U.S. experts predict a smaller winter COVID wave, posing challenges for Pfizer amid declining revenues, emphasizing the need to diversify non-COVID products.

What impact has the COVID-19 pandemic had on Italians' attitudes, opinions, and behaviors? From this question, the ResPOnsE COVID-19 project (Response of Italian Public Opinion to the COVID-19 Emergency) was developed starting in March 2020, with the aim of building a research infrastructure for the daily monitoring of public opinion during the COVID-19 emergency. The collection of daily information through online interviews (CAWI) to a sample reflecting the distribution of the Italian population by gender and area of residence was divided into four surveys that took place between April 2020 and July 2023, for a total of more than 40,000 interviews. The infrastructure was designed by the spsTREND "Hans Schadee" laboratory in collaboration with the SWG institute, as part of the "Departments of Excellence 2018-2022" project promoted by the Ministry of University and Research and is supported by funding from the Cariplo Foundation. Overall Research Design The research design included six surveys (waves) following a repeated cross-sectional design, consistent with the dynamic nature of the pandemic phenomenon. The six waves of ResPOnsE COVID-19 are distributed as follows. First wave: from April 6 to July 6, 2020 (~15000 cases) Second wave: from December 21, 2020 to January 2, 2021 (~3000 cases) Third wave: from March 17 to June 16, 2021 (~9300 cases) Fourth wave: from November 10 to December 22, 2021 (~3000 cases) Fifth wave: from November 7 to December 22, 2022 (~9000 cases) Sixth wave: from June 6 to July 6, 2023 (~3000 cases) Rolling Cross-Section and Panel Design The first, third, fourth, fifth and sixth waves collect interviews through a Rolling Cross-Section (RCS) design, that is consecutive daily samples for a relatively long period (in this case 1 to 3 months). In addition, about 60% of subjects were interviewed twice between the first and the sixth wave, thus allowing longitudinal analysis of intra-individual variations that occurred between 2020 and 2023. An RCS survey can be viewed as a cross-sectional survey of a single sample that is, however, "sliced" into many equivalent small subgroups that are released on consecutive days. On the day of release, individuals belonging to a particular sub-group are invited to participate in the survey. The distinguishing feature of the RCS design, however, is that these individuals can also respond in the days following the delivery of the invitation. Hence comes the term "rolling" meaning that the overall sample "rolls" through the days of the survey, making time (days) a random variable. The daily samples are mutually independent and the estimates derived for each are comparable. In this way, the RCS design is optimal for studying trends in the case of time-varying phenomena. For details, see the articles by Vezzoni et al. (2020) and Biolcati et al. (2021). Questionnaire structure The questionnaire administered in the ResPOnsE COVID-19 survey consists of a main questionnaire, containing a core set of questions repeated in each of the six surveys, and one or more thematic modules that may change with each survey. The main questionnaire consists of eleven thematic sections covering the entire survey period. Most of the questions in the questionnaire were repeated in the six surveys, while some questions were eliminated/changed or new ones were introduced in the transition to a new survey. Covering the entire survey period, the basic module is particularly suitable for diachronic analysis, while the structure of the thematic modules, usually collected over a few weeks, suggests an analysis of them with a cross-sectional approach. Source questionnaires in Italian are available for download. The sample The target population consists of Italian residents aged 18 years and older. In the RCS waves, on average, between 100 and 150 interviews were conducted each day, corresponding to about 1,000 interviews per week for the first and the two last surveys and about 700 for the third and fourth surveys (the interviews in the second survey were actually concentrated in a single week), for a total of 42,860 interviews. Given time and resource constraints, probabilistic sampling could not be used. Instead, the samples are drawn from an online community of a commercial research institute (SWG SpA). To correct against expected bias, the sample is stratified by ISTAT macro-area of residence and composed of quotas defined by gender and age. Weights have also been created for carryover to the population. Detailed instructions on using the weights can be downloaded together with the data files. The survey also includes a panel component: about 60 percent of subjects (n = 12,801) were interviewed at least twice between the first, third, fourth, fifth and sixth waves. Over-sampling was also conducted for the Lombardy region, for which 1124 additional cases are available in the third wave Macro level data The cumulative data file also includes official macro-level...

