According to a survey on pet ownership conducted by Rakuten Insight in January 2022, 29 percent of the female respondents in Singapore indicated that they got their pets during the COVID-19 lockdowns. On the other hand, 65 percent of the male respondents indicated that they already had their pets before the lockdown.

According to a survey on pet ownership conducted by Rakuten Insight in January 2022, 41 percent of the respondents in Indonesia indicated that they got their pets during the COVID-19 lockdowns. On the other hand, 59 percent of the respondents indicated that they already had their pets before the lockdown.

Objectives A key challenge for behaviour change is by-passing the influence of habits. Habits are easily triggered by contextual cues; hence context changes have been suggested to facilitate behaviour change (i.e., habit discontinuity). We examined the impact of a COVID-19 lockdown in England on habitual consumption of sugar-sweetened beverages (SSBs). The lockdown created a naturalistic context change because it removed typical SSB consumption situations (e.g., going out). We hypothesised that SSB consumption would be reduced during lockdown compared to before and after lockdown, especially in typical SSB drinking situations. Design In two surveys among the same participants (N = 211, N = 160; consuming SSBs at least once/week) we assessed the frequency of SSBs and water consumption occasions before (Time 1), during (Time 2) and after lockdown (Time 3), across typical SSB and water drinking situations. We also assessed daily amount consumed in each period, and perceived habitualness of drinking SSBs and water. Results As predicted, participants reported fewer occasions of drinking SSBs during lockdown compared to before and after, especially in typical SSB drinking situations. However, the daily amount of SSBs consumed increased during lockdown, compared to before and after. Exploratory analyses suggest that during lockdown, participants increased their SSB consump¬¬tion at home, especially if they had stronger perceived habitualness of SSB consumption. Conclusion These findings suggest that SSB consumption is easily transferred to other situations when the consumption context changes, especially for individuals with strong consumption habits. Habitual consumption may be hard to disrupt if the behaviour is rewarding.

In March 2020, the WHO declared the coronavirus a pandemic. Since then, the German government has tried to control the spread of the virus with various restrictions. These restrictions had a direct impact on the life of German students. In this study, we investigate to what extent the restrictions led to a change of value priorities of German students. From January 2019 to January 2022, we conducted a cross-sectional study with four measurement points and, in total, 1,328 participants. Two measurement points were before the first outbreak of COVID-19 in Germany, one in the second lockdown phase and the third after two years in the pandemic. In this study, the students were asked to indicate their value priorities while solving a real-world decision problem important to them. Results suggest increased value priorities of the values Intellectual Fulfillment and Environment and Nature and a decrease of Family and Partner value priority as a direct effect of the second lockdown phase. We also found small differences regarding value priorities between the male and female subjects. The data show bounce-back effects as the pandemic became more normal to the students. In the long run, value priorities seem to be stable, with the exception of a longer-lasting increase in Freedom and Independence.

Business Needs Survey 2022 – Impact of the Covid-19 pandemic on the needs of businesses in the City. The City conducted the 2020 Business Needs Survey following the first lockdown initiated in response to Covid-19. The survey aimed to provide insight into the needs of small business operators to determine the best approach in supporting them to remain economically viable. The City has conducted 2021 and 2022 Covid-19 Business Needs Surveys. The responses document how organisations, industry sectors and members were impacted by the pandemic immediately before the 2021 four-month lockdown. See previous surveys

According to a survey on pet ownership conducted by Rakuten Insight in January 2022, ** percent of the respondents in Malaysia indicated that they got their pets during the COVID-19 lockdowns. On the other hand, ** percent of the respondents indicated that they already had their pets before the lockdown.

