During the peak of the coronavirus (COVID-19) crisis (March-April 2020) when many countries worldwide introduced lockdown measures, e-commerce share in total retail sales saw proportions that were not seen before. In the United Kingdom, where an already mature e-commerce market exists, e-commerce share saw as high as **** percent, before stabilizing in the subsequent periods. In the most current period (as of January 31, 2021), United Kingdom, United States and Canada were the leading countries where e-commerce had a higher share as a proportion of total retail, at **, **, and ** percent, respectively.

Dataset

This dataset was created by Vivek Singh

Contents

This article investigates: What groups were most affected by COVID-19? How did mental health diagnosis and substance use disorder patterns change? Most importantly, what can these data show us about what was done immediately after COVID-19 to alleviate mental health and substance use diagnoses and how might we better address future public health challenges? This analysis utilized data from multiple national surveys to examine trends in mental health and substance use and the availability of treatment facilities. The analysis focused on both before and after the COVID-19 pandemic specifically in 2018 and 2022. The primary data sources were the General Social Survey (GSS), the Mental Health Client-Level Data (MH-CLD) data set, and the National Substance Use and Mental Health Services Survey (N-SUMHSS). This analysis reveals significant shifts in both mental health and substance use trends following the COVID-19 pandemic.

Data were collected from 113 participants, who shared their physical activity (PA) data using privately owned smart watches and activity trackers from Garmin and Fitbit. This data set consists of two data files: "data.csv" and "data raw.csv": The first file ("data.csv") contains daily averages for steps, total energy expenditure (TEE), activity energy expenditure (AEE), moderate-to-vigorous physical activity (MVPA), light PA (LPA), moderate PA (MPA), vigorous PA (VPA), and sedentary time, grouped by month. In addition, daily averages for the whole year of 2019 and 2020 are included. Finally, separate variables for the first and second half of March 2020 (pre- and post COVID-19 lockdown in Norway) are included. The second file ("data raw.csv") contains raw daily values for steps, TEE, AEE, MVPA, LPA, MPA, VPA, sedentary time, and non-wear time.

Introduction

There are several works based on Natural Language Processing on newspaper reports. Mining opinions from headlines [ 1 ] using Standford NLP and SVM by Rameshbhaiet. Al.compared several algorithms on a small and large dataset. Rubinet. al., in their paper [ 2 ], created a mechanism to differentiate fake news from real ones by building a set of characteristics of news according to their types. The purpose was to contribute to the low resource data available for training machine learning algorithms. Doumitet. al.in [ 3 ] have implemented LDA, a topic modeling approach to study bias present in online news media.

However, there are not many NLP research invested in studying COVID-19. Most applications include classification of chest X-rays and CT-scans to detect presence of pneumonia in lungs [ 4 ], a consequence of the virus. Other research areas include studying the genome sequence of the virus[ 5 ][ 6 ][ 7 ] and replicating its structure to fight and find a vaccine. This research is crucial in battling the pandemic. The few NLP based research publications are sentiment classification of online tweets by Samuel et el [ 8 ] to understand fear persisting in people due to the virus. Similar work has been done using the LSTM network to classify sentiments from online discussion forums by Jelodaret. al.[ 9 ]. NKK dataset is the first study on a comparatively larger dataset of a newspaper report on COVID-19, which contributed to the virus’s awareness to the best of our knowledge.

2 Data-set Introduction

2.1 Data Collection

We accumulated 1000 online newspaper report from United States of America (USA) on COVID-19. The newspaper includes The Washington Post (USA) and StarTribune (USA). We have named it as “Covid-News-USA-NNK”. We also accumulated 50 online newspaper report from Bangladesh on the issue and named it “Covid-News-BD-NNK”. The newspaper includes The Daily Star (BD) and Prothom Alo (BD). All these newspapers are from the top provider and top read in the respective countries. The collection was done manually by 10 human data-collectors of age group 23- with university degrees. This approach was suitable compared to automation to ensure the news were highly relevant to the subject. The newspaper online sites had dynamic content with advertisements in no particular order. Therefore there were high chances of online scrappers to collect inaccurate news reports. One of the challenges while collecting the data is the requirement of subscription. Each newspaper required $1 per subscriptions. Some criteria in collecting the news reports provided as guideline to the human data-collectors were as follows:

The headline must have one or more words directly or indirectly related to COVID-19.