The Monthly Outcome Survey (MOS) was designed to assess COVID-19 vaccine uptake as well as beliefs, intentions, and behaviors relevant to COVID-19 vaccination at a point in time. The survey fielded on a monthly basis from January 2021 to April 2023. When the MOS first launched, it focused on the primary series of COVID-19 vaccines; in later waves, it was expanded to assess parents’ intentions to get their children vaccinated or boosted and to track booster and updated vaccine uptake and readiness. The MOS fielded as part of an online omnibus survey, conducted with a cross-sectional sample of approximately 5,000 U.S. adults each month.

Italy went through five coronavirus waves during the pandemic. As of January 8, 2025, the number of active coronavirus cases in the country was equal to approximately 203,305. On January 23, 2022, there were 2,734,906 active infections in Italy, the highest figure since the start of the pandemic. Furthermore, the total number of cases (including active cases, recoveries, and deaths) in Italy reached 26.9 million, with the region mostly hit by the virus in the country being Lombardy. Despite this notably high number of infections, deaths and hospitalizations remain rather low, thanks to a very high vaccination rate. The virus originated in Wuhan, a Chinese city populated by millions and located in the province of Hubei. More statistics and facts about the virus in Italy are available here.For a global overview, visit Statista's webpage exclusively dedicated to coronavirus, its development, and its impact.

Abstract

The COVID 19 outbreak lead to significant challenges. From efforts to contain the spread of the virus to policy aimed at dampening the economic pain of the virus, policy makers have been presented with numerous decisions. In support of informing these efforts in Georgia, CRRC Georgia conducted six surveys between late April and early June, with the support of the Embassy of the Kingdom of the Netherlands in Tbilisi. The results were presented on a weekly basis to stakeholders in the Government of Georgia, international organizations, diplomats, and NGOs working on the crisis.

Geographic coverage

National coverage. Representative at the national- and at Tbilisi/Urban/Rural-levels of Georgia.

Analysis unit

Individual

Universe

Adult population (18 years old and over), excluding the populations living in territories affected by military conflict (South Ossetia and Abkhazia).

Kind of data

Sample survey data [ssd]

Sampling procedure

Simple random sample (random digit dialing) of cell phone owners.

Sample Size: Overall: 6145 respondents, including: Wave 1: 922 Wave 2: 1037 Wave 3: 1053 Wave 4: 1002 Wave 5: 1036 Wave 6: 1095

Sampling deviation

The surveys had between 992 and 1095 respondents in total. The theoretical margin of error was between 3.0% and 3.1% for each wave.

Mode of data collection

Computer Assisted Telephone Interview [cati]

Research instrument

The surveys covered a wide range of subjects. A set of core questions about institutional performance and approval of newly introduced policies were asked on each wave of the survey to track changes in public opinion. Each survey also included questions on the economic situation and a number of practices. Aside from these blocks, a set of new questions were introduced on a weekly basis to explore specific sets of issues in depth. Topics included religion, education policy, disinformation, gender, and democracy.

Response rate

Response rate: Wave 1: 42%, Wave 2: 41%, Wave 3: 39%, Wave 4: 39%, Wave 5: 35%, Wave 6: 37%.

After entering Italy, the coronavirus (COVID-19) spread fast. The strict lockdown implemented by the government during the Spring 2020 helped to slow down the outbreak. However, the country had to face four new harsh waves of contagion. As of January 1, 2025, the total number of cases reported by the authorities reached over 26.9 million. The north of the country was mostly hit, and the region with the highest number of cases was Lombardy, which registered almost 4.4 million of them. The north-eastern region of Veneto and the southern region of Campania followed in the list. When adjusting these figures for the population size of each region, however, the picture changed, with the region of Veneto being the area where the virus had the highest relative incidence. Coronavirus in Italy Italy has been among the countries most impacted by the coronavirus outbreak. Moreover, the number of deaths due to coronavirus recorded in Italy is significantly high, making it one of the countries with the highest fatality rates worldwide, especially in the first stages of the pandemic. In particular, a very high mortality rate was recorded among patients aged 80 years or older. Impact on the economy The lockdown imposed during the Spring 2020, and other measures taken in the following months to contain the pandemic, forced many businesses to shut their doors and caused industrial production to slow down significantly. As a result, consumption fell, with the sectors most severely hit being hospitality and tourism, air transport, and automotive. Several predictions about the evolution of the global economy were published at the beginning of the pandemic, based on different scenarios about the development of the pandemic. According to the official results, it appeared that the coronavirus outbreak had caused Italy’s GDP to shrink by approximately nine percent in 2020.

