On September 30, 2020, there were 17 new reported confirmed cases of COVID-19 in Australia. Australia's daily new confirmed coronavirus cases peaked on July 30 with 746 new cases on that day. This was considered to be the second wave of coronavirus infections in Australia, with the first wave peaking at the end of March at 460 cases before dropping to less than 20 cases per day throughout May and most of June.

 A second wave

Australia’s second wave of coronavirus found its epicenter in Melbourne, after over a month of recording low numbers of national daily cases. Despite being primarily focused within a single state, clusters of coronavirus cases in Victoria soon pushed the daily number of recorded cases over that of the first wave, with well over double the number of deaths. As a result, the Victorian Government once again increased lockdown measures to limit movement and social interaction. At the same time the other states and territories closed or restricted movement across borders, with some of the strictest border closures taking place in Western Australian.

 Is Australia entering into a recession?

After narrowly avoiding a recession during the global financial crisis, by September 2020 Australia had recorded two consecutive quarters of economic decline, hailing the country’s first recession since 1991. This did not necessarily come as a surprise for many Australians who had already witnessed a rising unemployment rate throughout the second quarter of 2020 alongside ongoing restrictions on retail and hospitality trading. However, thanks to welfare initiatives like JobKeeper and a government stimulus payment supplementing many household incomes, the economic situation could have been much worse at this point.

Objective: To assess and share learnings on the motivators and behavioural adherence across sex and age to evolving strategies in public policy to prevent the spread of SARS-CoV-2 at the end of a first COVID-19 wave and the beginning of a second COVID-19 wave in Australia.Design and Setting: A national longitudinal survey using a framework based on evidence-based behaviour change models. The survey was administered to a national sample representative across sex, age and location was undertaken at two time points: May 1st to 5th, 2020, and July 1st to 7th, 2020.Results: Overall 2,056 surveys were completed across the first and second rounds, with 63% (1,296/2,056) completing both. Age range was 18–99 years (median 53, IQR: 34–64). Suboptimal physical distancing and self-quarantining if unwell/diagnosed was reported in one in four respondents and not getting a test at onset of symptoms reported in one in three. Those non-adherent to all three behaviours (19%, 60/323), were mainly male, younger, lived in major cities and reported fewer concerns or motivators to change behaviour. Overall, government lockdown measures were considered very important by 81% (835/1,032) and appropriate by 75% (772/1,029).Conclusions: Prior to the suppression of a second COVID-19 wave, a significant minority of Australians reported suboptimal behavioural adherence to vital policy strategies to limit SARS-CoV-2 spread, mostly young adults and men. Successful wave 2 suppression required consistent communication from political and health leaders and supportive public health and economic strategies. Additional lockdown and punitive strategies were needed in Victoria and were generally well-supported and adhered to. To limit subsequent lockdown, this work reinforces the need for a mix of communication around saving lives of the vulnerable, and other strategies targeting high risk groups, facilitation of easy testing and minimisation of financial impacts.

This poll data is the 34th in an ongoing series of polls being undertaken by the Social Research Centre for the ANU. Their purpose is to assess Australians' opinions on important and topical issues, with an emphasis on international comparisons. This research is used to inform public debate and policy about issues affecting Australia. This wave of the project was collected in May 2020. Its aim is to provide a timely update on Australia's experiences during the COVID-19 pandemic. It builds upon previous waves collected in January and April which have been tracking public opinion over the 2019/20 Summer Bushfires and COVID-19 crisis. The poll includes questions regarding substance use, mental health and social impacts, financial impacts, and support service use. Finally, we ask Australians about gambling and data privacy issues. USER NOTICE: Several demographic variables have been restricted to protect the privacy of respondents. For details of data in the restricted file please see '1.ADA.OTHER.01466b.zip' in the files tab. If you are interested in the restricted variables please contact ADA at ada@ada.edu.au about restricted access requirements.

