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  • Publication . Preprint . Article . 2020
    Open Access English

    Within the context of SEIR models, we consider a lockdown that is both imposed and lifted at an early stage of an epidemic. We show that, in these models, although such a lockdown may delay deaths, it eventually does not avert a significant number of fatalities. Therefore, in these models, the efficacy of a lockdown cannot be gauged by simply comparing figures for the deaths at the end of the lockdown with the projected figure for deaths by the same date without the lockdown. We provide a simple but robust heuristic argument to explain why this conclusion should generalize to more elaborate compartmental models. We qualitatively discuss some important effects of a lockdown, which go beyond the scope of simple models, but could cause it to increase or decrease an epidemic's final toll. Given the significance of these effects in India, and the limitations of currently available data, we conclude that simple epidemiological models cannot be used to reliably quantify the impact of the Indian lockdown on fatalities caused by the COVID-19 pandemic. 23 pages; v2: refs added; minor textual updates

  • Open Access English

    This study analyses oil price movements through the lens of an agnostic random forest model, which is based on 1,000 regression trees. It shows that this highly disciplined, yet flexible computational model reduces in sample root mean square errors by 65% relative to a standard linear least square model that uses the same set of 11 explanatory factors. In forecasting exercises the RMSE reduction ranges between 51% and 68%, highlighting the relevance of non linearities in oil markets. The results underscore the importance of incorporating financial factors into oil models: US interest rates, the dollar and the VIX together account for 39% of the models RMSE reduction in the post 2010 sample, rising to 48% in the post 2020 sample. If Covid 19 is also considered as a risk factor, these shares become even larger.

  • Open Access English
    Authors: 
    Bernd Skiera; Lukas Jürgensmeier; Kevin Stowe; Iryna Gurevych;

    Knowledge about the daily number of new infections of Covid-19 is important because it is the basis for political decisions resulting in lockdowns and urgent health care measures. We use Germany as an example to illustrate shortcomings of official numbers, which are, at least in Germany, disclosed only with several days of delay and severely underreported on weekends (more than 40%). These shortcomings outline an urgent need for alternative data sources. The other widely cited source provided by the Center for Systems Science and Engineering at Johns Hopkins University (JHU) also deviates for Germany on average by 79% from the official numbers. We argue that Google Search and Twitter data should complement official numbers. They predict even better than the original values from Johns Hopkins University and do so several days ahead. These two data sources could also be used in parts of the world where official numbers do not exist or are perceived to be unreliable. 15 pages, 5 figures

  • Open Access English
    Authors: 
    David Buil-Gil; Yongyu Zeng; Steven Kemp;
    Country: United Kingdom

    AbstractMuch research has shown that the first lockdowns imposed in response to the COVID-19 pandemic were associated with changes in routine activities and, therefore, changes in crime. While several types of violent and property crime decreased immediately after the first lockdown, online crime rates increased. Nevertheless, little research has explored the relationship between multiple lockdowns and crime in the mid-term. Furthermore, few studies have analysed potentially contrasting trends in offline and online crimes using the same dataset. To fill these gaps in research, the present article employs interrupted time-series analysis to examine the effects on offline and online crime of the three lockdown orders implemented in Northern Ireland. We analyse crime data recorded by the police between April 2015 and May 2021. Results show that many types of traditional offline crime decreased after the lockdowns but that they subsequently bounced back to pre-pandemic levels. In contrast, results appear to indicate that cyber-enabled fraud and cyber-dependent crime rose alongside lockdown-induced changes in online habits and remained higher than before COVID-19. It is likely that the pandemic accelerated the long-term upward trend in online crime. We also find that lockdowns with stay-at-home orders had a clearer impact on crime than those without. Our results contribute to understanding how responses to pandemics can influence crime trends in the mid-term as well as helping identify the potential long-term effects of the pandemic on crime, which can strengthen the evidence base for policy and practice.