India reported over 44 million confirmed cases of the coronavirus (COVID-19) as of October 20, 2023. The number of people infected with the virus was declining across the south Asian country.

What is the coronavirus?

COVID-19 is part of a large family of coronaviruses (CoV) that are transmitted from animals to people. The name COVID-19 is derived from the words corona, virus, and disease, while the number 19 represents the year that it emerged. Symptoms of COVID-19 resemble that of the common cold, with fever, coughing, and shortness of breath. However, serious infections can lead to pneumonia, multi-organ failure, severe acute respiratory syndrome, and even death, if appropriate medical help is not provided.

COVID-19 in India

India reported its first case of this coronavirus in late January 2020 in the southern state of Kerala. That led to a nation-wide lockdown between March and June that year to curb numbers from rising. After marginal success, the economy opened up leading to some recovery for the rest of 2020. In March 2021, however, the second wave hit the country causing record-breaking numbers of infections and deaths, crushing the healthcare system. The central government has been criticized for not taking action this time around, with "#ResignModi" trending on social media platforms in late April. The government's response was to block this line of content on the basis of fighting misinformation and reducing panic across the country.

The New York Times is releasing a series of data files with cumulative counts of coronavirus cases in the United States, at the state and county level, over time. We are compiling this time series data from state and local governments and health departments in an attempt to provide a complete record of the ongoing outbreak.

Since late January, The Times has tracked cases of coronavirus in real time as they were identified after testing. Because of the widespread shortage of testing, however, the data is necessarily limited in the picture it presents of the outbreak.

We have used this data to power our maps and reporting tracking the outbreak, and it is now being made available to the public in response to requests from researchers, scientists and government officials who would like access to the data to better understand the outbreak.

The data begins with the first reported coronavirus case in Washington State on Jan. 21, 2020. We will publish regular updates to the data in this repository.

The goal of the Monthly Outcome Survey (MOS) Small Area Estimations (SAE) is to generate estimates of the proportions of adults, by county and month, who were in the population of interest for the U.S. Department of Health and Human Services’ (HHS) We Can Do This COVID-19 Public Education Campaign. These data are designed to be used by practitioners and researchers to understand how county-level COVID-19 updated (bivalent) vaccination hesitancy changed over time in the United States.

As of May 2020, the majority of Hungarians was worried that in the upcoming Fall or Winter a second wave of coronavirus (COVID-19) might follow. Only 11 percent of respondents reported not being worried at all.

For further information about the coronavirus (COVID-19) pandemic, please visit our dedicated Facts and Figures page.

According to the survey conducted in June 2020 among adults in Hong Kong, around 58 percent of respondents said they were worried about a second wave of local COVID-19 outbreak. Approximately 5.8 percent of respondents were not concerned about another pandemic outbreak in the city.

As of May 10, 2023, the cumulative number of confirmed COVID-19 cases totaled approximately 2.2 million in Hungary and 3.5 thousand people were infected with the virus at the time. By this date, the number of deceased had reached nearly 49 thousand.

The spread of COVID-19 in Hungary Local authorities announced the first two confirmed cases of COVID-19 on March 4, 2020, and the first coronavirus-related death on March 15, 2020. The virus had been present in all counties of Hungary long before the second wave reached the country at the end of the year. February 2021 was marked by the beginning of the third wave of COVID-19 in Hungary and the number of deaths from the virus reached an all-time high in April.

The government’s response In March 2020, a state of emergency was declared and the first security measures were introduced in Hungary. The same month, the coronavirus law was passed, which enabled Prime Minister Viktor Orbán to rule by decree indefinitely. By June 2020, Hungary had ended its rule by decree and the government had declared a "state of medical crisis". Due to the high number of deaths and severe cases, the government imposed strict restrictive measures again during the third wave in March 2021. Over the following months, however, these restrictions eased gradually owing to the increasing number of people receiving the first dose of the vaccine against the virus.

