the public as well as healthcare workers.
The available information was found to be outdated, fragmented across several platforms, and still had gaps in the data related to these facilities.
- Score: 1.666378501554705
- License: http://creativecommons.org/licenses/by-sa/4.0/
- Abstract: When the COVID-19 disease pandemic infiltrated the world, there was an
immediate need for accurate information. As with any outbreak, the outbreak
follows a clear trajectory, and subsequently, the supporting information for
that outbreak needs to address the needs associated with that stage of the
outbreak. At first, there was a need to inform the public of the information
related to the initial situation related to the "who" of the COVID-19 disease.
However, as time continued, the "where", "when" and "how to" related questions
started to emerge in relation to the public healthcare system themselves.
Questions surrounding the health facilities including COVID-19 hospital bed
capacity, locations of designated COVID-19 facilities, and general information
related to these facilities were not easily accessible to the general public.
Furthermore, the available information was found to be outdated, fragmented
across several platforms, and still had gaps in the data related to these
facilities. To rectify this problem, a group of volunteers working on the
covid19za project stepped in to assist. Each member leading a part of the
project chose to focus on one of four problems related to the challenges
associated with the Hospital information including: data quality, data
completeness, data source validation and data visualisation capacity. As the
project developed, so did the sophistication of the data, visualisation and
core function of the project. The future prospects of this project relate to a
Progressive Web Application that will avail this information for the public as
well as healthcare workers through comprehensive mapping and data quality.
Related papers
- AMIR: Automated MisInformation Rebuttal -- A COVID-19 Vaccination Datasets based Recommendation System [0.05461938536945722]
This work explored how existing information obtained from social media can be harnessed to facilitate automated rebuttal of misinformation at scale.
It leverages two publicly available datasets, FaCov (fact-checked articles) and misleading (social media Twitter) data on COVID-19 Vaccination.
arXiv Detail & Related papers (2023-10-29T13:07:33Z) - "COVID-19 was a FIFA conspiracy #curropt": An Investigation into the
Viral Spread of COVID-19 Misinformation [60.268682953952506]
We estimate the extent to which misinformation has influenced the course of the COVID-19 pandemic using natural language processing models.
We provide a strategy to combat social media posts that are likely to cause widespread harm.
arXiv Detail & Related papers (2022-06-12T19:41:01Z) - COVID-19: An exploration of consecutive systemic barriers to
pathogen-related data sharing during a pandemic [3.192308005611312]
In 2020, the COVID-19 pandemic resulted in a rapid response from governments and researchers worldwide.
As of late 2023, over millions have died as a result of COVID-19.
Data professionals working with pandemic-relevant data often face significant systemic barriers to accessing, sharing or re-using this data.
arXiv Detail & Related papers (2022-05-24T14:25:09Z) - Where Was COVID-19 First Discovered? Designing a Question-Answering
System for Pandemic Situations [0.0]
The COVID-19 pandemic is accompanied by a massive "infodemic" that makes it hard to identify concise and credible information for COVID-19-related questions.
Our paper is concerned with designing a question-answering system based on modern technologies to overcome information overload and misinformation in pandemic situations.
Our implementation is based on the comprehensive CORD-19 dataset, and we demonstrate our artifact's usefulness by evaluating its answer quality based on a sample of COVID-19 questions labeled by biomedical experts.
arXiv Detail & Related papers (2022-04-19T10:15:51Z) - Medical Visual Question Answering: A Survey [55.53205317089564]
Medical Visual Question Answering(VQA) is a combination of medical artificial intelligence and popular VQA challenges.
Given a medical image and a clinically relevant question in natural language, the medical VQA system is expected to predict a plausible and convincing answer.
arXiv Detail & Related papers (2021-11-19T05:55:15Z) - COVIDx-US -- An open-access benchmark dataset of ultrasound imaging data
for AI-driven COVID-19 analytics [116.6248556979572]
COVIDx-US is an open-access benchmark dataset of COVID-19 related ultrasound imaging data.
It consists of 93 lung ultrasound videos and 10,774 processed images of patients infected with SARS-CoV-2 pneumonia, non-SARS-CoV-2 pneumonia, as well as healthy control cases.
arXiv Detail & Related papers (2021-03-18T03:31:33Z) - Understanding the temporal evolution of COVID-19 research through
machine learning and natural language processing [66.63200823918429]
The outbreak of the novel coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been continuously affecting human lives and communities around the world.
We used multiple data sources, i.e., PubMed and ArXiv, and built several machine learning models to characterize the landscape of current COVID-19 research.
Our findings confirm the types of research available in PubMed and ArXiv differ significantly, with the former exhibiting greater diversity in terms of COVID-19 related issues.
arXiv Detail & Related papers (2020-07-22T18:02:39Z) - Trust and Transparency in Contact Tracing Applications [81.07729301514182]
The global outbreak of COVID-19 has led to efforts to manage and mitigate the continued spread of the disease.
One of these efforts include the use of contact tracing to identify people who are at-risk of developing the disease through exposure to an infected person.
There has been significant interest in the development and use of digital contact tracing solutions to supplement the work of human contact tracers.
The collection and use of sensitive personal details by these applications has led to a number of concerns by the stakeholder groups with a vested interest in these solutions.
arXiv Detail & Related papers (2020-06-19T20:29:24Z) - Use of Available Data To Inform The COVID-19 Outbreak in South Africa: A
Case Study [1.9686054517684888]
coronavirus disease (COVID-19), caused by the SARS-CoV-2 virus, was declared a pandemic by the World Health Organization (WHO) in February 2020.
Currently, there are no vaccines or treatments that have been approved after clinical trials.
The Data Science for Social Impact research group at the University of Pretoria, South Africa, has worked on curating and applying publicly available data in a way that is computer-readable.
arXiv Detail & Related papers (2020-04-02T06:13:37Z) - A County-level Dataset for Informing the United States' Response to
COVID-19 [5.682299443164938]
We present a dataset that aggregates relevant data from governmental, journalistic, and academic sources on the U.S. county level.
Our dataset contains more than 300 variables that summarize population estimates, demographics, ethnicity, housing, education, employment and income, climate, transit, scores, and healthcare system-related metrics.
arXiv Detail & Related papers (2020-04-01T05:07:27Z) - Mapping the Landscape of Artificial Intelligence Applications against
COVID-19 [59.30734371401316]
COVID-19, the disease caused by the SARS-CoV-2 virus, has been declared a pandemic by the World Health Organization.
We present an overview of recent studies using Machine Learning and, more broadly, Artificial Intelligence to tackle many aspects of the COVID-19 crisis.
arXiv Detail & Related papers (2020-03-25T12:30:33Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.