  • Open Access English
    Authors: 
    Brittany L. Manning; Alexandra Harpole; Emily M. Harriott; Kamila Postolowicz; Elizabeth S. Norton;
    Publisher: American Speech-Language-Hearing Association

    Purpose There has been increased interest in using telepractice for involving more diverse children in research and clinical services, as well as when in-person assessment is challenging, such as during COVID-19. Little is known, however, about the feasibility, reliability, and validity of language samples when conducted via telepractice. Method Child language samples from parent–child play were recorded either in person in the laboratory or via video chat at home, using parents' preferred commercially available software on their own device. Samples were transcribed and analyzed using Systematic Analysis of Language Transcripts software. Analyses compared measures between-subjects for 46 dyads who completed video chat language samples versus 16 who completed in-person samples; within-subjects analyses were conducted for a subset of 13 dyads who completed both types. Groups did not differ significantly on child age, sex, or socioeconomic status. Results The number of usable samples and percent of utterances with intelligible audio signal did not differ significantly for in-person versus video chat language samples. Child speech and language characteristics (including mean length of utterance, type–token ratio, number of different words, grammatical errors/omissions, and child speech intelligibility) did not differ significantly between in-person and video chat methods. This was the case for between-group analyses and within-child comparisons. Furthermore, transcription reliability (conducted on a subset of samples) was high and did not differ between in-person and video chat methods. Conclusions This study demonstrates that child language samples collected via video chat are largely comparable to in-person samples in terms of key speech and language measures. Best practices for maximizing data quality for using video chat language samples are provided.

  • Open Access English
    Authors: 
    Nur Hayati Sf; Pandin Mgr;
    Publisher: Preprints

    Background: In the current era of globalization, the Indonesian government's problem today is the weakening of nationalism and patriotism among the millennial generation. The large number of foreign cultures that have entered Indonesia has caused a sense of nationalism and patriotism. In addition, Indonesia is also facing the problem of spreading the Covid-19 virus. During the pandemic, various policies set by the government received protests from some circles because they felt their freedom was restricted. Therefore, the awareness of millennial generation nationalism is needed, especially during the Covid-19 pandemic like today. This research aims to make millennials aware of nationalism sense, which mainly to prevent the spread of Covid-19. This research is used to answer the questions of what the problems that arise due to the waning of the spirit of nationalism during the pandemic are? and what efforts should be made to maintain the spirit of nationalism? Methods: This research is a qualitative study using the literature review method. The articles used are research published in 2019 to 2021 in Google Scholar, with keywords that match the topic of millennial generation nationalism in the Covid-19 pandemic. Results and Discussion: The results of the study found that the spirit of Indonesian nationalism during the Covid-19 pandemic was decreasing. The decline in the sense of nationalism is due to several government policies that impact the psychology of society and the Indonesian economy. As a result, society, particularly the millennial generation, must play a role in breaking the chain of the Covid-19 virus's propagation by following the government's health standards. Conclusion: The government and society need to work together to understand nationalism in the millennial generation, especially in dealing with problems caused by the Covid-19 pandemic. Based on this, various efforts need to be made to foster the spirit of nationalism and overcome the Covid-19 pandemic. So that later, it can produce a generation that upholds the value of nationalism in everyday life.

  • Open Access English
    Authors: 
    Martin Müller; Marcel Salathé; Per E Kummervold;
    Project: EC | VACMA (797876), EC | VEO (874735)

    In this work, we release COVID-Twitter-BERT (CT-BERT), a transformer-based model, pretrained on a large corpus of Twitter messages on the topic of COVID-19. Our model shows a 10-30% marginal improvement compared to its base model, BERT-Large, on five different classification datasets. The largest improvements are on the target domain. Pretrained transformer models, such as CT-BERT, are trained on a specific target domain and can be used for a wide variety of natural language processing tasks, including classification, question-answering and chatbots. CT-BERT is optimised to be used on COVID-19 content, in particular social media posts from Twitter.

  • Open Access English
    Authors: 
    Anthony Lander;
    Publisher: Cold Spring Harbor Laboratory