On Octover 11, 2020 there were 31 people with COVID-19 that were being treated in hospitals across Australia. Over the period since April 2020 hospitalizations due to COVID-19 rose dramatically from late July after a period of relatively few hospitalizations in June. This corresponds with the second wave of coronavirus cases in the country, which was mostly concentrated in the state of Victoria.

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

Objectives: Western Australia’s unique public health response delayed the first wave of community COVID-19 transmission for 2 years. We aimed to determine the status of post-traumatic stress (PTSS), depressive, and anxiety symptoms among healthcare staff in major tertiary hospitals, together with associated risk and protective factors prior to the first substantial outbreak of COVID-19.Methods: A cross-sectional study was conducted with 431 healthcare staff immediately prior to the Western Australian border re-opening in 2022. Staff were recruited via notices in email newsletters, at four tertiary hospitals and a public mental health clinic in metropolitan Perth. Validated and original questionnaires were administered via Qualtrics.Results: Moderate levels of PTSS (22.3%), depression (21.9%), and anxiety (25.9%) were reported. Pathway analyses indicated that sleep difficulties, workplace stressors, and infectious disease training were associated with higher PTSS, depression and anxiety symptoms, and younger age was associated with higher levels of depression and anxiety. Nursing roles were associated with higher PTSS. Social support and workplace support were associated with lower levels of depression and anxiety but were not associated with PTSS.Conclusion: The findings illustrate high levels of resilience, but indicate a need for structural supports within the health system to foster staff mental health prior to the onset of emergencies.

Background: The numbers of days that people consume alcohol and other drugs over a fixed time interval, such as 28 days, are often collected in surveys for research in the addictions field. The presence of an upper bound on these variables can result in response distributions with "ceiling effects". Also, if some peoples’ substance use behaviors are characterized by various weekly patterns of use, summaries of substance days-of-use over longer periods can exhibit multiple modes. Multiple modes can also result from "heaping" of responses when respondents are unsure about the precise value. These characteristics of substance days-of-use data mean that models assuming common parametric response distributions will not always provide a good fit. Repository contents: Simulate longitudinal cannabis days-of-use over 28-day intervals intended to reproduce characteristics of data reported by respondents to an Australian survey of illicit drug users run over 4 waves during the COVID-19 pandemic in Australia in 2020–21. The dataset includes generated subject_id and survey_wave and iso explanatory variables, where iso is a dummy variable indicating subjects that were in quarantine or isolation at the time of the 28-day interval. R-code to fit proportional-odds and continuation-ratio ordinal models as well as binomial, beta-binomial, negative binomial and hurdle negative binomial models to these data are available at a linked companion website. We fitted a Bayesian multinomial model to reported cannabis days-of-use over four 28-day intervals (four survey waves) during the COVID-19 pandemic in Australia. Cannabis days-of-use was modeled as a nominal categorical variable with 29 levels, one for each possible response (0 days, 1 day, ..., 28 days). The model, fitted to responses by 443 illicit drug users across four survey waves, included only survey wave and isolation status (in isolation or quarantine yes/no) as explanatory variables with subject_id as a random intercept. A simulated sample of 600 participants was generated by twice subsampling 300 subject_ids without replacement from the full set of 443. Most participants will have been selected in both subsamples. A single cannabis days-of-use was simulated for 2 subsamples x 300 subject_ids x 4 survey waves = 2400 28-day intervals. The cannabis days of use simulated response was generated by a single draw from the posterior predictive distribution for each subsample. The survey wave and isolation explanatory variables and subject_id are included in the supplied dataset. Survey participants are not identifiable. The data are provided in an R dataset, synthetic_cannabis_use.RData. In order to run R code accompanying the dataset, the Rstan software package https://mc-stan.org/users/interfaces/rstan also needs to be installed.

These files contain details about the study and Mplus syntax files used to create the tables found in "COVID-19 and Teachers’ Somatic Burden, Stress, and Emotional Exhaustion: Examining the Role of Principal Leadership and Workplace Buoyancy."

