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CO2 emissions changes due to COVID-19: modified SSP2-4.5 to account for sector activity level

Monthly CO2 emission projections, modified by the country-specific impacts of COVID-19 lockdown.  This repository holds the netcdf files for CO2 emissions projected by the scenario SSP2-4.5, from the Scenario4MIPs database ( <a href="https://esgf-node.llnl.gov/search/input4mips/">https://esgf-node.llnl.gov/search/input4mips/</a>), modified by the country and sector activity levels associated with lockdown, projected out for 3 years after 2020 before returning to baseline. The details of these activity estimates can be found in <a href="https://github.com/Priestley-Centre/COVID19_emissions">https://github.com/Priestley-Centre/COVID19_emissions</a>. The methodology behind these calculations is based on <a href="https://github.com/Rlamboll/modify_COVID19_netCDF_Emissions/">https://github.com/Rlamboll/modify_COVID19_netCDF_Emissions/</a>, a slight modification of the approach used in <a href="https://zenodo.org/record/3947917#.XxR_qyhKhPZ">https://zenodo.org/record/3947917#.XxR_qyhKhPZ</a> for aerosols emissions, and version numbers used here are consistent with the data seen in that database. We present only a single scenario (called 2-year blip, featuring a one year recovery after the end of the 2 years) compared to the baseline.  Funding was provided by the European Union’s Horizon 2020 Research and Innovation Programme under grant agreement nos. 820829 (CONSTRAIN) <a href="http://constrain-eu.org/">http://constrain-eu.org/</a>

Weekly NOx aviation emissions changes due to COVID-19: modified SSP2-4.5 to account for sector activity level

Weekly NOx aviation emissions estimates for 2020 until 21/07/2020, modified by the country-specific impacts of COVID-19 lockdown.  This repository holds the netcdf files for NOx emissions projected by the scenario SSP2-4.5, from the Scenario4MIPs database (<a href="https://esgf-node.llnl.gov/search/input4mips/">https://esgf-node.llnl.gov/search/input4mips/</a>), modified by the country and sector activity levels associated with lockdown for 2020, with observation-based data up until the 5th of July and a fixed estimate thereafter. This is the weekly equivalent of the aviation file in <a href="https://zenodo.org/record/3951601#.XxYBsihKhPY">https://zenodo.org/record/3951601#.XxYBsihKhPY</a> for a shorter time period, although the version number is different since we have more information available and the normalisation process has been improved.  Funding was provided by the European Union’s Horizon 2020 Research and Innovation Programme under grant agreement nos. 820829 (CONSTRAIN) <a href="http://constrain-eu.org/">http://constrain-eu.org/</a>  see <a href="https://github.com/Priestley-Centre/COVID19_emissions">https://github.com/Priestley-Centre/COVID19_emissions</a> for more details on the methodology.

opencc-zeroJun 2020View details →

dryad36/100

Data from: Estimating transmission dynamics and serial interval of the first wave of COVID-19 infections under different control measures: A statistical analysis in Tunisia from February 29 to May 5, 2020

Background: Describing transmission dynamics of the outbreak and impact of intervention measures are critical to planning responses to future outbreaks and providing timely information to guide policy makers decision. We estimate serial interval (SI) and temporal reproduction number (Rt) of SARS-CoV-2 in Tunisia. Methods: We collected data of investigations and contact tracing between March 1, 2020 and May 5, 2020 as well as illness onset data during the period February 29-May 5, 2020 from National Observatory of New and Emerging Diseases of Tunisia. Maximum likelihood (ML) approach is used to estimate dynamics of Rt. Results: 491 of infector-infectee pairs were involved, with 14.46% reported pre-symptomatic transmission. SI follows Gamma distribution with mean 5.30 days [95% CI 4.66-5.95] and standard deviation 0.26 [95% CI 0.23-0.30]. Also, we estimated large changes in Rt in response to the combined lockdown interventions. The Rt moves from 3.18 [95% CI 2.73-3.69] to 1.77 [95% CI 1.49-2.08] with curfew prevention measure, and under the epidemic threshold (0.89 [95% CI 0.84-0.94]) by national lockdown measure. Conclusions: Overall, our findings highlight contribution of interventions to interrupt transmission of SARS-CoV-2 in Tunisia.