The content of each news must have 5 or more keywords directly or indirectly related to COVID-19.

The genre of the news can be anything as long as it is relevant to the topic. Political, social, economical genres are to be more prioritized.

Avoid taking duplicate reports.

Maintain a time frame for the above mentioned newspapers.

To collect these data we used a google form for USA and BD. We have two human editor to go through each entry to check any spam or troll entry.

2.2 Data Pre-processing and Statistics

Some pre-processing steps performed on the newspaper report dataset are as follows:

Remove hyperlinks.

Remove non-English alphanumeric characters.

Remove stop words.

Lemmatize text.

While more pre-processing could have been applied, we tried to keep the data as much unchanged as possible since changing sentence structures could result us in valuable information loss. While this was done with help of a script, we also assigned same human collectors to cross check for any presence of the above mentioned criteria.

The primary data statistics of the two dataset are shown in Table 1 and 2.

Table 1: Covid-News-USA-NNK data statistics

No of words per headline

7 to 20

No of words per body content

150 to 2100

Table 2: Covid-News-BD-NNK data statistics No of words per headline

10 to 20

No of words per body content

100 to 1500

2.3 Dataset Repository

We used GitHub as our primary data repository in account name NKK^1. Here, we created two repositories USA-NKK^2 and BD-NNK^3. The dataset is available in both CSV and JSON format. We are regularly updating the CSV files and regenerating JSON using a py script. We provided a python script file for essential operation. We welcome all outside collaboration to enrich the dataset.

3 Literature Review

Natural Language Processing (NLP) deals with text (also known as categorical) data in computer science, utilizing numerous diverse methods like one-hot encoding, word embedding, etc., that transform text to machine language, which can be fed to multiple machine learning and deep learning algorithms.

Some well-known applications of NLP includes fraud detection on online media sites[ 10 ], using authorship attribution in fallback authentication systems[ 11 ], intelligent conversational agents or chatbots[ 12 ] and machine translations used by Google Translate[ 13 ]. While these are all downstream tasks, several exciting developments have been made in the algorithm solely for Natural Language Processing tasks. The two most trending ones are BERT[ 14 ], which uses bidirectional encoder-decoder architecture to create the transformer model, that can do near-perfect classification tasks and next-word predictions for next generations, and GPT-3 models released by OpenAI[ 15 ] that can generate texts almost human-like. However, these are all pre-trained models since they carry huge computation cost. Information Extraction is a generalized concept of retrieving information from a dataset. Information extraction from an image could be retrieving vital feature spaces or targeted portions of an image; information extraction from speech could be retrieving information about names, places, etc[ 16 ]. Information extraction in texts could be identifying named entities and locations or essential data. Topic modeling is a sub-task of NLP and also a process of information extraction. It clusters words and phrases of the same context together into groups. Topic modeling is an unsupervised learning method that gives us a brief idea about a set of text. One commonly used topic modeling is Latent Dirichlet Allocation or LDA[17].

Keyword extraction is a process of information extraction and sub-task of NLP to extract essential words and phrases from a text. TextRank [ 18 ] is an efficient keyword extraction technique that uses graphs to calculate the weight of each word and pick the words with more weight to it.

Word clouds are a great visualization technique to understand the overall ’talk of the topic’. The clustered words give us a quick understanding of the content.

4 Our experiments and Result analysis

We used the wordcloud library^4 to create the word clouds. Figure 1 and 3 presents the word cloud of Covid-News-USA- NNK dataset by month from February to May. From the figures 1,2,3, we can point few information:

In February, both the news paper have talked about China and source of the outbreak.