Abstract

To understand the socio-economic impact of COVID-19 and associated government measures over the long term, the third round of the COVID-19 National Panel Phone Survey 2020 was collected by the National Institute of Statistics of Djibouti (INSD) between December 20, 2020 and February 2, 2021. Various channels of impact are explored such as job loss, availability and price changes of basic food items, ability to access healthcare, and food insecurity. New sections on the attitudes toward a potential vaccine and shock coping strategies have also been added (compared to the second round).

Note that a sample of 564 refugee households living in Djibouti has been collected during the same time frame and using the same questionnaire. This data set will be available for download separately on the microdata library.

Geographic coverage

Urban areas only. The survey is representative of the bottom 80 percent of the consumption distribution of the national households (thus the top 20 percent are excluded). It is representative by poverty status and by three domains of Balbala, rest of Djibouti city and urban areas outside Djibouti city.

Analysis unit

• Household
• Individual

Universe

The survey covers national households that reported telephone numbers, are included in the social registry data collected by the Ministry of Social Affairs and Solidarity (MASS) and have been interviewed after 2017.

Kind of data

Sample survey data [ssd]

Sampling procedure

As a recently conducted representative household survey with telephone numbers was not available, data from the national social registry collected by the Ministry of Social Affairs (MASS) was used as the sampling frame of the national sample. The social registry is an official database of households in Djibouti that may benefit from public transfers and be particular targets of poverty alleviation efforts. The sample consists of households drawn randomly from the social registry data restricted to urban households having at least one phone number and interviewed after July 1, 2017. The sample design is a one-stage probability sample selected from the sampling frame and stratified along two dimensions: the survey domain (three categories) and the poverty status (binary). This yields six independent strata. Within each stratum, households are selected with the same ex-ante probability, but this differs across strata. With a non-response rate averaging 26 percent for the national households, the third wave consisted of 1,383 interviewed national households with complete information that were representative of the urban national population, out of which 990 households were also interviewed in the two first waves, 190 were added as replacement households in the second wave and re-interviewed in the third one, and 203 were added as replacement households in the third wave.

Mode of data collection

Computer Assisted Telephone Interview [cati]

Research instrument

The questionnaire of the third round is adapted from the questionnaire of the second round and in accordance with the template questionnaire prepared by the Poverty and Equity GP to measure the impact of COVID-19 on household welfare. It was designed in French and dispensed in local language (Afar, Arabic, Somali, French or other). The questionnaire includes the following sections: - Household roster - Employment - Household's income sources - Needs - Access to services - Safety nets - Food insecurity - Shock coping strategies - Vaccine attitudes

Cleaning operations

The CSPro CATI data entry application helped to enforce skip and range patterns during data collection. Standard consistency checks (like age differences between parents and children and unicity of household heads) were carried out at the time of the data collection. Because the entry application was strictly system-controlled, complete cases including missing items were avoided. The various checks resulted in a limited need for secondary data editing, which eventually entailed two main steps from the WB team. First, duplicated names of household members, who were otherwise distinct, were corrected by adding a suffix “bis” to the names. Second, after analysis of text responses mentioned in the residual “other” categories, a few items codes were adjusted (not exceeding 10 in any category).

Response rate

The response rate among the national sample was about 74.3 percent with 1,383 interviewed national households. Slight differences were observed across location, with districts 1, 2 and 3 of Djibouti city more likely to respond than other locations (response rate at 76.4 percent versus 75.9 and 70.5 percent, respectively in Balbala and the other urban areas).

This dataset tracks the updates made on the dataset "HHS COVID-19 Monthly Outcome Survey - Wave 08" as a repository for previous versions of the data and metadata.

Over the course of 2020, more than 2.1 million coronavirus cases were reported to the authorities in Italy. This statistic breaks down this figure by region and wave of infections. Italy, in fact, underwent three distinct phases in the fight against COVID-19. The first one started in late February, as the first cases in the country were detected. In this first stage, most of the contagion happened in the Northern regions. The Italian government reacted by implementing a strict lockdown that lasted until May, when the contagion curve started to flatten. Between June and September, the number of new cases was risible. The third phase started in October, when a second wave of infection, much bigger in magnitude than the first, struck the country. This time, as it is possible to see from the graph, also Central and Southern regions were heavily affected.