    AbstractBackgroundIn a classic epidemic, the infected population has an early exponential phase, before slowing and fading to its peak. Mitigating interventions may change the exponent during the rising phase and a plateau can replace a peak. With interventions comes the risk that relaxation causes a second-wave. In the UK Covid-19 epidemic, infections cannot be counted, but their influence is seen in the curve of the mortality data. This work simulated social distancing and the lockdown in the UK Covid-19 epidemic to explore strategies for relaxation.MethodsCumulative mortality data was transposed 20 days earlier to identify three doubling periods separated by the 17th March—social distancing, and 23rd March—lockdown. A set of stochastic processes simulated viral transmission between interacting individuals using Covid-19 incubation and illness durations. Social distancing and restrictions on interactions were imposed and later relaxed.Principal FindingsDaily mortality data, consistent with that seen in the UK Covid-19 epidemic to 24th April 2020 was simulated. This output predicts that under a lockdown maintained till early July 2020, UK deaths will exceed 31,000, but leave a large susceptible population and a requirement for vaccination or quarantine. An earlier staged relaxation carries a risk of a second-wave. The model allows exploration of strategies for lifting the lockdown.InterpretationSocial distancing and the lockdown have had an impressive impact on the UK Covid-19 epidemic and saved lives, caution is now needed in planning its relaxation.FundingUnfunded research.Research in contextEvidence before this studyThe classical Susceptible, Infected, Recovered, (SIR) epidemiological model with additional compartments and sophistications have been widely used to make forecasts in the Covid-19 pandemic but are not easily accessible.Added value of this studyThis study adds reassurance that the interventions of social distancing introduced on the 17th March and the lockdown of the 23rd March 2020 have reduced mortality. The risks of a second-wave on their relaxation are real and illustrated graphically.Implications of all the available evidenceTogether with other models, credence is given to the risks of a second-wave in the UK Covid-19 epidemic on the relaxation of restrictions.

  • Open Access English
    Authors: 
    José Luis Izquierdo; Julio Ancochea; Joan B. Soriano;
    Publisher: Cold Spring Harbor Laboratory

    Background: There remain many unknowns regarding the natural history, onset, distribution and both the individual and population burden of the ongoing COVID-19 pandemic associated with the spread of the SARS-CoV-2 virus. Here, we used a combination of classic epidemiological methods, natural language processing (NLP), and machine learning (for predictive modelling), to analyse the clinical information in the electronic health records (EHRs) of patients with COVID-19. This approach holds the potential to better define the disease and its associated outcomes, most notably ICU admission. Methods: This is a multicentre, non-interventional, retrospective study using the unstructured free-text clinical information captured in the EHRs of the participating hospital sites within the SESCAM Healthcare Network (Castilla La-Mancha, Spain, with 2.035 M inhabitants). We collected clinical information from the entire population with available EHRs (1,364,924 patients) for the period comprised between January 1, 2020 and March 29, 2020. Following identification of all COVID-19 cases seen in hospitals and primary care settings (all departments), we extracted related information upon diagnosis (including demographic characteristics, symptoms upon diagnosis, and other clinical information) and during disease progression and outcome (admission, discharge, and ICU admission). A data-driven analysis explored the minimum set of clinical variables associated with requiring ICU admission. Findings: A total of 10,504 patients with a clinical or PCR-confirmed diagnosis of COVID-19 were identified, 52.5% males, with a mean±SD age of 58.2±19.7 years, and age distribution ranging from 39oC/102oF without respiratory crackles), were free of ICU admission. On the contrary, COVID-19 patients aged 40 to 79 years were likely to be admitted to the ICU if they had tachypnoea and delayed their visit to the ER after being seen in primary care. Interpretation: Our results show that a combination of easily obtained clinical variables (age, fever, and tachypnoea with/without respiratory crackles) predicts which COVID-19 patients require ICU admission. Funding Statement: This study was sponsored by SAVANA (https://www.savanamed.com/) Declaration of Interests: None to declare Ethics Approval Statement: This study was classified as a ‘non-post-authorization study’ (EPA) by the Spanish Agency of Medicines and Health Products (AEMPS), and it was approved by the Research Ethics Committee at the University Hospital of Guadalajara (Spain).

  • Publication . Preprint . 2020
    Open Access English
    Authors: 
    Aseel Addawood;
    Publisher: Preprints

    The COVID-19 pandemic spread of the coronavirus across the globe has affected our lives on many different levels. The world we knew before the spread of the virus has become another one. Every country has taken preventive measures, including social distancing, travel restrictions, and curfew, to control the spread of the disease. With these measures implemented, people have shifted to social media platforms in the online sphere, such as Twitter, to maintain connections. In this paper, we describe a coronavirus data set of Arabic tweets collected from January 1, 2020, primarily from hashtags populated from Saudi Arabia. This data set is available to the research community to glean a better understanding of the societal, economical, and political effects of the outbreak and to help policy makers make better decisions for fighting this epidemic.