StarTribune emphasized on Minnesota as the most concerned state. In April, it seemed to have been concerned more.

Both the newspaper talked about the virus impacting the economy, i.e, bank, elections, administrations, markets.

Washington Post discussed global issues more than StarTribune.

StarTribune in February mentioned the first precautionary measurement: wearing masks, and the uncontrollable spread of the virus throughout the nation.

While both the newspaper mentioned the outbreak in China in February, the weight of the spread in the United States are more highlighted through out March till May, displaying the critical impact caused by the virus.

We used a script to extract all numbers related to certain keywords like ’Deaths’, ’Infected’, ’Died’ , ’Infections’, ’Quarantined’, Lock-down’, ’Diagnosed’ etc from the news reports and created a number of cases for both the newspaper. Figure 4 shows the statistics of this series. From this extraction technique, we can observe that April was the peak month for the covid cases as it gradually rose from February. Both the newspaper clearly shows us that the rise in covid cases from February to March was slower than the rise from March to April. This is an important indicator of possible recklessness in preparations to battle the virus. However, the steep fall from April to May also shows the positive response against the attack. We used Vader Sentiment Analysis to extract sentiment of the headlines and the body. On average, the sentiments were from -0.5 to -0.9. Vader Sentiment scale ranges from -1(highly negative to 1(highly positive). There were some cases

where the sentiment scores of the headline and body contradicted each other,i.e., the sentiment of the headline was negative but the sentiment of the body was slightly positive. Overall, sentiment analysis can assist us sort the most concerning (most negative) news from the positive ones, from which we can learn more about the indicators related to COVID-19 and the serious impact caused by it. Moreover, sentiment analysis can also provide us information about how a state or country is reacting to the pandemic. We used PageRank algorithm to extract keywords from headlines as well as the body content. PageRank efficiently highlights important relevant keywords in the text. Some frequently occurring important keywords extracted from both the datasets are: ’China’, Government’, ’Masks’, ’Economy’, ’Crisis’, ’Theft’ , ’Stock market’ , ’Jobs’ , ’Election’, ’Missteps’, ’Health’, ’Response’. Keywords extraction acts as a filter allowing quick searches for indicators in case of locating situations of the economy,

Note: The DOI of the related paper will be provided upon publication.These datasets contain information on autoantibody and anti-SARS-CoV-2 IgG levels.In this study, 478 healthcare workers and 48 patients were followed prospectively over 5 visits from May 2020 to Sept 2021. SARS-CoV-2 serology and autoantibodies were assessed using planar and bead-based arrays. Detected epitopes were validated in blood and cerebrospinal fluid from two independent cohorts of Neuro-COVID patients (n=25) and pre-pandemic healthy controls (n=29).The datasets contain:SARS-CoV-2 serological profiles measured in all samples (above) using 3-plex bead arrays. The data is reported as raw and normalized MFI (AU) and compound serostatus.Proteome-wide autoantibody profiles measured in 12 sample pools using planar arrays. The data is reported as median fluorescent intensity, in raw and normalized arbitrary units (MFI [AU]), and reactivity classification.Targeted autoantibody profiles measured in 478 healthcare workers and 48 patients across 5 time points using 363-plex bead arrays. The data is reported as raw and normalized MFI (AU), fold change (FC) across infection, and new-onset classification.Peptide epitope mapping autoantibody profiles measured in a 142 healthcare workers and COVID-19 patients across 2 time points, and all Neuro-COVID patients (n=25) and pre-pandemic healthy controls (n=29), using 93-plex bead arrays. The data is reported as raw and normalized MFI (AU), and fold change (FC) across infection.Source data is provided with the paper.Access to this individual-level human data can be granted for non-commercial validation purposes and upon reasonable request to the provided contact. A reasonable request should contain the following:Name of PI and host organizationContact detailsThe scientific purpose of the data access requestCommitment to inform when the data has been used in a publicationCommitment not to host or share the data outside the requesting organizationStatement of non-commercial use of data