Ground-level ozone (O3) pollution has shifted from being a scientific topic to a governmental imperative in China. Shanghai, as a representative of mega cities, is also facing the challenge of O3 pollution constraining the continued improvement of urban air quality. In order to curb the spread of coronavirus disease 2019 (COVID-19), Shanghai has adopted extremely strict control measures during the period of April and May 2022 (CLP-22). The concentrations of precursor pollutants such as VOCs and NO2 have decreased by nearly 50%, however, O3 concentrations have increased by more than 20%. Considering the scale of urbanization, the particularity of lockdown period and the stringency of lockdown measures, O3 rise during CLP-22 in Shanghai was further investigated utilizing ground-level observed data, an observation-based model, and a chemical transport model. The increase in O3 was mainly attributable to ineffective VOCs and NOx reduction ratios and adverse meteorological conditions. The model results suggest that the higher VOCs reduction, the more effective in decreasing the daily maximum 8-hour moving average (MDA8) O3 concentration under the same total reduction percentage of VOCs and NOx emissions, whereas higher NOx reductions than VOCs may lead to O3 increases, especially in urban areas. This indicates that simple one-fits-all control measures such as short-term lockdown strategies may not achieve control targets for both primary and secondary pollutants. Only well-designed strategies with reasonable control of VOCs to NOx ratios and active VOC species can mitigate O3 pollution.

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.

Abstract

The phone survey was conducted to gather data on the socio-economic impact of COVID-19 crisis in Vanuatu. Community transmission of COVID-19 in Vanuatu started only in March 2022 followed by the nation-wide lockdown and other restrictions. Round 1 HFPS survey was a timely process to observe the effect of the crisis on the country. Round 1 interviewed 2,515 households both in urban and rural regions of the country from July 2022 to September 2022.

Survey topics included employment and income, food security, coping strategies, access to health services, and asset ownership - all on household level. Additionally, two individual-level datasets explore adult employment and child education. The former selects a randomly chosen adult in the household - could be the respondent of a household-level data, head of the household or another individual - and inquires about their employment status. For the latter, the respondent is being asked about education of a randomly chosen child in the household with more than one child.

While these findings are not without their caveats due to the lack of baseline data, constraints of the mobile phone survey methodology, and data quality constraints, they represent the best estimates to date and supplement other data on macroeconomic conditions, exports, firm-level information, etc. to develop an initial picture of the impacts of the crises on the population.

Geographic coverage

National urban and rural (6 provinces) coverage: Sanma, Shefa, Torba, Penama, Malampa, Tafea

Analysis unit

Household and Individual.

Universe

All respondents must be aged 18 and over and have a phone.

Kind of data

Sample survey data [ssd]

Sampling procedure

The Vanuatu HFPS Round 1 sample was generated in three ways. The first method is Random Digit Dialing (RDD) process covering all cell telephone numbers active at the time of the sample selection. Majority of the sample was generated through RDD in this round - approximately 84%.

The RDD methodology generates virtually all possible telephone numbers in the country under the national telephone numbering plan and then draws a random sample of numbers. This method guarantees full coverage of the population with a phone.

First, a large first-phase sample of cell phone numbers was selected and screened through an automated process to identify the active numbers. Then, a smaller second-phase sample was selected from the active residential numbers identified in the first-phase sample and was delivered to the data collection team to be called by the interviewers. When a cell phone was called, the call answerer was interviewed as long as he or she was 18 years of age or above and knowledgeable about the household activities.

The remaining 16% of Round 1 respondents was retrieved from Vanuatu's National Sustainable Development Plan (NSDP) Baseline Survey 2019/20.

Mode of data collection

Computer Assisted Telephone Interview [cati]

Research instrument

The questionnaire was developed in both English and Bislama. Sections of the Questionnaire are listed below: 1. Interview Information 2. Basic Information 3. Vaccine Information 4. Health 5. Education 6. Food Insecurity 7. Employment 8. Income 9. Coping Strategies 10. Assets 11. Digital 12. Recontact

The questionnaire is provided in this documentation as an external resource.

Cleaning operations

At the end of data collection, the raw dataset was cleaned by the survey firm and the World Bank team. Data cleaning mainly included formatting, relabeling, and excluding survey monitoring variables (e.g., interview start and end times). Data was edited using the software Stata.

Response rate

Total of 9,674 calls were attempted for Round 1. Response rate - where the phone was picked up - was 40%. Out of these, 66% completed the full survey.

