This file contains workforce absence statistics for education settings from 12 October 2020 to 17 December 2020 and again following wider reopening of schools, from 8 March 2021 to 16 September 2021. It excludes half term terms (19th October - 23rd October, and 2nd November 2020), the national lockdown during the spring term (4 January to 5 March 2021), Easter data (29 March - 19 April 2021) and summer holiday (17 July 2021 - 6 September 2021). Data for workforce during the restricted opening of schools can be found in table 1e.Data is in this file has been scaled to account for non-response so it is nationally representative.

The ongoing coronavirus pandemic has strongly impacted the shopping behavior of consumers in the United States and recent survey data indicates that consumers do not feel that this situation will be resolved in the upcoming holiday season. A May 2020 survey of U.S. consumers found that compared to last year, 49 percent of respondents were more interested in shopping online for the holidays. A third of respondents was also more interested in buying online and picking their order up in-store.

As of July 2nd, 2024 the COVID-19 Deaths by Population Characteristics Over Time dataset has been retired. This dataset is archived and will no longer update. We will be publishing a cumulative deaths by population characteristics dataset that will update moving forward.

A. SUMMARY This dataset shows San Francisco COVID-19 deaths by population characteristics and by date. This data may not be immediately available for recently reported deaths. Data updates as more information becomes available. Because of this, death totals for previous days may increase or decrease. More recent data is less reliable.

Population characteristics are subgroups, or demographic cross-sections, like age, race, or gender. The City tracks how deaths have been distributed among different subgroups. This information can reveal trends and disparities among groups.

B. HOW THE DATASET IS CREATED As of January 1, 2023, COVID-19 deaths are defined as persons who had COVID-19 listed as a cause of death or a significant condition contributing to their death on their death certificate. This definition is in alignment with the California Department of Public Health and the national https://preparedness.cste.org/wp-content/uploads/2022/12/CSTE-Revised-Classification-of-COVID-19-associated-Deaths.Final_.11.22.22.pdf">Council of State and Territorial Epidemiologists. Death certificates are maintained by the California Department of Public Health.

Data on the population characteristics of COVID-19 deaths are from: *Case reports *Medical records *Electronic lab reports *Death certificates

Data are continually updated to maximize completeness of information and reporting on San Francisco COVID-19 deaths.

To protect resident privacy, we summarize COVID-19 data by only one characteristic at a time. Data are not shown until cumulative citywide deaths reach five or more.

Data notes on each population characteristic type is listed below.

Race/ethnicity * We include all race/ethnicity categories that are collected for COVID-19 cases.

Gender * The City collects information on gender identity using these guidelines.

C. UPDATE PROCESS Updates automatically at 06:30 and 07:30 AM Pacific Time on Wednesday each week.

Dataset will not update on the business day following any federal holiday.

D. HOW TO USE THIS DATASET Population estimates are only available for age groups and race/ethnicity categories. San Francisco population estimates for race/ethnicity and age groups can be found in a view based on the San Francisco Population and Demographic Census dataset. These population estimates are from the 2016-2020 5-year American Community Survey (ACS).

This dataset includes many different types of characteristics. Filter the “Characteristic Type” column to explore a topic area. Then, the “Characteristic Group” column shows each group or category within that topic area and the number of deaths on each date.

New deaths are the count of deaths within that characteristic group on that specific date. Cumulative deaths are the running total of all San Francisco COVID-19 deaths in that characteristic group up to the date listed.

This data may not be immediately available for more recent deaths. Data updates as more information becomes available.

To explore data on the total number of deaths, use the COVID-19 Deaths Over Time dataset.

E. CHANGE LOG

• 9/11/2023 - on this date, we began using an updated definition of a COVID-19 death to align with the California Department o

An August 2020 survey revealed that ** percent of adults in the United States had taken a staycation during the coronavirus (COVID-19) pandemic. The generation with the highest share of respondents that had taken a staycation was Millennials, with ** percent. Comparatively, ** percent of Gen Z and ** percent of Baby Boomer respondents stated that this was the case. Meanwhile, one of the most common sources of staycation inspiration for U.S. travelers was listening to the opinions of friends and relatives.