Before the coronavirus (COVID-19) pandemic, 17 percent of U.S. employees worked from home 5 days or more per week, a share that increased to 44 percent during the pandemic. The outbreak of the COVID-19 pandemic accelerated the remote working trend, as quarantines and lockdowns made commuting and working in an office close to impossible for millions around the world. Remote work, also called telework or working from home (WFH), provided a solution, with employees performing their roles away from the office supported by specialized technology, eliminating the commute to an office to remain connected with colleagues and clients. What enables working from home?

To enable remote work, employees rely on a remote work arrangements that enable hybrid work and make it safe during the COVID-19 pandemic. Technology supporting remote work including laptops saw a surge in demand, video conferencing companies such as Zoom jumped in value, and employers had to consider new communication techniques and resources. Is remote work the future of work?

The response to COVID-19 has demonstrated that hybrid work models are not necessarily an impediment to productivity. For this reason, there is a general consensus that different remote work models will persist post-COVID-19. Many employers see benefits to flexible working arrangements, including positive results on employee wellness surveys, and potentially reducing office space. Many employees also plan on working from home more often, with 25 percent of respondents to a recent survey expecting remote work as a benefit of employment. As a result, it is of utmost importance to acknowledge any issues that may arise in this context to empower a hybrid workforce and ensure a smooth transition to more flexible work models.

In September 2020, a survey found that ** percent of respondents in the United States had been buying household supplies online before the coronavirus pandemic. After COVID-19, there was an expected ** percentage point increase in consumers buying these home items online. The same survey was conducted in February 2021 and it revealed that spending intentions decreased across many categories. Fitness and wellness, groceries, personal care products, and household supplies were among the few segments where post-COVID-19 growth was still expected. The change is real The coronavirus pandemic has upended lives worldwide, from how we work to shop and socialize, in general. In a survey published in March 2021, U.S consumers were asked about the important attributes of shopping online, among which the most chosen answers were faster delivery and in-stock availability. However, the share of consumers that shopped online for the first time is relatively minimal. For instance, only ****percent of German and Japanese respondents had never purchased online before 2020. Shopping online more than ever The e-commerce purchase frequency has also changed. In the U.S., over ** percent of respondents mentioned that their household goods online purchasing cycle had increased compared to one month previously. In terms of traffic and reach, food and groceries, home and garden, and sports and outdoors were the fastest-growing e-commerce categories worldwide.

Title: The impact of COVID-19 on chronic care patients health care utilization and health outcomes in Haiti, Malawi, Mexico and Rwanda Original data source: Electronic Medical Records Date range: March 1st, 2019-Feb 28th, 2021 Geographic region: Non-representative subnational regions of Haiti, Malawi, Mexico, and Rwanda Clinical populations: Diabetes, HIV, and hypertension patients Level of data: Aggregated by country, sex, age category, clinical population, and pre- vs post-COVID-19 period Size of the data: 35 KB Research question/s that use the dataset 1. Has the COVID-19 pandemic changed the risk of poor clinical outcomes among chronic care patients living with HIV, cardiovascular disease and diabetes programs in Haiti, Malawi, Mexico and Rwanda? 2. Among these patients, how has care utilization changed during the COVID-19 pandemic? Useful Links https://icoda-research.org/project/dp-pih-covco/

Data access information: In order to request access to data, please contact Jean Claude Mugunga, jmugunga@pih.org, with a description of your study team, your research questions, and which countr(ies) and clinical program(s) you would like data for. Note that Dr. Mugugna will reach out to representatives from each country you request data from for approval before sharing the data.

