Availability of open data related to COVID-19 epidemic in Italy.

Background: Since the beginning of the COVID-19 outbreak in Italy, health authorities have released epidemiologic data about this disease. These data were the most important sources of information which were periodically updated and analyzed by researchers to predict the spread of the epidemic. However, comprehensive and timely data on the evolution of COVID-19 have not always been made available to researchers and physicians. Method: The aim of our work is to investigate quality, availability and format of epidemiologic data about COVID-19 in Italy in different territorial and temporal areas. We tried to access the online resources made available by each of the 19 Italian Regions and the two autonomous Provinces, and in more detail by the Local Health Authorities of one of them, the Emilia-Romagna Region. We analyzed the main sources and flows of data (namely new and cumulative cases of infection, total swabs, new and cumulative COVID-19 deaths, overall and divided by sex), describing their characteristics such as accessibility, format and completeness. We eventually reviewed the data published by the Italian Ministry of Health, the National Institute of Health (ISS) and the Civil Protection Department. The Tim Berners-Lee scale was used to evaluate the open data format. Results: The flow of COVID-19 epidemiologic data in Italy originated from the Local Health Authorities that transmitted the data - on a daily basis - to the regional authorities, which in turn transferred them to the national authorities. We found a rather high heterogeneity in both the content and the format of the released data, both at the local and the regional level. Few Regions were releasing data in open format. ISS was the only national source of data that provided the number of COVID-19 health outcomes divided by sex and age groups since Spring 2020. Conclusions: Despite multiple potential useful sources for COVID-19 epidemiology are present in Italy, very few open format data were available both at a macro geographical level (e.g. per Region) and at the provincial level. The access to open format epidemiologic data should be eased, to allow researchers to adequately assess future epidemics and therefore favor timely and effective public health interventions.

Automating The Impact Reporting Of NICE Guidance

IntroductionThe National Institute for Health and Care Excellence (NICE) intends to automate the way it monitors the uptake, impact, and value of its guidance. Traditionally this has been done by developing impact reports, long documents that, while well received, are time consuming to develop and can quickly become outdated.MethodsWe focused on a novel topic that would benefit from new data sources to examine its impact: a rapid guideline for managing the long-term effects of coronavirus disease 2019 (COVID-19). We shortlisted "measurable” recommendations within the guideline that were likely to be captured in data collections. We then reviewed available data sources that included relevant up-to-date data. Finally, we explored what existing methods were available to NICE for automating impact reporting.ResultsFor long COVID-19 we accessed OpenSAFELY, a secure, transparent software platform for primary care COVID-19 data that was developed in response to the pandemic. This captured data on the management of long COVID-19 in primary care as well as onward referral to specialist clinics. In addition, we accessed data from the CVD-COVID-UK/COVID-IMPACT Consortium, which links general practice records with primary care dispensing data. This enabled us to analyze the impact of the pandemic on the prescribing and dispensing of cardiovascular disease medications. Working with our digital team we developed an automated impact reporting dashboard using Google's data studio. This enabled different views of the data, for example by region or socioeconomic status, to be presented in an automated way.ConclusionsAutomating the impact reporting of NICE guidance provides up-to-date information on its value to the health system. While we were able to collect new sources of data and automate some aspects of how these were viewed, full automation requires several enablers. These include an application programming interface between the data sources and NICE, and ensuring that NICE guidance is computer readable so that its measurement is practical in healthcare systems.

STOPPAGE: Spatio-temporal data driven cloud-fog-edge computing framework for pandemic monitoring and management

Several global health incidents and evidences show the increasing likelihood of pandemics (large-scale outbreaks of infectious disease), which has adversely affected all aspects of human lives. It is essential to develop an analytics framework by extracting and incorporating the knowledge of heterogeneous data-sources to deliver insights for enhancing preparedness to combat the pandemic. Specifically, human mobility, travel history, and other transport statistics have significantly impact on the spread of any infectious disease. This article proposes a spatio-temporal knowledge mining framework, named STOPPAGE, to model the impact of human mobility and other contextual information over the large geographic areas in different temporal scales. The framework has two key modules: (i) spatio-temporal data and computing infrastructure using fog/edge based architecture;and (ii) spatio-temporal data analytics module to efficiently extract knowledge from heterogeneous data sources. We created a pandemic-knowledge graph to discover correlations among mobility information and disease spread, a deep learning architecture to predict the next hotspot zones. Further, we provide necessary support in home-health monitoring utilizing Femtolet and fog/edge based solutions. The experimental evaluations on real-life datasets related to COVID-19 in India illustrate the efficacy of the proposed methods. STOPPAGE outperforms the existing works and baseline methods in terms of accuracy by (Formula presented.) (18–21)% in predicting hotspots and reduces the power consumption of the smartphone significantly. The scalability study yields that the STOPPAGE framework is flexible enough to analyze a huge amount of spatio-temporal datasets and reduces the delay in predicting health status compared to the existing studies. © 2022 John Wiley & Sons Ltd.