How familiar is the U.S. public with the term ‘staycation’?

The term 'staycation’ is typically used to refer to a holiday spent in one's home country rather than abroad, or one spent at home and involving day trips to local attractions. During an August 2020 survey, over ** percent of respondents showed familiarity with the term ‘staycation’ in the United States Meanwhile, ** percent stated that they had never heard of this term before.

What are the cheapest U.S. cities for a staycation?

The coronavirus (COVID-19) pandemic created a number of obstacles for international travel. Staycations, as a result, became a practical alternative for many would-be international travelers. When considering metrics such as the price of a meal and beer, the cost of a one-way ticket on local transport, the average price of a * star hotel, the number of hotels, and the number of day trips in under ** dollars, two of the cheapest cities for a staycation in the U.S. as of October 2020 were Omaha (Nebraska) and Houston (Texas).

Abstract

The objective of the second round of the World Bank high frequency mobile phone survey was to measure the continued socioeconomic impacts of COVID-19 in Papua New Guinea, including on livelihoods, food security, and public safety and security. The length of the survey was limited to 15 minutes and the survey instrument consisted of the following modules: Basic Information, Employment and Income Loss, Food Access and Food Security, Health, Public Trust and Security, and Assets and Wellbeing. The questions on employment and income were asked to the respondent and to the household head if different from the respondent. The recall period for current employment was in the previous week. In addition, retrospective questions were asked for new respondents about the baseline (“the start of this year 2020”) as well about the situation at the time of round 1 in June (“June, around the time of the Queen’s birthday holiday”). The information from the new respondent could then be pooled with the returning respondents to have three consistent points in 2020. For retrospective questions on employment, the baseline is defined as “the start of this year 2020” and new households were asked both about the baseline as well as the situation in early July, corresponding with the implementation of round 1 of data collection. Three subsequent rounds are planned, with the next in May 2021, though the implementation calendar may be revised to respond to changing conditions on the ground.

Geographic coverage

National coverage: 22 provinces covered.

Analysis unit

Household and Individual.

Universe

Over 18 years of age from the Digicel subscriber logs.

Kind of data

Sample survey data [ssd]

Sampling procedure

The original objective of round 2 was to re-interview all households and respondents that were interviewed in round 1. There is high turnover of SIM cards in PNG, however, as numbers must be officially registered with a valid government ID within six months of activation or they are disconnected. Overall, of the original 3,115 households and 4,528 individuals interviewed during round 1, only 951 households and 962 individuals were re-interviewed in round 2. Though a small percentage of respondents refused (less than 1 percent), the main reason for failure to re-contact was that the number was no longer working. In addition, there were 67 households in which someone answered at the original mobile number, but they were not a part of the original household. Therefore 1,804 additional households were added for the second round, for a total sample size of 2,820 households and 3,368 individuals in round 2. To attempt to address some of the issues seen in round 1 in terms of the skew towards the higher deciles of the wealth distribution, a different targeting mechanism was used in round 2 based on subscriber characteristics derived from the Digicel database to try to address some of the skew towards richer households seen in the first round. To proxy poor households, the team targeted subscribers that did not send text messages on the assumption they were less likely to be literate. Similarly, subscribers that received only incoming calls or for whom the majority of credit was not purchased but transferred from other subscribers were thought to be more likely to be poor.

The UNICEF survey of households with children interviewed 2,449 of the 2,820 households interviewed in the second round of the World Bank survey, 86.8 percent of the total sample, and 96.6 percent of the total 2,534 that were targeted as having children under age 15. Using logit econometric model to compare the characteristics of eligible households which attritted between round 2 of the World Bank survey and the UNICEF survey, there are no statistically significant relationships accounting for the sex and education of the respondent, household wealth, and the geographic location (province, urban/ rural), with the exception of a statistically significant higher probability of attrition from those living in East Sepik Province.

For more information on sampling, please refer to the report provided in the External Resources.

Mode of data collection

Computer Assisted Telephone Interview [cati]

Research instrument

The questionnaire - that can be found in the External Resources of this documentation - was developped both in English and in Pidgin.