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84 Research products, page 1 of 9
  • Publication . Preprint . Article . 2020
    Open Access English

    Within the context of SEIR models, we consider a lockdown that is both imposed and lifted at an early stage of an epidemic. We show that, in these models, although such a lockdown may delay deaths, it eventually does not avert a significant number of fatalities. Therefore, in these models, the efficacy of a lockdown cannot be gauged by simply comparing figures for the deaths at the end of the lockdown with the projected figure for deaths by the same date without the lockdown. We provide a simple but robust heuristic argument to explain why this conclusion should generalize to more elaborate compartmental models. We qualitatively discuss some important effects of a lockdown, which go beyond the scope of simple models, but could cause it to increase or decrease an epidemic's final toll. Given the significance of these effects in India, and the limitations of currently available data, we conclude that simple epidemiological models cannot be used to reliably quantify the impact of the Indian lockdown on fatalities caused by the COVID-19 pandemic. 23 pages; v2: refs added; minor textual updates

  • Open Access English

    This study analyses oil price movements through the lens of an agnostic random forest model, which is based on 1,000 regression trees. It shows that this highly disciplined, yet flexible computational model reduces in sample root mean square errors by 65% relative to a standard linear least square model that uses the same set of 11 explanatory factors. In forecasting exercises the RMSE reduction ranges between 51% and 68%, highlighting the relevance of non linearities in oil markets. The results underscore the importance of incorporating financial factors into oil models: US interest rates, the dollar and the VIX together account for 39% of the models RMSE reduction in the post 2010 sample, rising to 48% in the post 2020 sample. If Covid 19 is also considered as a risk factor, these shares become even larger.

  • Open Access English
    Authors: 
    Bernd Skiera; Lukas Jürgensmeier; Kevin Stowe; Iryna Gurevych;

    Knowledge about the daily number of new infections of Covid-19 is important because it is the basis for political decisions resulting in lockdowns and urgent health care measures. We use Germany as an example to illustrate shortcomings of official numbers, which are, at least in Germany, disclosed only with several days of delay and severely underreported on weekends (more than 40%). These shortcomings outline an urgent need for alternative data sources. The other widely cited source provided by the Center for Systems Science and Engineering at Johns Hopkins University (JHU) also deviates for Germany on average by 79% from the official numbers. We argue that Google Search and Twitter data should complement official numbers. They predict even better than the original values from Johns Hopkins University and do so several days ahead. These two data sources could also be used in parts of the world where official numbers do not exist or are perceived to be unreliable. 15 pages, 5 figures

  • Open Access English
    Authors: 
    David Buil-Gil; Yongyu Zeng; Steven Kemp;
    Country: United Kingdom

    AbstractMuch research has shown that the first lockdowns imposed in response to the COVID-19 pandemic were associated with changes in routine activities and, therefore, changes in crime. While several types of violent and property crime decreased immediately after the first lockdown, online crime rates increased. Nevertheless, little research has explored the relationship between multiple lockdowns and crime in the mid-term. Furthermore, few studies have analysed potentially contrasting trends in offline and online crimes using the same dataset. To fill these gaps in research, the present article employs interrupted time-series analysis to examine the effects on offline and online crime of the three lockdown orders implemented in Northern Ireland. We analyse crime data recorded by the police between April 2015 and May 2021. Results show that many types of traditional offline crime decreased after the lockdowns but that they subsequently bounced back to pre-pandemic levels. In contrast, results appear to indicate that cyber-enabled fraud and cyber-dependent crime rose alongside lockdown-induced changes in online habits and remained higher than before COVID-19. It is likely that the pandemic accelerated the long-term upward trend in online crime. We also find that lockdowns with stay-at-home orders had a clearer impact on crime than those without. Our results contribute to understanding how responses to pandemics can influence crime trends in the mid-term as well as helping identify the potential long-term effects of the pandemic on crime, which can strengthen the evidence base for policy and practice.