Cambodia’s economic recovery solidified in 2022 with real growth accelerating to 5.2 percent. After shifting to “living with COVID-19” in late 2021, the economy is firmly on a path to recovery and has now returned to its pre-pandemic growth trajectory. Initially led by the strong performance of export-oriented manufacturing, growth drivers are rotating to the services and agriculture sectors. Driven by pent-up consumer demand, the overall contribution of the services sector to economic growth is returning to the 2019 levels. Underpinned by the complete removal of COVID-19-related mobility restrictions and China’s recent reopening, international arrivals have picked up, reaching 830,000 during the first two months of 2023, approaching pre-pandemic levels.

This is the README file for the scripts of the preprint "Self-Perceived Loneliness and Depression During the COVID-19 Pandemic: a Two-Wave Replication Study" by Carollo et al. (2022)

Access the pre-print here: https://ucl.scienceopen.com/document/read?vid=0769d88b-e572-48eb-9a71-23ea1d32cecf

Abstract: Background: The global COVID-19 pandemic has forced countries to impose strict lockdown restrictions and mandatory stay-at-home orders with varying impacts on individual’s health. Combining a data-driven machine learning paradigm and a statistical approach, our previous paper documented a U-shaped pattern in levels of self-perceived loneliness in both the UK and Greek populations during the first lockdown (17 April to 17 July 2020). The current paper aimed to test the robustness of these results by focusing on data from the first and second lockdown waves in the UK. Methods: We tested a) the impact of the chosen model on the identification of the most time-sensitive variable in the period spent in lockdown. Two new machine learning models - namely, support vector regressor (SVR) and multiple linear regressor (MLR) were adopted to identify the most time-sensitive variable in the UK dataset from wave 1 (n = 435). In the second part of the study, we tested b) whether the pattern of self-perceived loneliness found in the first UK national lockdown was generalizable to the second wave of UK lockdown (17 October 2020 to 31 January 2021). To do so, data from wave 2 of the UK lockdown (n = 263) was used to conduct a graphical and statistical inspection of the week-by-week distribution of self-perceived loneliness scores. Results: In both SVR and MLR models, depressive symptoms resulted to be the most time-sensitive variable during the lockdown period. Statistical analysis of depressive symptoms by week of lockdown resulted in a U-shaped pattern between week 3 to 7 of wave 1 of the UK national lockdown. Furthermore, despite the sample size by week in wave 2 was too small for having a meaningful statistical insight, a qualitative and descriptive approach was adopted and a graphical U-shaped distribution between week 3 and 9 of lockdown was observed. Conclusions: Consistent with past studies, study findings suggest that self-perceived loneliness and depressive symptoms may be two of the most relevant symptoms to address when imposing lockdown restrictions.

In particular, the folder includes the scripts for the pre-processing, training, and post-processing phases of the research.

==== PRE-PROCESSING WAVE 1 DATASET ==== - "01_preprocessingWave1.py": this file include the pre-processing of the variables of interest for wave 1 data; - "02_participantsexcludedWave1.py": this file include the script adopted to implement the exclusion criteria of the study for wave 1 data; - "03_countryselectionWave1.py": this file include the script to select the UK dataset for wave 1.

==== PRE-PROCESSING WAVE 2 DATASET ==== - "04_preprocessingWave1.py": this file include the pre-processing of the variables of interest for wave 2 data; - "05_participantsexcludedWave1.py": this file include the script adopted to implement the exclusion criteria of the study for wave 2 data; - "06_countryselectionWave1.py": this file include the script to select the UK dataset for wave 2.

==== TRAINING ==== - "07_MLR.py": this file includes the script to run the multiple regression model; - "08_SVM.py": this file includes the script to run the support vector regression model.

==== POST-PROCESSING: STATISTICAL ANALYSIS ==== - "09_KruskalWallisTests.py": this file includes the script to run the multipair and the pairwise Kruskal-Wallis tests.

This dataset and code package supports the study titled "Liquidity Stress, ETF Returns, and Structural Breaks: Evidence from Pre- and Post-COVID Markets."
It investigates liquidity stress measures, ETF trading performance, and structural breaks around the COVID-19 shock, using daily data from 2010–2025.

