Use of Available Data To Inform The COVID-19 Outbreak in South Africa: A Case Study

• URL: http://arxiv.org/abs/2004.04813v2
• Date: Wed, 29 Apr 2020 16:36:37 GMT
• Title: Use of Available Data To Inform The COVID-19 Outbreak in South Africa: A Case Study
• Authors: Vukosi Marivate, Herkulaas MvE Combrink
• Abstract summary: 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.
• Score: 1.9686054517684888
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The 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. Social distancing measures, including travel bans, school closure, and quarantine applied to countries or regions are being used to limit the spread of the disease and the demand on the healthcare infrastructure. The seclusion of groups and individuals has led to limited access to accurate information. To update the public, especially in South Africa, announcements are made by the minister of health daily. These announcements narrate the confirmed COVID-19 cases and include the age, gender, and travel history of people who have tested positive for the disease. Additionally, the South African National Institute for Communicable Diseases updates a daily infographic summarising the number of tests performed, confirmed cases, mortality rate, and the regions affected. However, the age of the patient and other nuanced data regarding the transmission is only shared in the daily announcements and not on the updated infographic. To disseminate this information, 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 so that information can be shared to the public - using both a data repository and a dashboard. Through collaborative practices, a variety of challenges related to publicly available data in South Africa came to the fore. These include shortcomings in the accessibility, integrity, and data management practices between governmental departments and the South African public. In this paper, solutions to these problems will be shared by using a publicly available data repository and dashboard as a case study.

Related papers

• Event Detection from Social Media for Epidemic Prediction [76.90779562626541]
  We develop a framework to extract and analyze epidemic-related events from social media posts. Experimentation reveals how ED models trained on COVID-based SPEED can effectively detect epidemic events for three unseen epidemics. We show that reporting sharp increases in the extracted events by our framework can provide warnings 4-9 weeks earlier than the WHO epidemic declaration for Monkeypox.
  arXiv  Detail & Related papers  (2024-04-02T06:31:17Z)
• "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)
• 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)
• Mapping the South African health landscape in response to COVID-19 [1.666378501554705]
  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.
  arXiv  Detail & Related papers  (2020-06-26T21:14:06Z)
• Discussing Privacy and Surveillance on Twitter: A Case Study of COVID-19 [2.0305676256390934]
  Technology is uniquely positioned to help us analyze large amounts of information to provide valuable insight during widespread public health concerns. Apple and Google have recently launched tools for contact tracing. It might be useful to highlight the privacy concerns of users, should they exist, through analysis of Twitter data and information sharing policies during unprecedented public health outbreaks.
  arXiv  Detail & Related papers  (2020-06-11T20:56:55Z)
• Cross-lingual Transfer Learning for COVID-19 Outbreak Alignment [90.12602012910465]
  We train on Italy's early COVID-19 outbreak through Twitter and transfer to several other countries. Our experiments show strong results with up to 0.85 Spearman correlation in cross-country predictions.
  arXiv  Detail & Related papers  (2020-06-05T02:04:25Z)
• CoronaSurveys: Using Surveys with Indirect Reporting to Estimate the Incidence and Evolution of Epidemics [29.03294669532478]
  We propose a technique based on (anonymous) surveys in which participants report on the health status of their contacts. This indirect reporting technique, known in the literature as network scale-up method, preserves the privacy of the participants and their contacts. Results obtained by CoronaSurveys show the power and flexibility of the approach, suggesting that it could be an inexpensive and powerful tool for LMICs.
  arXiv  Detail & Related papers  (2020-05-24T11:58:23Z)
• COVI White Paper [67.04578448931741]
  Contact tracing is an essential tool to change the course of the Covid-19 pandemic. We present an overview of the rationale, design, ethical considerations and privacy strategy of COVI,' a Covid-19 public peer-to-peer contact tracing and risk awareness mobile application developed in Canada.
  arXiv  Detail & Related papers  (2020-05-18T07:40:49Z)
• Digital Ariadne: Citizen Empowerment for Epidemic Control [55.41644538483948]
  The COVID-19 crisis represents the most dangerous threat to public health since the H1N1 pandemic of 1918. Technology-assisted location and contact tracing, if broadly adopted, may help limit the spread of infectious diseases. We present a tool, called 'diAry' or 'digital Ariadne', based on voluntary location and Bluetooth tracking on personal devices.
  arXiv  Detail & Related papers  (2020-04-16T15:53:42Z)
• 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)

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.