Abstract:
The role of two leadership factors (autonomy-supportive and autonomy-thwarting leadership) and one personal resource (workplace buoyancy) were examined as predictors of three teacher outcomes: somatic burden, stress related to change, and emotional exhaustion. Data were collected from 325 Australian teachers in May, 2020 during the first wave of COVID-19. During this time, many Australian children were being taught remotely from home, while other students were attending schools in-person. Findings showed that autonomy-supportive leadership was associated with greater buoyancy and, in turn, lower somatic burden, stress related to change, and emotional exhaustion (while controlling for covariates, including COVID-19 work situation). Autonomy-thwarting leadership was positively associated with emotional exhaustion. In addition, autonomy-supportive leadership was indirectly associated with the outcomes. The findings provide understanding of factors that may be harnessed to support teachers during subsequent waves of COVID-19 and other future disruptions to schooling that may occur.

Household Travel Survey (HTS) is the most comprehensive source of personal travel data for the Sydney Greater Metropolitan Area (GMA). This data explores average weekday travel patterns for residents in Sydney GMA.\r \r The Household Travel Survey (HTS) collects information on personal travel behaviour. The study area for the survey is the Sydney Greater Metropolitan Area (GMA) which includes Sydney Greater Capital City Statistical Area (GCCSA), parts of Illawarra and Hunter regions. All residents of occupied private dwellings within the Sydney GMA are considered within scope of the survey and are randomly selected to participate.\r The HTS has been running continuously since 1997/981 and collects data for all days through the year – including during school and public holidays.\r \r Typically, approximately 2,000-3,000 households participate in the survey annually. Data is collected on all trips made over a 24-hour period by all members of the participating households.\r \r Annual estimates from the HTS are usually produced on a rolling basis using multiple years of pooled data for each reporting year2. All estimates are weighted to the Australian Bureau of Statistics’ Estimated Resident Population, corresponding to the year of collection3. Unless otherwise stated, all reported estimates are for an average weekday.\r \r \r \r Due to disruptions in data collection resulting from the lockdowns during the COVID-19 pandemic, post-COVID releases of HTS data are based on a lower sample size than previous HTS releases. To ensure integrity of the results and mitigate risk of sampling errors some post-COVID results have been reported differently to previous years. Please see below for more information on changes to HTS post-COVID (2020/21 onwards).\r \r 1. Data collection for the HTS was suspended during lock-down periods announced by the NSW Government due to COVID-19.\r \r 2. Exceptions apply to the estimates for 2020/21 which are based on a single year of sample as it was decided not to pool the sample with data collected pre-COVID-19. \r \r 3. HTS population estimates are also slightly lower than those reported in the ABS census as the survey excludes overseas visitors and those in non-private dwellings.\r \r Changes to HTS post-COVID (2020/21 onwards)\r \r HTS was suspended from late March 2020 to early October 2020 due to the impact and restrictions of COVID-19, and again from July 2021 to October 2021 following the Delta wave of COVID-19. Consequently, both the 2020/21 and 2021/22 releases are based on a reduced data collection period and smaller samples.\r \r Due to the impact of changed travel behaviours resulting from COVID-19 breaking previous trends, HTS releases since 2020/21 have been separated from pre-COVID-19 samples when pooled. As a result, HTS 2020/21 was based on a single wave of data collection which limited the breadth of geography available for release. Subsequent releases are based on pooled post-COVID samples to expand the geographies included with reliable estimates.\r \r Disruption to the data collection during, and post-COVID has led to some adjustments being made to the HTS estimates released post-COVID:\r \r SA3 level data has not been released for 2020/21 and 2021/22 due to low sample collection.\r LGA level data for 2021/22 has been released for selected LGAs when robust Relative Standard Error (RSE) for total trips are achieved\r Mode categories for all geographies are aggregated differently to the pre-COVID categories\r Purpose categories for some geographies are aggregated differently across 2020/21 and 2021/22.\r A new data release – for six cities as defined by the Greater Sydney Commission - is included since 2021/22.\r Please refer to the Data Document for 2022/23 (PDF, 262.54 KB) for further details.\r \r \r RELEASE NOTE\r \r The latest release of HTS data is 15 May 2025. This release includes Region, LGA, SA3 and Six Cities data for 2023/24. Please see 2023/24 Data Document for details.\r \r A revised dataset for LGAs and Six Cities for HTS 2022/23 data has also been included in this release on 15 May 2025. If you have downloaded HTS 2022/23 data by LGA and/or Six Cities from this link prior to 15/05/2025, we advise you replace it with the revised tables. If you have been supplied bespoke data tables for 2022/23 LGAs and/or Six Cities, please request updated tables.\r \r Revisions to HTS data may be made on previously published data as new sample data is appended to improve reliability of results. Please check this page for release dates to ensure you are using the most current version or create a subscription (https://opendata.transport.nsw.gov.au/subscriptions) to be notified of revisions and future releases.\r

