39,915 research outputs found
Tracking COVID-19 using online search
Previous research has demonstrated that various properties of infectious
diseases can be inferred from online search behaviour. In this work we use time
series of online search query frequencies to gain insights about the prevalence
of COVID-19 in multiple countries. We first develop unsupervised modelling
techniques based on associated symptom categories identified by the United
Kingdom's National Health Service and Public Health England. We then attempt to
minimise an expected bias in these signals caused by public interest -- as
opposed to infections -- using the proportion of news media coverage devoted to
COVID-19 as a proxy indicator. Our analysis indicates that models based on
online searches precede the reported confirmed cases and deaths by 16.7 (10.2 -
23.2) and 22.1 (17.4 - 26.9) days, respectively. We also investigate transfer
learning techniques for mapping supervised models from countries where the
spread of disease has progressed extensively to countries that are in earlier
phases of their respective epidemic curves. Furthermore, we compare time series
of online search activity against confirmed COVID-19 cases or deaths jointly
across multiple countries, uncovering interesting querying patterns, including
the finding that rarer symptoms are better predictors than common ones.
Finally, we show that web searches improve the short-term forecasting accuracy
of autoregressive models for COVID-19 deaths. Our work provides evidence that
online search data can be used to develop complementary public health
surveillance methods to help inform the COVID-19 response in conjunction with
more established approaches.Comment: Published in Nature Digital Medicine. Please note that the published
version differs from this preprin
A Cross-Domain Approach to Analyzing the Short-Run Impact of COVID-19 on the U.S. Electricity Sector
The novel coronavirus disease (COVID-19) has rapidly spread around the globe
in 2020, with the U.S. becoming the epicenter of COVID-19 cases since late
March. As the U.S. begins to gradually resume economic activity, it is
imperative for policymakers and power system operators to take a scientific
approach to understanding and predicting the impact on the electricity sector.
Here, we release a first-of-its-kind cross-domain open-access data hub,
integrating data from across all existing U.S. wholesale electricity markets
with COVID-19 case, weather, cellular location, and satellite imaging data.
Leveraging cross-domain insights from public health and mobility data, we
uncover a significant reduction in electricity consumption across that is
strongly correlated with the rise in the number of COVID-19 cases, degree of
social distancing, and level of commercial activity.Comment: This paper has been accepted for publication by Joule. The manuscript
can also be accessed from EnerarXiv:
http://www.enerarxiv.org/page/thesis.html?id=198
The Impact of Social Media on Panic During the COVID-19 Pandemic in Iraqi Kurdistan: Online Questionnaire Study
Background: In the first few months of 2020, information and news reports about the coronavirus disease (COVID-19) were rapidly published and shared on social media and social networking sites. While the field of infodemiology has studied information patterns on the Web and in social media for at least 18 years, the COVID-19 pandemic has been referred to as the first social media infodemic. However, there is limited evidence about whether and how the social media infodemic has spread panic and affected the mental health of social media users.
Objective: The aim of this study is to determine how social media affects self-reported mental health and the spread of panic about COVID-19 in the Kurdistan Region of Iraq.
Methods: To carry out this study, an online questionnaire was prepared and conducted in Iraqi Kurdistan, and a total of 516 social media users were sampled. This study deployed a content analysis method for data analysis. Correspondingly, data were analyzed using SPSS software.
Results: Participants reported that social media has a significant impact on spreading fear and panic related to the COVID-19 outbreak in Iraqi Kurdistan, with a potential negative influence on people’s mental health and psychological well-being. Facebook was the most used social media network for spreading panic about the COVID-19 outbreak in Iraq. We found a significant positive statistical correlation between self-reported social media use and the spread of panic related to COVID-19 (R=.8701). Our results showed that the majority of youths aged 18-35 years are facing psychological anxiety.
Conclusions: During lockdown, people are using social media platforms to gain information about COVID-19. The nature of the impact of social media panic among people varies depending on an individual's gender, age, and level of education. Social media has played a key role in spreading anxiety about the COVID-19 outbreak in Iraqi Kurdistan
COVID-19 publications: Database coverage, citations, readers, tweets, news, Facebook walls, Reddit posts
© 2020 The Authors. Published by MIT Press. This is an open access article available under a Creative Commons licence.
The published version can be accessed at the following link on the publisher’s website: https://doi.org/10.1162/qss_a_00066The COVID-19 pandemic requires a fast response from researchers to help address biological,
medical and public health issues to minimize its impact. In this rapidly evolving context,
scholars, professionals and the public may need to quickly identify important new studies. In
response, this paper assesses the coverage of scholarly databases and impact indicators
during 21 March to 18 April 2020. The rapidly increasing volume of research, is particularly
accessible through Dimensions, and less through Scopus, the Web of Science, and PubMed.
Google Scholar’s results included many false matches. A few COVID-19 papers from the
21,395 in Dimensions were already highly cited, with substantial news and social media
attention. For this topic, in contrast to previous studies, there seems to be a high degree of
convergence between articles shared in the social web and citation counts, at least in the
short term. In particular, articles that are extensively tweeted on the day first indexed are
likely to be highly read and relatively highly cited three weeks later. Researchers needing wide
scope literature searches (rather than health focused PubMed or medRxiv searches) should
start with Dimensions (or Google Scholar) and can use tweet and Mendeley reader counts as
indicators of likely importance
What country, university or research institute, performed the best on COVID-19? Bibliometric analysis of scientific literature
In this article, we conduct data mining to discover the countries,
universities and companies, produced or collaborated the most research on
Covid-19 since the pandemic started. We present some interesting findings, but
despite analysing all available records on COVID-19 from the Web of Science
Core Collection, we failed to reach any significant conclusions on how the
world responded to the COVID-19 pandemic. Therefore, we increased our analysis
to include all available data records on pandemics and epidemics from 1900 to
2020. We discover some interesting results on countries, universities and
companies, that produced collaborated most the most in research on pandemic and
epidemics. Then we compared the results with the analysing on COVID-19 data
records. This has created some interesting findings that are explained and
graphically visualised in the article
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