The COVID-19 pandemic has had a substantial impact on mental health; because students are particularly vulnerable to loneliness, isolation, stress and unhealthy lifestyle choices, their mental health and wellbeing may potentially be more severely impacted by lockdown measures than the general population. This study assessed the mental health and wellbeing of UK undergraduate students during and after the lockdowns associated with the COVID-19 pandemic. Data were collected via online questionnaire at 3 time points – during the latter part of the first wave of the pandemic (spring/summer 2020; n=46) while stringent lockdown measures were still in place but gradually being relaxed; during the second wave of the pandemic (winter 2020-21; n=86) while local lockdowns were in place across the UK; and during the winter of 2021-22 (n=77), when infection rates were high but no lockdown measures were in place. Stress was found to most strongly predict wellbeing and mental health measures during the two pandemic waves. Other substantial predictors were diet quality and intolerance of uncertainty. Positive wellbeing was the least well accounted for of our outcome variables. Conversely, we found that depression and anxiety were higher during winter 2021-22 (no lockdowns) than winter 2020-21 (under lockdown). This may be due to the high rates of infection over that period and the effects of COVID-19 infection itself on mental health. This suggests that, as significant as the effects of lockdowns were on the wellbeing of the nation, not implementing lockdown measures could potentially have been even more detrimental for mental health.

Amid the COVID-19 outbreak, the ENCOVID-19 provides information on the well-being of Mexican households in four main domains: labor, income, mental health, and food insecurity. It offers timely information to understand the social consequences of the pandemic and the lockdown measures. It is a project consisting of a series of cross-sectional telephone surveys collected in key moments of the COVID-19 pandemic. In addition to the four main domains and a set of COVID19-related questions, the survey includes new key indicators every month to capture the impact of the pandemic on issues like education, social programs, and crime. This is the eleventh dataset of the project, corresponding to April 2022, collected 24 months after the lockdown began in Mexico. Data collection was performed from March 17 to May 2, 2022.

When COVID-19 emerged, lockdowns were implemented to restrict the rate of transmission. Early findings have shown the extent this action had on the wellbeing of the general population. However, it was expected the impact was more pronounced on individuals living with chronic illness, or those supporting them. This study aimed to longitudinally understand the effects of lockdown on carers of people living with Parkinson’s, and how their experiences evolved after lockdown ended. Nine participants (3 male, 6 female, aged 64-79) were recruited through Parkinson’s UK and a university Parkinson’s Research Database. Participants were interviewed via telephone on two occasions: the first occasion participants discussed their experiences of lockdowns (from March 2020 to June 2021) in relation to supporting their spouse as well as their own challenges. In the second interview (completed five months after the first round of interviews) participants reflected on their experiences of life post-lockdown and the effects lockdown had on their reintegration into society. Using interpretative phenomenological analysis, four themes emerged from participant interviews: (i) Lockdown-induced revolution and evolution of relationship dynamic with spouse; (ii) Fighting to be seen, heard, and understood in healthcare encounters; (iii) Making sense of, and adapting to, risk in a time of COVID-19; and (iv) Isolated and needing support during and after lockdown. Themes are illustrated with data excerpts from both data collection points. Findings show that a perceived sense of control and access to Parkinson’s support were central factors that shaped both participants’ experiences of managing during lockdown and their beliefs surrounding their relationship dynamic with their spouse post-lockdown .

10-second interval electricity data (real and reactive power) of 390 apartments located in >12 residential buildings in New York City (Jan 2019 - Dec 2021), published at level of 15 aggregated apartments per trace ("15/15 rule"). In line with stay-at-home and/or work-from-home patterns prompted by the pandemic and the emergence of a "new normal" thereafter, annual average load per apartment in MFRED increased from 343 Watt in 2019 to 360 Watt in 2020, and then fell again to 347 Watt in 2021 and 340 Watt in 2022. Files attached with this dataset cover 2022. Data for 2019 as well as comprehensive explanations of the dataset can be found in Meinrenken et al. (doi.org/10.1038/s41597-020-00721-w), which is attached. Data for 2020 and 2021 can be found in Meinrenken et al. (doi.org/10.7910/DVN/EJ2C4F).

Related article: Johanna Eklund, Julia P G Jones, Matti Räsänen, Jonas Geldmann, Ari-Pekka Jokinen, Adam Pellegrini, Domoina Rakotobe, O. Sarobidy Rakotonarivo, Tuuli Toivonen, and Andrew Balmford. Elevated fires during COVID-19 lockdown and the vulnerability of protected areas. Nature Sustainability (2022) https://doi.org/10.1038/s41893-022-00884-x.