The Australia and New Zealand Protective Face Masks Market size was valued at USD 269.0 USD Million in 2023 and is projected to reach USD 351.61 USD Million by 2032, exhibiting a CAGR of 3.9 % during the forecast period. The face mask market in Australia and New Zealand has grown during the Covid-19 period. It is a gadget worn as a mask over the nose and mouth to minimize airborne particles, pathogens, and respiratory droplet transmission. It has been crafted for the purpose of protection against infections, pollution, allergens, and other dangerous components present in the atmosphere. They are commonly utilized in clinics, industries, public places, and simply individual homes to reduce the contamination risks and to keep people safe. They are of many kinds including surgical masks, N95 respirators, cloth masks, and face shields which protect you against varying levels of danger. Besides, the protective mask market is a diverse area where the significance of innovation, regulations, and awareness of the public are united. By the day, we run to the next obstacles caused by this pandemic. However, we cannot dismiss the importance of masks and the way they can help many. Recent developments include: May 2022 – 3M, a leading international conglomerate, announced its decision to undertake a major expansion project at its Valley, Nebraska, plant to augment the company's respiratory and hearing protection product lines. This ambitious endeavor involves an investment of approximately USD 58 million for the construction of a state-of-the-art 80,000-square-foot facility., April 2020 – The Detmold Group announced the establishment of a face mask manufacturing facility in Brompton. With the ongoing coronavirus health crisis and the increased demand for personal protective equipment, the company took swift action to set up local production of face masks in record time., March 2020 – Honeywell announced expanding its manufacturing capabilities in Phoenix to produce N95 face masks to assist the government's response to the novel coronavirus. The company's Phoenix expansion and its previously announced new production in Rhode Island will enable Honeywell to manufacture over 20 million N95 disposable masks monthly to combat COVID-19.. Key drivers for this market are: Continuous Uncertain COVID-19 Waves and Emerging Variants to Fuel the Demand for Product. Potential restraints include: Environmental Concerns Related to Disposable Face Masks May Hinder their Usage in the Long Term. Notable trends are: Rising Adoption of Reusable Face Masks to Fuel Market Growth.

Building a New Life in Australia: The Longitudinal Study of Humanitarian Migrants (BNLA) aims to identify factors which help or hinder positive settlement outcomes. BNLA follows 1,509 humanitarian migrating units who arrived in Australia or had their permanent visas granted in the six months between May and December 2013. Participants include offshore visa holders who arrived in Australia holding a permanent humanitarian visa and onshore visa holders who received their permanent protection visa between May and December 2013. Wave 1 took place from October 2013 to March 2014 interviewing 2,399 principal and secondary applicants. The first five waves of data collection were conducted annually. Waves 1, 3 and 5 interviews were conducted face-to-face and waves 2 and 4 interviews were conducted by telephone. Wave 6 was conducted 5 years after wave 5, between January and July 2023. Wave 6 data was collected online and face-to-face. The survey and participant materials were translated into 14 languages in wave 1, 9 languages in waves 2 - 5 and 5 languages in Wave 6. Interviews were conducted by bilingual interviewers; some interviews also used interpreters (interviews were conducted in nineteen languages in total in waves 1 and 2, thirteen languages in Wave 3, eleven languages in Wave 4, and ten languages in Wave 5 and seven languages in wave 6). For waves 2 and 4, shorter telephone interviews omit some of the questions asked in the longer face-to-face interviews. Topics covered by the study include: demographics, immigration experience, housing and neighbourhood, English language proficiency, education and training, employment and income, health, self-sufficiency, community support, personal resources and life satisfaction, and life in Australia. Additional modules include the child module in Wave 3, childcare and gender roles from Wave 5 and the COVID-19 and youth module in Wave 6. Researchers interested in using this data should note: (1) BNLA does not include data about migrants in the family and skilled streams of the permanent Migration Program; (2) BNLA only includes humanitarian migrants who arrived/were granted a visa during a specific time period; (3) Analysis at the state level is not possible.