FakeCovid- A Multilingual Cross domain Fact Check Dataset for COVID-19

FakeCovid is the first multilingual cross-domain dataset of 7623 fact-checked news articles for COVID-19, collected from 04/01/2020 to 01/07/2020. We have collected the fact-checked articles from 92 fact-checking websites after obtaining references from Poynter and Snopes. We have manually annotated the collected articles into 11 categories of the fact-checked news according to their content. We ultimately generated dataset is in 40 languages from 105 countries.

opencc-by-4.0Aug 2020View details →

Facebook activity of Romanian libraries during the COVID-19 crisis

This dataset shows the activity on Facebook (no of posts, shares, and reactions) of the Romanian county libraries during the COVID-19 crisis.

opencc-by-4.0Aug 2020View details →

Pediatric Asthma Healthcare Utilization, Viral Testing, and Air Pollution Changes during the COVID-19 Pandemic

Data related to asthma care encounters during the COVID19 pandemic.

Observational studies on preventive measures and treatments for Covid-19

In the course of our PubMed searches and preprints from MedRxiv, we identified a number of observational studies on preventive measures and treatments for Covid-19 that we have included in our systematic review. This file is updated regularly.

opencc-zeroAug 2020View details →

dryad36/100

Data for: Psychological distress, anxiety, suicidality, and wellbeing in New Zealand during the COVID-19 lockdown: a cross-sectional study

Dataset for following paper. Introduction New Zealand's early response to the novel coronavirus pandemic included a strict lockdown which eliminated community transmission of COVID-19. However, this success was not without cost, both economic and social. In our study, we examined the psychological wellbeing of New Zealanders during the COVID-19 lockdown when restrictions reduced social contact, limited recreation opportunities, and resulted in job losses and financial insecurity. Methods We conducted an online panel survey of a demographically representative sample of 2010 adult New Zealanders. The survey contained three standardised measures – the Kessler Psychological Distress Scale (K10), the GAD-7, and the Well-Being Index (WHO-5) – as well as questions designed specifically to measure family violence, suicidal ideation, and alcohol consumption. It also included items assessing positive aspects of the lockdown. Results Thirty percent of respondents reported moderate to severe psychological distress (K10), 16% moderate to high levels of anxiety, and 39% low wellbeing; well above baseline measures. Poorer outcomes were seen among young people and those who had lost jobs or had less work, those with poor health status, and who had past diagnoses of mental illness. Suicidal ideation was reported by 6%, with 2% reporting making plans for suicide and 2% reporting suicide attempts. Suicidality was highest in those aged 18–34. Just under 10% of participants had directly experienced some form of family harm over the lockdown period. However, not all consequences of the lockdown were negative, with 64% reporting 'silver linings', which included enjoying working from home, spending more time with family, and a quieter, less polluted environment. Conclusions New Zealand's lockdown successfully eliminated COVID-19 from the community, but our results show this achievement brought a significant psychological toll. Although much of the debate about lockdown measures has focused on their economic effects, our findings emphasise the need to pay equal attention to their effects on psychological wellbeing.

opencc-by-4.0Sep 2020View details →

Contaminación atmosférica durante el COVID-19

La enfermedad respiratoria aguda severa SARS-CoV-2 o simplemente COVID-19, tiene impactos negativos directos sobre la salud de las personas a nivel mundial. Pero a su vez, ha generado impactos positivos sobre el medio ambiente, debido a la cuarentena y la paralización de las diferentes actividades. Considerando que ha repercutido principalmente sobre la atmósfera, muchos de los investigadores se han centrado en estudiar los impactos producidos por esta situación. De esta manera, nuestro aporte consiste en recopilar los impactos informados por la literatura científica en el entorno internacional, los cuales se compilaron del 04 de julio al 05 de setiembre del 2020, mediante la selección y búsqueda en las diferentes bases de datos reconocidas, encontrando 297 trabajos publicados desde el 01 de enero del 2020. Los temas de búsqueda fueron: “environmental impact coronavirus”, “COVID-19 impact”, “air pollution COVID-19”. Todas las investigaciones que contenían información sobre un impacto o respuesta a la pandemia de COVID-19 relacionados con la contaminación del aire se consideraron relevantes para la elaboración de la base de datos. Para cada publicación, se extrajo información:

opencc-by-4.0Sep 2020View details →

Aerocargo Trafic Argentina - from 2014 to 2019 (no covid-19 data included) Critical Infrastructure Risk Research Program

Proyectos Bianuales de Investigación Universidad Nacional de Cuyo Propuestas metodológicas y modelos de concepción, operación y gestión para la reducción de vulnerabilidad e incremento de la resiliencia en la nueva generación de infaestructuras crítica de américa latina. Código Sigeva:  B083 Resolución Nro.:  Otorgamiento: RES 4142/2019-R- Expediente de pago: NOTA- CUY:38892/2019

opencc-by-4.0Oct 2020View details →

DAS data of Penn State FORESEE array during the COVID-19 measures

This repository contains data used in the paper "Seismic noises recorded by infrastructure fiber optics reveal the impact of COVID-19 measures on human activities, The Seismic Record, submitted"

opencc-by-nc-4.0Oct 2020View details →

Emission primarily drives nationwide air quality changes during and after the COVID-19 lockdown in China

Research data includes air pollutant observation data, wildfire data and meteorological data.

opencc-by-4.0Apr 2020View details →

Convivencia en el hogar durante el COVID-19

En este viernes en nuestro programa "Foro Abierto” hablamos sobre la Convivenica en el hogar durante el COVID-19 junto al Ph.D Pablo Ruisoto, docente de la Universidad de Navarra, Dra. Silvia Vaca docente investigadora del grupo de Psicológica Clínica y de la Salud de la UTPL y el Mgtr. Manuel Yunga, Coordinador del Área de Formación Integral de la UTPL

opencc-by-4.0Oct 2020View details →

Teaching scenarios in COVID-19 times

Teaching scenarios in COVID-19 times

openother-openOct 2020View details →

Misinformation of COVID-19 on Twitter

The dataset of Misinformation of COVID-19 on Twitter is crawled from the social media Twitter. This data set contains Twitter crawling about the tweets of the Indonesian COVID-19 community. The data is used as preliminary data for classifying misinformation tweets about COVID-19.The dataset is in xlsx file.

opencc-by-4.0Oct 2020View details →

Data set, combining epidemiological, genetics, and government stringency data of COVID-19 pandemic.

This data set combines epidemiological, genetics, and government stringency data of COVID-19 pandemics, all from open data sources. The sources are: Our World in Data, Worldometer, GISAID-Nextstrain, and the Oxford COVID-19 Government Response Tracker (OxCGRT). The cut off date of the first version is at the end of June 2020.  The simple data set is provided as an Excel workbook, where the first, "readme" worksheet describes the details of data of all the worksheets in the data set.  This is a working data set, expected to be refreshed over time.  Raw data are not cleaned - this collection is a tool to check various hypotheses regarding possible relations among the various data types. Simple visualisations of data relations are provided in a separate sheet.

Single-cell immune repertoire sequencing of two convalescent COVID-19 patients

Single-cell immune repertoire sequencing of two convalescent COVID-19 patients using 10x genomics 5' immune profiling. Resulting output files are from the count and vdj functions from 10x genomic's cellranger v3.1.0.

Imperial College London COVID-19 outputs Jan-Sep 20

Bibliographic records of Imperial College London COVID-19 research outputs. Includes journal articles, preprints, datasets, reports and software/code.  Publication dates 01.01.2020 - 30.09.2020.