In this dataset:

This dataset contains information about monthly fire incidence and precipitation for the protected areas of Madagascar from January 2012 to December 2020. The fire data is sourced from NASA’s Visible Infrared Imaging Radiometer Suite (VIIRS) 375 m active fire product and the precipitation data from the Global Precipitation Measurement (GPM) mission (for years 2016-2020) and its predecessor The Tropical Rainfall Measuring Mission (TRMM) (for years 2011-2015) at spatial resolution 10 km. The fire and precipitation data was overlayed with the protected area polygons of the June 2020 release of the World Database of Protected Areas. For sources and more details on how the data was compiled see the related article. The data can be used to inspect temporal dynamics of wildfires inside protected areas and for informing adaptive protected area management and planning.

Please cite this dataset as:

Johanna Eklund, Julia P G Jones, Matti Räsänen, Jonas Geldmann, Ari-Pekka Jokinen, Adam Pellegrini, Domoina Rakotobe, O. Sarobidy Rakotonarivo, Tuuli Toivonen, and Andrew Balmford. Elevated fires during COVID-19 lockdown and the vulnerability of protected areas. Nature Sustainability (2022) https://doi.org/10.1038/s41893-022-00884-x.

Column names

NAME: Name of protected area

Fires_sum: Number of observed fires (VIIRS)

Month: Month

Year: Year

Precipitation: Precipitation (mm)

Plag_1:Plag_12: Precipitation during previous month; 2 months ago; 3 months ago…12 months ago

YEAR_CREAT: Year of establishment of protected area

Biome: Biome

REP_AREA: Area of protected area (km2)

Fires_per_km2: Fires per km2

Prec_acc_12m: Accumulated precipitation during the last 12 months

fBiome: Biome as factor

fNAME: Name as factor

sPrecipitation: Precipitation (scaled; see Methods section of article)

sPlag_1: Precipitation in previous month (scaled; see Methods section of article)

sPrec_acc_12m: Accumulated precipitation during the last 12 months (scaled; see Methods section of article)

Pred_Zinb_1a: Predicted fires (see Methods section of article)

Diff_Zinb_1a: Difference: Observed fires - predicted fires

Year_pred: Year for prediction

License Creative Commons Attribution 4.0 International.

Due to changes in the collection and availability of data on COVID-19, this website will no longer be updated. The webpage will no longer be available as of 11 May 2023. On-going, reliable sources of data for COVID-19 are available via the COVID-19 dashboard and the UKHSA

GLA Covid-19 Mobility Report

Since March 2020, London has seen many different levels of restrictions - including three separate lockdowns and many other tiers/levels of restrictions, as well as easing of restrictions and even measures to actively encourage people to go to work, their high streets and local restaurants. This reports gathers data from a number of sources, including google, apple, citymapper, purple wifi and opentable to assess the extent to which these levels of restrictions have translated to a reductions in Londoners' movements.

The data behind the charts below come from different sources. None of these data represent a direct measure of how well people are adhering to the lockdown rules - nor do they provide an exhaustive data set. Rather, they are measures of different aspects of mobility, which together, offer an overall impression of how people Londoners are moving around the capital. The information is broken down by use of public transport, pedestrian activity, retail and leisure, and homeworking.

Public Transport

For the transport measures, we have included data from google, Apple, CityMapper and Transport for London. They measure different aspects of public transport usage - depending on the data source. Each of the lines in the chart below represents a percentage of a pre-pandemic baseline.

https://cdn.datapress.cloud/london/img/dataset/60e5834b-68aa-48d7-a8c5-7ee4781bde05/2025-06-09T20%3A54%3A15/6b096426c4c582dc9568ed4830b4226d.webp" alt="Embedded Image" />

activity Source Latest Baseline Min value in Lockdown 1 Min value in Lockdown 2 Min value in Lockdown 3 Citymapper Citymapper mobility index 2021-09-05 Compares trips planned and trips taken within its app to a baseline of the four weeks from 6 Jan 2020 7.9% 28% 19% Google Google Mobility Report 2022-10-15 Location data shared by users of Android smartphones, compared time and duration of visits to locations to the median values on the same day of the week in the five weeks from 3 Jan 2020 20.4% 40% 27% TfL Bus Transport for London 2022-10-30 Bus journey ‘taps' on the TfL network compared to same day of the week in four weeks starting 13 Jan 2020 - 34% 24% TfL Tube Transport for London 2022-10-30 Tube journey ‘taps' on the TfL network compared to same day of the week in four weeks starting 13 Jan 2020 - 30% 21% Pedestrian activity