This was the 69th wave of data collection undertaken by the Social Research Centre’s probability-based panel, Life in Australia™. This wave was run on behalf of the Australian National University, focussing on experiences with COVID-19, mental health, employment, optimism about the past and future, policy issues, trust of organisations, gambling.

As of May 2, 2023, the outbreak of the coronavirus disease (COVID-19) had spread to almost every country in the world, and more than 6.86 million people had died after contracting the respiratory virus. Over 1.16 million of these deaths occurred in the United States.

Waves of infections Almost every country and territory worldwide have been affected by the COVID-19 disease. At the end of 2021 the virus was once again circulating at very high rates, even in countries with relatively high vaccination rates such as the United States and Germany. As rates of new infections increased, some countries in Europe, like Germany and Austria, tightened restrictions once again, specifically targeting those who were not yet vaccinated. However, by spring 2022, rates of new infections had decreased in many countries and restrictions were once again lifted.

What are the symptoms of the virus? It can take up to 14 days for symptoms of the illness to start being noticed. The most commonly reported symptoms are a fever and a dry cough, leading to shortness of breath. The early symptoms are similar to other common viruses such as the common cold and flu. These illnesses spread more during cold months, but there is no conclusive evidence to suggest that temperature impacts the spread of the SARS-CoV-2 virus. Medical advice should be sought if you are experiencing any of these symptoms.

COVID-19 rate of death, or the known deaths divided by confirmed cases, was over ten percent in Yemen, the only country that has 1,000 or more cases. This according to a calculation that combines coronavirus stats on both deaths and registered cases for 221 different countries. Note that death rates are not the same as the chance of dying from an infection or the number of deaths based on an at-risk population. By April 26, 2022, the virus had infected over 510.2 million people worldwide, and led to a loss of 6.2 million. The source seemingly does not differentiate between "the Wuhan strain" (2019-nCOV) of COVID-19, "the Kent mutation" (B.1.1.7) that appeared in the UK in late 2020, the 2021 Delta variant (B.1.617.2) from India or the Omicron variant (B.1.1.529) from South Africa.

Where are these numbers coming from?

The numbers shown here were collected by Johns Hopkins University, a source that manually checks the data with domestic health authorities. For the majority of countries, this is from national authorities. In some cases, like China, the United States, Canada or Australia, city reports or other various state authorities were consulted. In this statistic, these separately reported numbers were put together. Note that Statista aims to also provide domestic source material for a more complete picture, and not to just look at one particular source. Examples are these statistics on the confirmed coronavirus cases in Russia or the COVID-19 cases in Italy, both of which are from domestic sources. For more information or other freely accessible content, please visit our dedicated Facts and Figures page.