Data Management in EpiGraph COVID-19 Epidemic Simulator

The transmission of COVID-19 through a population depends on many factors which model, incorporate, and integrate many heterogeneous data sources. The work we describe in this paper focuses on the data management aspect of EpiGraph, a scalable agent-based virus-propagation simulator. We describe the data acquisition and pre-processing tasks that are necessary to map the data to the different models implemented in EpiGraph in a way that is efficient and comprehensible. We also report on post-processing, analysis, and visualization of the outputs, tasks that are fundamental to make the simulation results useful for the final users. Our simulator captures complex interactions between social processes, virus characteristics, travel patterns, climate, vaccination, and non-pharmaceutical interventions. We end by demonstrating the entire pipeline with one evaluation for Spain for the third COVID wave starting on December 27th of 2020. © 2022, Springer Nature Switzerland AG.

Analyzing the Impact of Lockdown in Controlling COVID-19 Spread and Future Prediction

COVID-19 outbreaks are the critical challenge to the administrative units of all worldwide nations. India is also more concerned about monitoring the virus’s spread to control its growth rate by stringent behaviour. The present COVID-19 situation has huge impact in India, and the results of various preventive measures are discussed in this paper. This research presents different trends and patterns of data sources of States that suffered from the second wave of COVID-19 in India until 3rd July 2021. The data sources were collected from the Indian Ministry of Health and Family Welfare. This work reacts particularly to many research activities to discover the lockdown effects to control the virus through traditional methods to recover and safeguard the pandemic. The second wave caused more losses in the economy than the first wave and increased the death rate. To avoid this, various methods were developed to find infected cases during the regulated national lockdown, but the infected cases still harmed unregulated incidents. The COVID-19 forecasts were made on 3rd July 2021, using exponential simulation. This paper deals with the methods to control the second wave giving various analyses reports showing the impact of lockdown effects. This highly helps to safeguard from the spread of the future pandemic.

Data Dashboards for Advancing Health and Equity: Proving Their Promise?

Some analyze health and health equity data to distinct geographic boundaries.3 Others present and disseminate a new metric.4 Still others aggregate local policies and laws that affect population health to guide research and advocacy.5 During the rapidly unfolding COVID-19 pandemic, many state and local health departments struggled to make data publicly available, and the Johns Hopkins University COVID Tracker quickly became the "go-to" data source for by-the-day counts of COVID-19 cases, deaths, tests, and vaccinations.6 Other COVID-19 dashboards have since drawn explicit attention to COVID-19 inequities7-9 (see Table 1 for examples). [...]quantitative indicators of a dashboard's usefulness must also be measured, including visit and revisit frequency data from Web site analytics, as well as media, social media, and scientific article citations.10 Dashboards generally seek to "liberate" access to data. [...]in early 2020, the Opportunity Atlas team at Harvard University used data from private companies to add granular measures of local shifts in economic activity resulting from pandemic shutdowns.12 The City Health Dashboard added a census tract-level measure of COVID-19 local riskto guide local testing and vaccination efforts.3 Rigorous methods to measure a dashboard's actual impact on health and health equity are more elusive. The more complex a Web site is in terms of data and functionality (e.g., number of metrics, number of underlying data sources, range of geographies, comparison functions, multiyear data), the more staff time is required to ensure that all facets are updated regularly. Because of this, and because there will always be salient new and creative ways to combine data, we anticipate that health data dashboards will continue to be developed by both public and nonpublic actors, with financial support from foundation and federal agency grants.

Historical inclusion and exclusion: New Zealand archives as primary resources for historical research

Purpose>In response to the special issue call for papers on international sources for advertising and marketing history, this paper aims to provide information, this paper provides information on two prominent New Zealand archives: Archives New Zealand and the Alexander Turnbull Library (ATL).Design/methodology/approach>Archives New Zealand and the ATL were chosen as they are the two largest archives in New Zealand, and both have different but complementary roles – one for the preservation of government records and the other for the preservation of private collections. The history of each is provided as well as a discussion of relevant materials for marketing historians. This is followed by a discussion of the limitations of the archives with regards to their colonial contexts and potential for ignoring the “other” over the years.Findings>Archives New Zealand houses official government documents and thus occupational registrations, licences, trademarks, patents and copyright records are held, along with unique product design registration files and the complete history of health promotion in New Zealand. The ATL houses personal and thus biographically useful photographs, society records and minutes, personal letters and diaries, photos and glass plate negatives, portraits and paintings, architectural works and music.Originality/value>For researchers pursuing historical research in marketing, the archival documents offered by government archives and donated private collections from throughout the world provide invaluable resources. This paper also provides a discussion of the colonial focus on record-keeping and potential bias stemming from colonial structures of government and lack of representation of marginalised groups.