The questionnaire included the following modules: -Basic Information, -Employment and Income Loss, -Food Access and Food Security, -Coping Strategies, -Health, -Public Trust and Security, -and Assets and Wellbeing.

The questionnaire for the UNICEF survey included sections on: -Basic Information, Knowledge and Behavior, -Service Delivery, -Roster of Children Living in the Household (including schooling status), -Access to Health, -Education, -Child Discipline, -and Life Perspectives, It is to be noted that the latter three UNICEF sections were administered to a randomly selected child between the ages of 3-14.

Cleaning operations

At the end of data collection, the raw dataset was cleaned by the World Bank team. This included formatting, and correcting results based on monitoring issues, enumerator feedback and survey changes.

Data was edited using the software Stata.

Response rate

Please check at Figure 3 in the "Background" section of the report (provided as External Resource).

The accommodation sector in Denmark has been heavily affected by the coronavirus outbreak. As of June 2020, however, the number of overnight stays started increasing. While hotels in Denmark counted roughly two million overnight stays in August 2019, the number amounted to around 1.2 million in August 2020. However, the number of overnight stays in holiday houses exceeded those of the previous year.

On March 11, 2020, Denmark officially shut down and closed all borders only a few days later. For further information about the coronavirus (COVID-19) pandemic, please visit our dedicated Facts and Figures page.

In April 2024, the number of overnight stays at holiday houses in Denmark was *** million stays. Comparably, during the outbreak of the coronavirus (COVID-19) pandemic in 2020, the number of overnight stays significantly declined from the previous year to *** thousand in the same month. July is the most popular month for overnight trips to holiday houses in Denmark.

Retail platforms have undergone an unprecedented global traffic increase between January 2019 and June 2020, surpassing even holiday season traffic peaks. Overall, retail websites generated almost ** billion visits in June 2020, up from ***** billion global visits in January 2020. This is of course due to the global coronavirus pandemic which has forced millions of people to stay at home in order to stop the spread of the virus. Due to many shelter at home orders and a desire to avoid crowded stores in places where it is possible to shop, consumers have turned to the internet to procure everyday items such as groceries or toilet paper.

Catalonia, June, and July of 2021.

According to a June 2021 study, the share of Italians going on summer holidays is forecast to increase significantly in 2021 over the previous year. In 2020, due to the impact of the coronavirus (COVID-19) pandemic, roughly ** percent of the survey sample claimed to have been on summer vacations, decreasing from ** percent in 2019. As estimated, the share of Italian summer vacationers is expected to reach ** percent in 2021.

Catalonia, June, and July of 2021.

An annual survey conducted among British consumers looked at the share of people taking a vacation in the previous 12 months. According to the 2022 study, examining the trips made between September 2021 and August 2022, roughly ** percent of respondents made a holiday abroad. This figure shows an increase of ** percentage points compared to the 2021 survey, focusing on the vacations made during the first year of the coronavirus (COVID-19) pandemic. Meanwhile, the share of Britons taking domestic holidays in the past 12 months rose by five percentage points from 2021 to 2022.

An annual survey conducted among British consumers examined the average number of holidays abroad taken per person in the previous 12 months from 2011 to 2022. According to the 2022 study, looking at the trips made between September 2021 and August 2022, UK residents took an average of *** overseas holidays per capita. While this figure denotes an increase from the 2021 survey, focusing on vacations taken during the first year of the coronavirus (COVID-19) pandemic, it remained below pre-pandemic levels.

How did the COVID-19 pandemic hit outbound tourism from the UK? As the travel restrictions enforced during the health crisis disrupted international tourism, the total number of visits abroad from the UK fell dramatically during the pandemic, reaching a record low of around ** million in 2021. With the sharp decline in visits came a significant drop in the total UK outbound tourism expenditure, decreasing by nearly ** billion British pounds in 2021 compared to 2019.

What are the most popular destinations for UK travelers? Despite the significant decline in tourists caused by the COVID-19 pandemic, Spain remained the leading outbound travel destination from the UK during the health crisis, recording over **** million Britons' visits in 2021. Meanwhile, when focusing on the domestic market, the South West and South East of England were the most popular regions for summer staycations in the UK.