Mental health consequences of the Covid-19 outbreak in Spain

The objective is to analyze the effects of the pandemic and alarm situation on the psychological health, loneliness, intersectional discrimination and spiritual wellbeing of the general population in a longitudinal way in three moments: after two weeks of the beginning of the confinement (between March 21 and 29), after a month (between April 13 and 27), and after two months (between May 21 and June 4), with the beginning of the deconfinement and return to the new normality. The study received the approval of the Deontological Commission of the Faculty of Psychology of the Complutense University of Madrid (pr_2019_20_029) prior to its implementation. The signing of the informed consent and acceptance of the data protection laws was also included in the evaluation. The evaluations were carried out by means of an online survey, with a sample of 3480 persons in the first data collection and of 1041 and 569 persons in the successive moments of evaluation. The presence of depressive symptoms, anxiety and posttraumatic stress disease (PTSD) was evaluated by means of screening tests. Sociodemographic data, variables about Covid-19, loneliness, psychological well-being, social support, discrimination and a sense of belonging were collected. Participants Recruitment consisted of sending requests for participation to people belonging to databases of different institutions: students and workers in public organizations such as the Complutense University of Madrid and the academic Chair Against Stigma, and private organizations such as the company Group 5. These databases contain sufficient data to perform reasonable sampling of the Spanish population. To increase the sample size as much as possible participants were asked to help with its dissemination. The percentage of people recruited in this way was small, estimated as less than 5%. The inclusion criteria were: 1. To be over 18 years old; 2. To be living in Spain during the Covid-19 health emergency; 3. To have agreed to participate in the second evaluation of the study. A total of 3480 people participated in the first evaluation. For the subsequent evaluations, those people who had previously agreed to participate in the study were contacted by email in a longitudinal way (specific section of the evaluation), recruiting a total of N = 1041 in the second data collection, and in the third evaluation N = 569. In the resulting sample, a majority of women (81%) was obtained as opposed to 51% of the general population. With respect to age, a greater equivalence was obtained, although with a higher percentage of people under 60 years than in the general population: 29% (18-30), 64% (31-59) and 7% (60-80) for the three respective groups, compared to 10%, 44% and 19% for the general population (the remaining 5% do not meet the criteria for inclusion/exclusion). Variables and instruments The following variables and instruments were included in the assessment: Sociodemographic variables Using ad hoc questions, data was collected on age (subsequently grouped into clusters: 18-30, 31-59, 60-80); gender identity; marital status (single, married, divorced, separated, widower); educational level (elementary studies, high school, vocational training, university, postgraduate); economic situation (subjective perception from very bad to very good). COVID-19 related variables Suffering from symptoms (yes, no); existence of a family members or close relatives who are infected (yes, no); perception of the information received on the alarm situation (considering that they have sufficient information, or that they are over-informed).  Mental health Mental health was assessed with the PHQ-4 composed by the Patient Health Questionnaire 2 (PHQ-2) (Kroenke, Spitzer, Williams, & Löwe, 2009) and the Generalized Anxiety Disorder Scale (GAD-2) (Spitzer, Kroenke, Williams, & Löwe, 2006). The PHQ-2 was used in its Spanish version (Diez-Quevedo, Rangil, Sanchez-Planell, Kroenke, & Spitzer, 2001) and is a brief self-report questionnaire that addresses the frequency of depressive symptoms. It consists of 2 Likert-type questions ranging from 0 “never” to 3 “every day”. Higher scores indicate greater symptomatology, providing a severity score that ranges from 0 to 6. GAD-2 was also used in its Spanish version (Garcia-Campayo et al., 2014). The GAD-2 Questionnaire includes the first 2 items of the GAD-7 Likert format, with a maximum score of 6 points. Loneliness Measured by the 3 item version of the UCLA Loneliness Scale (UCLA-3) in its Spanish version and self-applied (Russell 1996; Velarde-Mayol et al. 2016). The three items in Likert format with three response options (1 rarely, 2 sometimes, 3 often), address three dimensions of loneliness: relational connection, social connection, and self-perceived isolation. Intersectional