Innovation during the COVID-19 crisis: Why it was more critical for official statistics than ever1

During the COVID-19 pandemic, quantitative evidence was desperately needed in order to understand and manage an unprecedented situation, and to make important national, European and international decisions. Eurostat and the national statistical institutes (NSIs) played a key role in managing the pandemic, providing society with a high quality statistical information service. In particular, the crisis accelerated innovation in statistical production, steered complex processes of change towards the use of new data sources and privately held data for official statistics, enhanced the adoption of new statistical methods, and consequently the production of experimental statistics and dashboards.While the new data ecosystem provided opportunities for the production of official statistics, the importance of privacy preservation, data security and the development of adequate data quality frameworks remained a priority. Important strands of work for the future would be: satisfying the increased needs of users, as well as supporting decision-making and the delivery of government services in emergencies. NSIs would also do well to invest in innovation, collaborate and establish partnerships with the data providers and research communities that have worked closely with them since the beginning of the pandemic. This article is based on the experiences of six NSIs in the Netherlands, Spain, France, Italy, Germany and Finland during the pandemic. [ FROM AUTHOR] Copyright of Statistical Journal of the IAOS is the property of IOS Press and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

PIETS: Parallelised Irregularity Encoders for Forecasting with Heterogeneous Time-Series

Heterogeneity and irregularity of multi-source data sets present a significant challenge to time-series analysis. In the literature, the fusion of multi-source time-series has been achieved either by using ensemble learning models which ignore temporal patterns and correlation within features or by defining a fixed-size window to select specific parts of the data sets. On the other hand, many studies have shown major improvement to handle the irregularity of time-series, yet none of these studies has been applied to multi-source data. In this work, we design a novel architecture, PIETS, to model heterogeneous time-series. PIETS has the following characteristics: (1) irregularity encoders for multi-source samples that can leverage all available information and accelerate the convergence of the model;(2) parallelised neural networks to enable flexibility and avoid information over-whelming;and (3) attention mechanism that highlights different information and gives high importance to the most related data. Through extensive experiments on real-world data sets related to COVID-19, we show that the proposed architecture is able to effectively model heterogeneous temporal data and outperforms other state-of-the-art approaches in the prediction task.

Sources Used to Data-Driven Decision-Making in Tourism Management. Identifying the Main Areas of Research

The tourism sector is one of the most affected by the health situation caused by COVID-19. As a result, digital transformation is accelerating in this sector. One of the pillars of this transformation is the management of organizations based on data-driven decision-making. The raw material for such data-driven strategies is obviously the sources of information used. This paper attempts to give a knowledge map of the diverse sources of information used in tourism for this decision-making. To this purpose, we analyse the scientific publications of the last five years in order to identify the main areas of action related to the sources used for data-driven management in tourism. As a result of this bibliometric analysis, we have identified 14 topics that have attracted the interest of the scientific community grouped into three main areas of action. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Accessing primary sources for marketing and advertising history research from family history websites

Purpose: The purpose of this study is to respond to the Journal of Historical Research in Marketing special issue call for discussions that can assist advertising and marketing history researchers locate primary sources of interest to their research by describing the resources available through the online family history websites Ancestry.com and FindMyPast.com. Design/methodology/approach: Brief histories of Ancestry and FindMyPast are presented, based on publicly available records and secondary sources. This paper explains the types of data researchers can access via Ancestry.com and FindMypast.com, the costs of access and then provides some examples of how these resources have been used in past research by marketing and advertising historians. Findings: Family history websites such as Ancestry and FindMyPast can provide researchers with access to a wide variety of data sources, such as census and voting records;immigration records;city directories;birth, marriage and death records;military records;and almanacs and gazetteers, but at a cost. In some cases, paying for digital access to records is more convenient, timely and can cost less than travelling to access these same documents in physical form. Depending on the researcher’s geographical location and the country from which records are sought, this can add up to quite a cost savings. When using these sources, it is wise to determine which database contains more of the records you are searching for;Ancestry tends to have better US and Canadian resources, while FindMyPast covers the UK better. Originality/value: Researchers interested in conducting advertising and marketing history research need access to primary data sources. Given restricted travel budgets and, indeed, restricted travel under COVID-19 conditions, gaining access to primary sources in digital form can allow researchers to continue their work. At any time, gaining access to digital records without having to travel can speed up the research process. Researchers new to the field, and those with many years of experience, can benefit from learning more about family history databases as primary data sources. © 2022, Emerald Publishing Limited.