A word on the flaws of numbers like this

People are right to ask whether these numbers are at all representative or not for several reasons. First, countries worldwide decide differently on who gets tested for the virus, meaning that comparing case numbers or death rates could to some extent be misleading. Germany, for example, started testing relatively early once the country’s first case was confirmed in Bavaria in January 2020, whereas Italy tests for the coronavirus postmortem. Second, not all people go to see (or can see, due to testing capacity) a doctor when they have mild symptoms. Countries like Norway and the Netherlands, for example, recommend people with non-severe symptoms to just stay at home. This means not all cases are known all the time, which could significantly alter the death rate as it is presented here. Third and finally, numbers like this change very frequently depending on how the pandemic spreads or the national healthcare capacity. It is therefore recommended to look at other (freely accessible) content that dives more into specifics, such as the coronavirus testing capacity in India or the number of hospital beds in the UK. Only with additional pieces of information can you get the full picture, something that this statistic in its current state simply cannot provide.

Abstract

The COVID-19 Preventive Health Survey was designed to help policymakers and health researchers better monitor and understand people’s knowledge, attitudes and practices about COVID-19 to improve communications and their response to the pandemic. This survey is conducted in partnership between Facebook, the Massachusetts Institute of Technology (MIT), and Johns Hopkins University (JHU), with advice from the World Health Organization. Sampled users see the invitation at the top of their News Feed, but the surveys are collected off the Facebook app and the Facebook company does not collect or receive individual survey responses. The survey asks users to self-report their adherence to preventive measures, such as wearing masks and what they know about COVID-19, including symptoms of the disease, risk factors and how their community is handling the pandemic.

Geographic coverage

This survey was fielded in 67 countries and territories.

Wave Countries and Territories: Argentina, Bangladesh, Brazil, Colombia, Egypt, France, Germany, India, Indonesia, Italy, Japan, Malaysia, Mexico, Nigeria, Pakistan, Philippines, Poland, Romania, Thailand, Turkey, United Kingdom, United States, Vietnam

Snapshot Countries and Territories: Afghanistan, Algeria, Angola, Australia, Azerbaijan, Bolivia, Cambodia, Cameroon, Canada, Chile, Cote d’Ivoire, Ecuador, Estonia, Georgia, Ghana, Guatemala, Honduras, Iraq, Jamaica, Kazakhstan, Kenya, Mongolia, Morocco, Mozambique, Myanmar, Nepal, Netherlands, Peru, Portugal, Senegal, Singapore, South Africa, South Korea, Spain, Sri Lanka, Sudan, Taiwan, Tanzania, Trinidad & Tobago, Uganda, Ukraine, United Arab Emirates, Uruguay, Venezuela

Analysis unit

• Public Aggregate Data: Subnational levels
• Microdata through Facebook Data for Good program: Individual level

Universe

The target population consists of active Facebook users. The sampling frame is active Facebook users with ages 18+, which includes users living within 23 countries or territories. The sampling frame is restricted to people who use Facebook in one of the supported locales.

Kind of data

Sample survey data [ssd]

Sampling procedure

The target population consists of active Facebook users. The sampling frame is active Facebook users with ages 18+, which includes users living within 23 countries or territories. The sampling frame is restricted to people who use Facebook in one of the supported locales.

The Facebook app invites a sample of adult users to take an optional, off-Facebook survey through an invitation at the top of their Facebook News Feed. Users who click on the invitation are redirected to a Qualtrics page hosted by MIT where they are informed about the survey and can take the survey. While MIT designs, collects, and analyzes the survey data, Facebook provides assistance with questionnaire translation, survey sampling and recruitment, and statistical bias correction.

Mode of data collection

Internet [int]

Research instrument

The survey includes questions about self-reported preventive behaviors and knowledge and attitudes towards COVID-19 vaccines. The survey instrument is managed by MIT and available in more than 55 languages. Two versions of the survey were fielded across 67 countries and territories. Countries with sufficient sample sizes receive a “Wave Survey” that is fielded every 2 weeks between July 2020 and March 2021. The rest of the countries receive a periodic “Snapshot Survey”. Snapshot and wave surveys were developed based on feedback from global health partners so that information could be collected that is helpful to inform public health responses even in areas with fewer survey respondents. As of Spring 2021, some questions from the survey have been merged with the larger Covid-19 Trends and Impact Survey. The full survey instrument is available here.