  • Open Access English
    Authors: 
    Brittany L. Manning; Alexandra Harpole; Emily M. Harriott; Kamila Postolowicz; Elizabeth S. Norton;
    Publisher: American Speech-Language-Hearing Association

    Purpose There has been increased interest in using telepractice for involving more diverse children in research and clinical services, as well as when in-person assessment is challenging, such as during COVID-19. Little is known, however, about the feasibility, reliability, and validity of language samples when conducted via telepractice. Method Child language samples from parent–child play were recorded either in person in the laboratory or via video chat at home, using parents' preferred commercially available software on their own device. Samples were transcribed and analyzed using Systematic Analysis of Language Transcripts software. Analyses compared measures between-subjects for 46 dyads who completed video chat language samples versus 16 who completed in-person samples; within-subjects analyses were conducted for a subset of 13 dyads who completed both types. Groups did not differ significantly on child age, sex, or socioeconomic status. Results The number of usable samples and percent of utterances with intelligible audio signal did not differ significantly for in-person versus video chat language samples. Child speech and language characteristics (including mean length of utterance, type–token ratio, number of different words, grammatical errors/omissions, and child speech intelligibility) did not differ significantly between in-person and video chat methods. This was the case for between-group analyses and within-child comparisons. Furthermore, transcription reliability (conducted on a subset of samples) was high and did not differ between in-person and video chat methods. Conclusions This study demonstrates that child language samples collected via video chat are largely comparable to in-person samples in terms of key speech and language measures. Best practices for maximizing data quality for using video chat language samples are provided.

  • Open Access English
    Authors: 
    Nur Hayati Sf; Pandin Mgr;
    Publisher: Preprints

    Background: In the current era of globalization, the Indonesian government's problem today is the weakening of nationalism and patriotism among the millennial generation. The large number of foreign cultures that have entered Indonesia has caused a sense of nationalism and patriotism. In addition, Indonesia is also facing the problem of spreading the Covid-19 virus. During the pandemic, various policies set by the government received protests from some circles because they felt their freedom was restricted. Therefore, the awareness of millennial generation nationalism is needed, especially during the Covid-19 pandemic like today. This research aims to make millennials aware of nationalism sense, which mainly to prevent the spread of Covid-19. This research is used to answer the questions of what the problems that arise due to the waning of the spirit of nationalism during the pandemic are? and what efforts should be made to maintain the spirit of nationalism? Methods: This research is a qualitative study using the literature review method. The articles used are research published in 2019 to 2021 in Google Scholar, with keywords that match the topic of millennial generation nationalism in the Covid-19 pandemic. Results and Discussion: The results of the study found that the spirit of Indonesian nationalism during the Covid-19 pandemic was decreasing. The decline in the sense of nationalism is due to several government policies that impact the psychology of society and the Indonesian economy. As a result, society, particularly the millennial generation, must play a role in breaking the chain of the Covid-19 virus's propagation by following the government's health standards. Conclusion: The government and society need to work together to understand nationalism in the millennial generation, especially in dealing with problems caused by the Covid-19 pandemic. Based on this, various efforts need to be made to foster the spirit of nationalism and overcome the Covid-19 pandemic. So that later, it can produce a generation that upholds the value of nationalism in everyday life.

  • Open Access English
    Authors: 
    Martin Müller; Marcel Salathé; Per E Kummervold;
    Project: EC | VACMA (797876), EC | VEO (874735)

    In this work, we release COVID-Twitter-BERT (CT-BERT), a transformer-based model, pretrained on a large corpus of Twitter messages on the topic of COVID-19. Our model shows a 10-30% marginal improvement compared to its base model, BERT-Large, on five different classification datasets. The largest improvements are on the target domain. Pretrained transformer models, such as CT-BERT, are trained on a specific target domain and can be used for a wide variety of natural language processing tasks, including classification, question-answering and chatbots. CT-BERT is optimised to be used on COVID-19 content, in particular social media posts from Twitter.

  • Open Access English
    Authors: 
    Anthony Lander;
    Publisher: Cold Spring Harbor Laboratory