Included in this deposit:

• /src/full_analysis.py: Complete Python script that replicates all results, figures, and statistical tests in one file.

• /data/:

  • F-F_Research_Data_5_Factors_2x3_daily.CSV: Fama-French 5-Factor daily dataset.

  • F-F_Momentum_Factor_daily.CSV: Fama-French UMD (Momentum) factor daily dataset.

• Figures:

  • Figure 1.png: Alternative Liquidity Stress Measures.

  • Figure 2.png: Sharpe Ratios by ETF: Pre- and Post-COVID Comparison.

  • Figure 3.png: SPY Cumulative Returns (Pre/Post COVID).

  • Figure 4.png: Approximate Bai-Perron Structural Break Detection (SSR vs Break Date).

• README.md: Basic project instructions and file structure overview.

• LICENSE (CC BY 4.0).txt: License file permitting reuse with attribution.

• requirements.txt: Python package requirements for full reproducibility.

Key methods implemented:

• Amihud illiquidity calculation

• Rolling stress detection (top 5% extreme illiquidity)

• Simple ETF trading strategy backtests across holding periods (5, 15, 30 days)

• Pre- vs. Post-COVID Sharpe ratio comparisons

• Fama-French 5-Factor + Momentum factor regressions (Newey-West HAC standard errors)

• Chow Test for structural breaks at COVID onset

• Approximate Bai-Perron search for best breakpoints using Sum of Squared Residuals (SSR)

This work is licensed under Creative Commons Attribution 4.0 International (CC BY 4.0).

Researchers, practitioners, and students are welcome to reuse, adapt, or extend this dataset and code for academic, professional, or instructional purposes.

Largest changes in influenza vaccine rates, Pre- vs. Post-COVID-19.

In 2020, global gross domestic product declined by 6.7 percent as a result of the coronavirus (COVID-19) pandemic outbreak. In Latin America, overall GDP loss amounted to 8.5 percent.

IntroductionElectronic health records can be used to understand the diverse presentation of post-acute and long-term health outcomes following COVID-19 infection. In England, the UK Health Security Agency, in collaboration with the University of Oxford, has created the Evaluation of post-acute COVID-19 Health Outcomes (ECHOES) dataset to monitor how an initial SARS-CoV-2 infection episode is associated with changes in the risk of health outcomes that are recorded in routinely collected health data.MethodsThe ECHOES dataset is a national-level dataset combining national-level surveillance, administrative, and healthcare data. Entity resolution and data linkage methods are used to create a cohort of individuals who have tested positive and negative for SARS-CoV-2 in England throughout the COVID-19 pandemic, alongside information on a range of health outcomes, including diagnosed clinical conditions, mortality, and risk factor information.ResultsThe dataset contains comprehensive COVID-19 testing data and demographic, socio-economic, and health-related information for 44 million individuals who tested for SARS-CoV-2 between March 2020 and April 2022, representing 15,720,286 individuals who tested positive and 42,351,016 individuals who tested negative.DiscussionWith the application of epidemiological and statistical methods, this dataset allows a range of clinical outcomes to be investigated, including pre-specified health conditions and mortality. Furthermore, understanding potential determinants of health outcomes can be gained, including pre-existing health conditions, acute disease characteristics, SARS-CoV-2 vaccination status, and genomic variants.

AnLoCOV is a large anonymized longitudinal GPS location dataset for studies spanning over pre- and post-COVID periods in Ecuador. This project contains data collected using the Global Positioning System (GPS) from mobile devices. This GPS data was acquired using the Google Location History, accessible in the Google Maps application. It includes data from 338 people over ten years (2012–2022). The dataset's goal is to promote studies on human mobility behavior and activity spaces using GPS data from mobile devices.