The snapshot survey was fielded to 44 countries and territories with a one-time sample over a 2 week period. A follow up sample was done in late 2020 of snapshot countries and territories to provide updated information.

The wave survey was fielded to 23 countries and territories with repeated, bi-monthly cross-sections. Each of the 8 waves is two weeks long. Sampled users may be invited to take the survey again in subsequent weeks, depending on the density of their area. However, the responses of sampled users who participate more than once will not be linked longitudinally.

Response rate

Response rates to online surveys vary widely depending on a number of factors including survey length, region, strength of the relationship with invitees, incentive mechanisms, invite copy, interest of respondents in the topic and survey design.

Sampling error estimates

Any survey data is prone to several forms of error and biases that need to be considered to understand how closely the results reflect the intended population. In particular, the following components of the total survey error are noteworthy:

Sampling error is a natural characteristic of every survey based on samples and reflects the uncertainty in any survey result that is attributable to the fact that not the whole population is surveyed.

Facebook provides MIT (and other researchers) with analytic weights that adjust for non-response and coverage biases. Making adjustments using the weights ensures that the sample more accurately reflects the characteristics of the target population represented.

Data appraisal

Non-Response Bias This means that some sampled users are more likely to respond to the survey than others. To adjust for this, Facebook calculates the inverse probability that sampled users complete the survey using their self-reported age and gender as well as other characteristics known to correlate with nonresponse. Then these inverse probabilities are used to create weights for responses, after which the survey sample reflects the active adult user population on the Facebook app.

Coverage Bias This means not everyone in every country has a Facebook app account or uses their account regularly. To adjust for this, Facebook adjusts the weights created in the first step even further so that the distribution of age, gender, and administrative region of residence in the survey sample reflects that of the general population.

The survey was conducted on the 78th wave of Life in Australia™, the Social Research Centre’s probability-based online panel. The Australian National University commissioned the Social Research Centre to conduct ANUpoll 56. The survey focused on experiences with COVID-19, mental health and wellbeing, employment, fertility, and federalism.

In November 2020, 29 percent of businesses in Australia indicted that they invested in capital expenditure due to future economic uncertainty. While this was the most common reason for capital expenditure in both August and November, significantly more businesses were undertaking capital expenditure in August, at the height of Australia's coronavirus second wave.

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

This was the 66th wave of data collection undertaken by the Social Research Centre’s probability-based panel, Life in Australia™. This wave was run on behalf of the Australian National University, focussing on current events around the world, experiences with COVID-19, mental health, employment, data privacy and support for randomised controlled trials.

This dataset is the 33rd ANU Poll undertaken by the Social Research Centre for the ANU. The purpose of the ANU Poll is to assess Australians' opinions on important and topical issues. These polls are typically conducted three times a year, or about every four months. Some questions appear in every poll in order to provide information about changes in opinion over time; the majority of questions appear in one poll only. This research is used to inform public debate and policy about issues affecting Australia. In this particular wave of the project, we investigate Australia's experiences during the COVID-19 pandemic. This includes questions regarding hygiene and social distancing measures, mental health and social impacts, and economic impacts of the pandemic. Finally, we ask Australians about their personal experiences of ethnicity-based discrimination.