According to a survey conducted amongst doctors in 2021, around 28.4 percent of respondents stated that an imbalance of vaccine supply and demand was a reason for the fourth wave of coronavirus (COVID-19) in South Korea. South Korea began buying vaccines later than many other countries, and subsequently struggled to keep up with the demand, leading to initial slow rollout and low vaccination rates. Relaxed guidelines despite the spread of mutated strains, such as the delta variant, and societal expectations for the pandemic to end were also commonly given as reasons. The fourth wave of COVID-19 in South Korea began in July 2021.

The COVID-19 pandemic has dramatically altered family life in the United States. Over the long duration of the pandemic, parents had to adapt to shifting work conditions, virtual schooling, the closure of daycare facilities, and the stress of not only managing households without domestic and care supports but also worrying that family members may contract the novel coronavirus. Reports early in the pandemic suggest that these burdens have fallen disproportionately on mothers, creating concerns about the long-term implications of the pandemic for gender inequality and mothers’ well-being. Nevertheless, less is known about how parents’ engagement in domestic labor and paid work has changed throughout the pandemic and beyond, what factors may be driving these changes, and what the long-term consequences of the pandemic may be for the gendered division of labor and gender inequality more generally. The Study on U.S. Parents’ Divisions of Labor During COVID-19 (SPDLC) collects longitudinal survey data from partnered U.S. parents that can be used to assess changes in parents’ divisions of domestic labor, divisions of paid labor, and well-being throughout and after the COVID-19 pandemic. The goal of SPDLC is to understand both the short- and long-term impacts of the pandemic for the gendered division of labor, work-family issues, and broader patterns of gender inequality. Survey data for this study is collected using Prolifc (www.prolific.co), an opt-in online platform designed to facilitate scientific research. The sample is comprised U.S. adults who were residing with a romantic partner and at least one biological child (at the time of entry into the study). In each survey, parents answer questions about both themselves and their partners. Wave 1 of the SPDLC was conducted in April 2020, and parents who participated in Wave 1 were asked about their division of labor both prior to (i.e., early March 2020) and one month after the pandemic began. Wave 2 of the SPDLC was collected in November 2020. Parents who participated in Wave 1 were invited to participate again in Wave 2, and a new cohort of parents was also recruited to participate in the Wave 2 survey. Wave 3 of SPDLC was collected in October 2021. Parents who participated in either of the first two waves were invited to participate again in Wave 3, and another new cohort of parents was also recruited to participate in the Wave 3 survey. Wave 4 of the SPDLC was collected in October 2022. Parents who participated in either of the first three waves were invited to participate again in Wave 4, and another new cohort of parents was also recruited to participate in the Wave 4 survey. This research design (follow-up survey of panelists and new cross-section of parents at each wave) will continue through 2024, culminating in six waves of data spanning the period from March 2020 through October 2024. An estimated total of approximately 6,500 parents will be surveyed at least once throughout the duration of the study. SPDLC data will be released to the public two years after data is collected; Waves 1-3 are currently publicly available. Wave 4 will be publicly available in October 2024, with subsequent waves becoming available yearly. Data will be available to download in both SPSS (.sav) and Stata (.dta) formats, and the following data files will be available: (1) a data file for each individual wave, which contains responses from all participants in that wave of data collection, (2) a longitudinal panel data file, which contains longitudinal follow-up data from all available waves, and (3) a repeated cross-section data file, which contains the repeated cross-section data (from new respondents at each wave) from all available waves. Codebooks for each survey wave and a detailed user guide describing the data are also available.

The goal of the Monthly Outcome Survey (MOS) Small Area Estimations (SAE) is to generate estimates of the proportions of adults, by county and month, who were in the population of interest for the U.S. Department of Health and Human Services’ (HHS) We Can Do This COVID-19 Public Education Campaign. These data are designed to be used by practitioners and researchers to understand how county-level COVID-19 vaccination hesitancy changed over time in the United States.

This dataset tracks the updates made on the dataset "HHS COVID-19 Monthly Outcome Survey - Wave 24" as a repository for previous versions of the data and metadata.

This dataset tracks the updates made on the dataset "HHS COVID-19 Monthly Outcome Survey - Wave 27" as a repository for previous versions of the data and metadata.