With the data we currently have it's harder to estimate pedestrian activity and high street busyness. A few indicators can give us information on how people are making trips out of the house:

https://cdn.datapress.cloud/london/img/dataset/60e5834b-68aa-48d7-a8c5-7ee4781bde05/2025-06-09T20%3A54%3A15/bcf082c07e4d7ff5202012f0a97abc3a.webp" alt="Embedded Image" />

activity Source Latest Baseline Min value in Lockdown 1 Min value in Lockdown 2 Min value in Lockdown 3 Walking Apple Mobility Index 2021-11-09 estimates the frequency of trips made on foot compared to baselie of 13 Jan '20 22% 47% 36% Parks Google Mobility Report 2022-10-15 Frequency of trips to parks. Changes in the weather mean this varies a lot. Compared to baseline of 5 weeks from 3 Jan '20 30% 55% 41% Retail & Rec Google Mobility Report 2022-10-15 Estimates frequency of trips to shops/leisure locations. Compared to baseline of 5 weeks from 3 Jan '20 30% 55% 41% Retail and recreation

In this section, we focus on estimated footfall to shops, restaurants, cafes, shopping centres and so on.

https://cdn.datapress.cloud/london/img/dataset/60e5834b-68aa-48d7-a8c5-7ee4781bde05/2025-06-09T20%3A54%3A16/b62d60f723eaafe64a989e4afec4c62b.webp" alt="Embedded Image" />

activity Source Latest Baseline Min value in Lockdown 1 Min value in Lockdown 2 Min value in Lockdown 3 Grocery/pharmacy Google Mobility Report 2022-10-15 Estimates frequency of trips to grovery shops and pharmacies. Compared to baseline of 5 weeks from 3 Jan '20 32% 55.00% 45.000% Retail/rec <a href="https://ww

Italy has been majorly affected by the COVID-19 pandemic. The government declared a full lockdown and limited human and commercial activities to keep the pandemic under control. The limited human activity reduced the spread of the virus and the cultural noise it created. The effect of the lockdown is detected by the Italian strong motion network, which covers the entire country with their stations mostly located in the settlements. To assess the effect of the lockdown, background noise information up to 1 s from 2022 is used as a comparison. It is found that the background noise levels dropped around 1.46 dB during the lockdown, with a nationwide reduction in almost all of the stations. Noise levels have dropped both in the daytime and nighttime during the lockdown, with a more significant noise drop during the nighttime, which can be linked to the ban on dining in restaurants and bars and the curfew. A similar trend is found in weekday and weekend comparisons; in both time ranges, 2022 was noisier regarding the lockdown period. Stations located in public spaces such as schools and city halls observed noise reduction of up to 7.99 dB, and this noise level reduction is visible in major cities. We analyzed the 10 most populated Italian cities and their surroundings and found noise reduction of up to 5.5 and 2.1 dB in the median.

BackgroundAfter the declaration of the COVID-19 pandemic, governments established national lockdowns and social distancing as an effective plan to control this disease. As a result of the lockdown policies, diabetic patients` access to food products, medication, and routine follow-ups is disrupted, making it difficult for them to control their disease.MethodsInternational databases, including PubMed/Medline, Web of Science, and Scopus, were searched until April 2022. All observational studies included assessing the impact of lockdown and social distancing on eating habits (as primary outcome), and glycemic and anthropometric indices (as secondary outcomes) of diabetic patients during the COVID-19 pandemic. The Newcastle-Ottawa Quality Scale was used to assess the quality rating of the studies.ResultsOverall, 22 studies were included in this systematic review, the results of which varied in different communities. In most studies, consumption of grains, fruits, and vegetables was reported to increase. On the other hand, consumption of snacks and sweets was reported to increase in other surveys. During the COVID-19 lockdown, most diabetic patients preferred to cook meals at home, using less takeout, fast foods, and alcoholic drinks. Although the patients mostly improved their eating habits, the glycemic and anthropometric indices were contradictory in different studies. Studies showed that the eating habits of diabetic patients vary from country to country, even in some cases and studies done in the same country showed different results. For example, all the studies done in Japan showed an increase in the consumption of snacks and sweets, leading to weight gain in the patients. However, conflicting results in eating habits have been observed in studies conducted in India.ConclusionThe lockdown policies have led to a beneficial change in the eating habits of diabetic patients to consume more fruits and vegetables and reduce the consumption of animal protein products and alcoholic beverages. While some diabetic patients have increased consumption of snacks and sweets, leading to a disturbance in their glycemic and anthropometric indices control. Understanding the consequences of lockdown and social distancing of the diabetic patient during the COVID-19 pandemic can help public health authorities make better recommendations to improve glycemic control.