discrimination Intersectional discrimination was evaluated by means of the Intersectional Day-to-Day Discrimination Index (InDI-D) (Scheim & Bauer, 2019), in its Spanish version, which was translated by the authors of this study. This scale provides a measure of the intersectional discrimination that can be produced by different conditions: gender, ethnicity, mental health diagnosis, and in this case, the presence of COVID-19 was also included. We used the main scale formed by 9 Likert-type items (e.g. “Since the sanitary emergency caused by COVID-19 in Spain, have you been treated as if you were someone hostile, unhelpful or rude?”) with four response options (1 “never” – 4 “many times”). The different questions evaluated the presence of intersectional discrimination from the beginning of the alarm situation generated by the coronavirus. The higher the score the more discrimination suffered. Internalized stigma Internalized stigma was evaluated with two items adapted from the Internalized Stigma of Mental Illness (ISMI) scale (Boyd Ritsher, Otilingam & Grajales, 2003). The items (“Since the emergency situation generated by the coronavirus, have you avoided contacting people –in those cases permitted during lockdown– to avoid rejection?”; “Since the emergency situation generated by the coronavirus, have you felt that the people who are not in your situation are unable to understand you?”) were modified to evaluate intersectional internalized stigma, the self-stigma that can be generated by diverse conditions. These items refer to the alienation and social withdrawal dimensions taken from the original scale. It was evaluated with the same Likert-type scale as the one used to measure the intersectional perceived discrimination. Social support Social support was evaluated by means of the Multidimensional Scale of Perceived Social Support (EMAS) (Zimet, Dahlem, Zimet, & Farley, 1988), adapted to a Spanish version (Landeta & Calvete, 2002). The scale, made up of 12 Likert-type items with 7 possible responses (1 “totally disagree” to 7 “totally agree”), evaluates the levels of perceived social support, identifying where the support comes from and how it is perceived. The EMAS explores three possible sources of perceived social support –family (4 items), friends (4 items) and relevant people (4 items)–, and offers a full measure of social support. Spiritual well-being Spiritual well-being was assessed using the Spanish version of the Functional Assessment of Chronic Illness Therapy Spiritual Well-Being (FACIT-Sp12) (Cella et al. 1998). This test evaluates physical, family, functional and spiritual well-being, focusing in this questionnaire only on spiritual well-being with two dimensions: meaning and peace. Four items were selected from the scale focusing on these aspects. The answers were Likert type from 0 (nothing) to 4 (a lot). Higher scores indicate greater well-being. Self-Compassion Scale (SCS) Was used in its Spanish version (Garcia-Campayo et al., 2014; Neff, 2003). The scale evaluates how the subject usually acts towards himself in difficult moments in different dimensions. Here we explore with 6 items the following three: self-love, common humanity and mindfulness. The items are Likert type (1 to 5). Higher scores indicate more self-pity. Sense of belonging The sense of belonging to different work/study groups, friends, family and neighborhood or community was evaluated through four Likert-type items (1 much - 4 nothing) (Hernán Montalbán & Rodríguez Moreno 2017).

Robust estimates of the true (population) infection rate for COVID-19: a backcasting approach

Differences in COVID-19 testing and tracing across countries, as well as changes in testing within each country over time, make it difficult to estimate the true (population) infection rate based on the confirmed number of cases obtained through RNA viral testing. We applied a backcasting approach to estimate a distribution for the true (population) cumulative number of infections (infected and recovered) for 15 developed countries. Our sample comprised countries with similar levels of medical care and with populations that have similar age distributions. Monte Carlo methods were used to robustly sample parameter uncertainty. We found a strong and statistically significant negative relationship between the proportion of the population who test positive and the implied true detection rate. Despite an overall improvement in detection rates as the pandemic has progressed, our estimates showed that, as at 31 August 2020, the true number of people to have been infected across our sample of 15 countries was 6.2 (95% CI: 4.3–10.9) times greater than the reported number of cases. In individual countries, the true number of cases exceeded the reported figure by factors that range from 2.6 (95% CI: 1.8–4.5) for South Korea to 17.5 (95% CI: 12.2–30.7) for Italy.