    AbstractBackgroundIn a classic epidemic, the infected population has an early exponential phase, before slowing and fading to its peak. Mitigating interventions may change the exponent during the rising phase and a plateau can replace a peak. With interventions comes the risk that relaxation causes a second-wave. In the UK Covid-19 epidemic, infections cannot be counted, but their influence is seen in the curve of the mortality data. This work simulated social distancing and the lockdown in the UK Covid-19 epidemic to explore strategies for relaxation.MethodsCumulative mortality data was transposed 20 days earlier to identify three doubling periods separated by the 17th March—social distancing, and 23rd March—lockdown. A set of stochastic processes simulated viral transmission between interacting individuals using Covid-19 incubation and illness durations. Social distancing and restrictions on interactions were imposed and later relaxed.Principal FindingsDaily mortality data, consistent with that seen in the UK Covid-19 epidemic to 24th April 2020 was simulated. This output predicts that under a lockdown maintained till early July 2020, UK deaths will exceed 31,000, but leave a large susceptible population and a requirement for vaccination or quarantine. An earlier staged relaxation carries a risk of a second-wave. The model allows exploration of strategies for lifting the lockdown.InterpretationSocial distancing and the lockdown have had an impressive impact on the UK Covid-19 epidemic and saved lives, caution is now needed in planning its relaxation.FundingUnfunded research.Research in contextEvidence before this studyThe classical Susceptible, Infected, Recovered, (SIR) epidemiological model with additional compartments and sophistications have been widely used to make forecasts in the Covid-19 pandemic but are not easily accessible.Added value of this studyThis study adds reassurance that the interventions of social distancing introduced on the 17th March and the lockdown of the 23rd March 2020 have reduced mortality. The risks of a second-wave on their relaxation are real and illustrated graphically.Implications of all the available evidenceTogether with other models, credence is given to the risks of a second-wave in the UK Covid-19 epidemic on the relaxation of restrictions.

  • Open Access English
    Authors: 
    José Luis Izquierdo; Julio Ancochea; Joan B. Soriano;
    Publisher: Cold Spring Harbor Laboratory

    Background: There remain many unknowns regarding the natural history, onset, distribution and both the individual and population burden of the ongoing COVID-19 pandemic associated with the spread of the SARS-CoV-2 virus. Here, we used a combination of classic epidemiological methods, natural language processing (NLP), and machine learning (for predictive modelling), to analyse the clinical information in the electronic health records (EHRs) of patients with COVID-19. This approach holds the potential to better define the disease and its associated outcomes, most notably ICU admission. Methods: This is a multicentre, non-interventional, retrospective study using the unstructured free-text clinical information captured in the EHRs of the participating hospital sites within the SESCAM Healthcare Network (Castilla La-Mancha, Spain, with 2.035 M inhabitants). We collected clinical information from the entire population with available EHRs (1,364,924 patients) for the period comprised between January 1, 2020 and March 29, 2020. Following identification of all COVID-19 cases seen in hospitals and primary care settings (all departments), we extracted related information upon diagnosis (including demographic characteristics, symptoms upon diagnosis, and other clinical information) and during disease progression and outcome (admission, discharge, and ICU admission). A data-driven analysis explored the minimum set of clinical variables associated with requiring ICU admission. Findings: A total of 10,504 patients with a clinical or PCR-confirmed diagnosis of COVID-19 were identified, 52.5% males, with a mean±SD age of 58.2±19.7 years, and age distribution ranging from 39oC/102oF without respiratory crackles), were free of ICU admission. On the contrary, COVID-19 patients aged 40 to 79 years were likely to be admitted to the ICU if they had tachypnoea and delayed their visit to the ER after being seen in primary care. Interpretation: Our results show that a combination of easily obtained clinical variables (age, fever, and tachypnoea with/without respiratory crackles) predicts which COVID-19 patients require ICU admission. Funding Statement: This study was sponsored by SAVANA (https://www.savanamed.com/) Declaration of Interests: None to declare Ethics Approval Statement: This study was classified as a ‘non-post-authorization study’ (EPA) by the Spanish Agency of Medicines and Health Products (AEMPS), and it was approved by the Research Ethics Committee at the University Hospital of Guadalajara (Spain).

  • Publication . Preprint . 2020
    Open Access English
    Authors: 
    Aseel Addawood;
    Publisher: Preprints

    The COVID-19 pandemic spread of the coronavirus across the globe has affected our lives on many different levels. The world we knew before the spread of the virus has become another one. Every country has taken preventive measures, including social distancing, travel restrictions, and curfew, to control the spread of the disease. With these measures implemented, people have shifted to social media platforms in the online sphere, such as Twitter, to maintain connections. In this paper, we describe a coronavirus data set of Arabic tweets collected from January 1, 2020, primarily from hashtags populated from Saudi Arabia. This data set is available to the research community to glean a better understanding of the societal, economical, and political effects of the outbreak and to help policy makers make better decisions for fighting this epidemic.

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