The phone survey was conducted to gather data on the socio-economic impact of COVID-19 crisis in Vanuatu. Community transmission of COVID-19 in Vanuatu started only in March 2022 followed by the nation-wide lockdown and other restrictions. Round 1 HFPS survey was a timely process to observe the effect of the crisis on the country. Round 1 interviewed 2,515 households both in urban and rural regions of the country from July 2022 to September 2022.

Survey topics included employment and income, food security, coping strategies, access to health services, and asset ownership - all on household level. Additionally, two individual-level datasets explore adult employment and child education. The former selects a randomly chosen adult in the household - could be the respondent of a household-level data, head of the household or another individual - and inquires about their employment status. For the latter, the respondent is being asked about education of a randomly chosen child in the household with more than one child.

While these findings are not without their caveats due to the lack of baseline data, constraints of the mobile phone survey methodology, and data quality constraints, they represent the best estimates to date and supplement other data on macroeconomic conditions, exports, firm-level information, etc. to develop an initial picture of the impacts of the crises on the population.

Version 01: Cleaned, labelled and anonymized version of the Master file

-HOUSEHOLD DATASET: Basic Information, Vaccine, Health, Education, Food Insecurity, Employment, Income, Coping Strategies, Assets
-ADULT EMPLOYMENT DATASET: Basic Information, Employment
-CHILD EDUCATION DATASET: Basic Information, Child Education

• Collection start: 2022
• Collection end: 2022

In 2022, the number of accidents reported throughout the public transit networks in the U.S. amounted to 6746, up by over 12 percent from the year 2021. The year 2020 experienced a significant drop in the number of accidents following the COVID-19 pandemic lockdown. In the past decade, the number of transit accidents peaked in 2016 at 7,308.

This datase contains the metadata associated with this publication:

A. Estévez-Danta, L. Bijlsma, R. Capela, R. Cela, A. Celma, F. Hernández, U. Lertxundi, J. Matias, R. Montes, G. Orive, A. Prieto, M.M. Santos, R. Rodil, J.B. Quintana

Use of illicit drugs, alcohol and tobacco in Spain and Portugal during the COVID-19 crisis in 2020 as measured by wastewater-based epidemiology

Science of the Total Environment, 2022, 836, 155697

https://doi.org/10.1016/j.scitotenv.2022.155697

The data is deposited in ZENODO:

https://zenodo.org/doi/10.5281/zenodo.10829752

If you reuse the data, please cite the publication and ZENODO deposit mentioned above

Explanation of the different sheets of the Excel file (All_Data_STOTEN_2022_155697) or different individual CSV files (named as below):

WWTP_details: explanation of wastewater treatment plats (WWTPs) sampled, flow rates, etc.

Concentrations: concentrations measured in the samples

PNDL: population normalized daily loads calculated per each sample

Consumption: estimated drug use (see the publication for correction factors)

EF: enantiomeric fraction, expressed as fraction of the R-enantiomer for the samples analyzed

Abreviations

AMP Amphetamine

MAMP Methamphetamine

MDMA 3,4-Methylenedioxymethamphetamine

BE Benzoylecgonine

COC Cocaine

THC-COOH 11-Nor-9-carboxy-Δ9-tetrahydrocannabinol

THC Δ9-Tetrahydrocannabinol

COT Cotinine

OH-COT Trans-3'-Hydroxycotinine

NIC Nicotine

EtS Ethyl sulfate