This dataset reports the daily reported number of the 7-day moving average rates of Deaths involving COVID-19 by vaccination status and by age group.

Learn how the Government of Ontario is helping to keep Ontarians safe during the 2019 Novel Coronavirus outbreak.

Effective November 14, 2024 this page will no longer be updated. Information about COVID-19 and other respiratory viruses is available on Public Health Ontario’s interactive respiratory virus tool: https://www.publichealthontario.ca/en/Data-and-Analysis/Infectious-Disease/Respiratory-Virus-Tool

Data includes:

• Date on which the death occurred
• Age group
• 7-day moving average of the last seven days of the death rate per 100,000 for those not fully vaccinated
• 7-day moving average of the last seven days of the death rate per 100,000 for those fully vaccinated
• 7-day moving average of the last seven days of the death rate per 100,000 for those vaccinated with at least one booster

Additional notes

As of June 16, all COVID-19 datasets will be updated weekly on Thursdays by 2pm.

As of January 12, 2024, data from the date of January 1, 2024 onwards reflect updated population estimates. This update specifically impacts data for the 'not fully vaccinated' category.

On November 30, 2023 the count of COVID-19 deaths was updated to include missing historical deaths from January 15, 2020 to March 31, 2023.

CCM is a dynamic disease reporting system which allows ongoing update to data previously entered. As a result, data extracted from CCM represents a snapshot at the time of extraction and may differ from previous or subsequent results. Public Health Units continually clean up COVID-19 data, correcting for missing or overcounted cases and deaths. These corrections can result in data spikes and current totals being different from previously reported cases and deaths. Observed trends over time should be interpreted with caution for the most recent period due to reporting and/or data entry lags.

The data does not include vaccination data for people who did not provide consent for vaccination records to be entered into the provincial COVaxON system. This includes individual records as well as records from some Indigenous communities where those communities have not consented to including vaccination information in COVaxON.

“Not fully vaccinated” category includes people with no vaccine and one dose of double-dose vaccine. “People with one dose of double-dose vaccine” category has a small and constantly changing number. The combination will stabilize the results.

Spikes, negative numbers and other data anomalies: Due to ongoing data entry and data quality assurance activities in Case and Contact Management system (CCM) file, Public Health Units continually clean up COVID-19, correcting for missing or overcounted cases and deaths. These corrections can result in data spikes, negative numbers and current totals being different from previously reported case and death counts.

Public Health Units report cause of death in the CCM based on information available to them at the time of reporting and in accordance with definitions provided by Public Health Ontario. The medical certificate of death is the official record and the cause of death could be different.

Deaths are defined per the outcome field in CCM marked as “Fatal”. Deaths in COVID-19 cases identified as unrelated to COVID-19 are not included in the Deaths involving COVID-19 reported.

Rates for the most recent days are subject to reporting lags

All data reflects totals from 8 p.m. the previous day.

This dataset is subject to change.

The COVIDsortium study aims to identify host and pathogen correlates of protection and pathogenesis in SARS-CoV-2 infection. The prospective observational cohort study (n=1000) healthcare workers (HCWs) with serial data/sample collection pre & post COVID.

Dataset collected from Thai domestic travelers on travel destination preferences both on pre- and post-COVID-19 pandemic.

The Data Description file was conducted to describe variables for the dataset.

ABSTRACT This paper addresses the topic of post-covid-19 scenario projections. It aims to present a methodological framework for evaluating post-covid-19 scenarios. By applying the continuity principle - which establishes that there is an extension of the Pre-Impact (Time-1) period into the Post-Impact period (Time-2) - it argues that post-covid-19 conditions are socially latent in the pre-covid-19 period. More precisely, the unleashing of the covid pandemic in general, and of social distancing actions in particular, have accelerated pre-existing social change processes. This means that the more scenarios take into account this phenomenon, the greater their empirical adequacy. Based on these premises, the article seeks, on the one hand, to evaluate existing scenarios and, on the other, to propose a new scenario.