The first wave of the COVID-19 pandemic led to significant socioeconomic changes in Canada due to business and school closures, and related job losses. This increased food insecurity among vulnerable populations, as well as many who had not been previously food insecure, placing unprecedented demand on charitable food organizations. This study documented the pandemic’s impact on charitable food organizations in Manitoba, Canada during the first wave in spring 2020. Using a multi-method design, data on pandemic-related program challenges and newly implemented policies/procedures were collected from: food bank organization websites and Facebook pages; online news media outlets; and semi-structured interviews with food organization leadership. Inductive thematic analysis was used to identify emerging patterns and themes. Second level coding was used to integrate data from different sources. Six challenge themes emerged: increased need for services; acquisition and distribution of food supply; staff and volunteer resource management; emotional vulnerability of staff, volunteers, and clients; difficulties with internal and external communications; and lack of structural supports. Five policy/procedure themes emerged: program and service delivery changes; finance and administrative changes; safety protocols; advocacy for resources and community engagement; and changes to paid and volunteer staffing. The first wave of COVID-19 had a significant impact on the Manitoba charitable food sector. Food banks re-configured programs to meet client needs amid shifting public health directives, with diminished resources, rising demand, and insufficient government support. Despite the resiliency of community food organizations during the pandemic, the status quo with respect to addressing food insecurity is inefficient and inadequate.La première vague de la pandémie de COVID-19 a mené à des changements socioéconomiques considérables au Canada en raison des fermetures de commerces et d’écoles et des pertes d’emplois concomitantes. Cela a augmenté l’insécurité alimentaire pour les populations vulnérables ainsi que pour plusieurs personnes qui n’avaient jamais été dans cette situation auparavant. La demande auprès des organismes d’aide alimentaire en a été sans précédent. Cette étude a documenté l’effet de la pandémie sur les organismes d’aide alimentaire au Manitoba, Canada, durant la première vague, au printemps 2020. À l’aide d’une méthode multiple, les données sur les défis des programmes liés à la pandémie et les nouvelles stratégies et procédures mises en place ont été collectées à partir des sites Web et des pages Facebook des banques alimentaires, des sorties dans les nouvelles des médias en ligne et des entretiens semi-structurés avec des responsables d’organismes alimentaires. L’analyse inductive thématique a été utilisée pour repérer les tendances et les thèmes récurrents. Le codage de second niveau a été utilisé pour intégrer des informations de différentes sources. Six motifs de défi en sont ressortis : l’accroissement des besoins vis-à-vis des services; l’acquisition et la distribution des denrées; la gestion du personnel et des bénévoles, la vulnérabilité émotionnelle du personnel, des bénévoles et de la clientèle; les difficultés de communication interne et externe; et le manque de soutien structurel. Cinq thèmes concernant les stratégies et les procédures ont aussi émergé : les changements dans les programmes et les services de livraison; les changements sur les plans administratifs et financiers; les protocoles de sécurité; la revendication de ressources et l’appel à l’engagement de la communauté; et les changements sur le plan du travail payé et bénévole. La première vague de COVID-19 a eu un effet important dans le secteur alimentaire caritatif. Les banques alimentaires ont reconçu leurs programmes pour répondre aux besoins de leur clientèle tout en s’adaptant aux directives changeantes de la santé publique, et ce, avec des ressources réduites, une demande accrue et un soutien gouvernemental insuffisant. Bien que les organismes communautaires à vocation alimentaire aient fait preuve de résilience durant la pandémie, en matière de gestion de l’insécurité alimentaire, le statu quo est inefficace et inadéquat.

COVID-19 and associated public health policies have significantly disrupted the lives of both adults and children. Experiences of COVID-positive adults are well described but less is known about the experiences of families of children who receive a positive diagnosis, and the impact of public health policies on this experience. This study aimed to develop a framework to understand the lived experience of families with a child testing positive for COVID-19. We applied a qualitative study design, using grounded theory. The study took place in Melbourne, Australia between July and December 2020, during the first major Australian COVID-19 wave. Parents of children 0–18 years tested at a walk-in clinic at a paediatric tertiary referral hospital were invited to participate. Two interviewers jointly undertook in-depth interviews with parents of children who tested positive. Interviews were transcribed and two analysts used an inductive, critical realist analysis approach with NVivo and a virtual whiteboard. Results are presented incorporating a stratified reality (empirical, actual, real). Families described seven sequential stages of the COVID-19 positive testing journey: COVID-19 close to home; time to be tested; waiting for the test result; receiving the result; dealing with the diagnosis; coping with isolation; and moving forward/looking back. Our findings highlight how public health policies and messages targeting the general (adult) public were experienced by families. We provide a framework that families move through when their child tests positive for COVID-19. Within each phase, we report unmet needs and identify strategies to improve future pandemic planning for parents and children.