Responsible, Automated Data Gathering for Timely Citizen Dashboard Provision during a Global Pandemic (COVID-19)

Creating a public understanding of the dynamics of a pandemic, such as COVID-19, is vital for introducing restrictive regulations. Gathering diverse data responsibly and sharing it with experts and citizens in a timely manner is challenging. This article reviews methodologies of COVID-19 dashboard design and discusses both technical and non-technical challenges associated. Advice and lessons learned from building a citizen-focused, automated county-precision dashboard for Germany are shared. Within four months, the web-based tool had 5 million unique visitors and 70 million sessions. Three developers set up the basic version in less than one week. Early on, data was screen scraped. An iterative process improved timeliness by adding more fine-grained data sources. A collaborative online table editor enabled near real-time corrections. Alerting was setup for errors, and statistics apply for sanity checking. Static site generation and a content delivery network help to serve large user loads in a timely manner. The flexible design allowed to iteratively integrate more complex statistics based on expert knowledge built on top of the collected data and secondary data sources such as ICU beds and citizen movement. © 2020 Owner/Author.

(In)Visible Tourism According to Online Cash Registers in Hungary, 2018–2020

The aim of this paper is to share innovations and some key lessons learned in the use of non-traditional data sources to improve data quality and enable more accurate and efficient data use in the field of tourism. Research on visitor traffic is based on classical statistical measures, but it may be worth expanding it with alternative data sources, such as databases based on online cash register (OCR) data. These data can be particularly useful for analysing tourism-related consumption habits in a given area. The study introduces the “invisible”, tourism-related, non-accommodation spending characteristics of transit traffic in Hungary, the possibilities of its analysis and spatial aspects, using online cash register data (includes all retail sales in Hungary, except for motorcycle purchases), and additionally, we identify the most affected municipalities which are invisible for traditional data sources. The results show that invisible tourism, linked to transit traffic, has significant economic potential. The analysis of this new type of database will provide a more accurate and faster picture of consumption associated with hidden tourism, which can be an important input for economic and marketing development.

Estimation of the Origin-Destination Matrix for Trucks That Use Highways: A Case Study in Chile

Nowadays, freight transport is crucial in the functioning of cities worldwide. To dig further into the understanding of urban freight transport movements, in this research, we conducted a case study in which we estimated an origin-destination matrix for the trucks traveling on Autopista Central, one of Santiago de Chile’s most important urban highways. To do so, we used full real-world vehicle-by-vehicle information of freight vehicles’ movements along the highway. This data was collected from several toll collection gates equipped with free-flow and automatic vehicle identification technology. However, this data did not include any vehicle information before or after using the highway. To estimate the origins and destinations of these trips, we proposed a multisource methodology that used GPS information provided by SimpliRoute, a Chilean routing company. Nevertheless, this GPS data involved only a small subset of trucks that used the highway. In order to reduce the bias, we built a decision tree model for estimating the trips’ origin, whose input data was complemented by other public databases. Furthermore, we computed trip destinations using proportionality factors obtained from SimpliRoute data. Our results showed that most of the estimated origins belonged to outskirt municipalities, while the estimated destinations were mainly located in the downtown area. Our findings might help improve freight transport comprehension in the city, enabling the implementation of focused transport policies and investments to help mitigate negative externalities, such as congestion and pollution.

The Impact of the Pandemic on Demographic Processes in the Russian Arctic

Russia has achieved a high level of Internet connectivity and the use of digital technologies;this helps to accumulate and systematize huge amounts of population data. Modern challenges, such as the COVID-19 pandemic, require a more prompt and detailed analysis of the demographic situation. Understanding the information collected by digital platforms and services can improve the quality of decision-making and be widely used in science and management. The aim of our study is to assess the change in the demographic situation in the Russian Arctic under the influence of the pandemic, with the use of new sources of population data that have emerged as a result of digitalization of the economy and public life. The article proposes an outline for the formation of a demographic knowledge base by combining traditional population statistics with data from digital platforms. We consider advantages and disadvantages of new data sources, features and examples of their application. We provide a detailed description of demographic processes in the Arctic Zone of the Russian Federation in 2020-2021 with the use of municipal statistics, data from Yandex online platforms and international pandemic databases. With the help of the proposed outline, we consider the dynamics of morbidity, mortality and vaccination against coronavirus infection. We study the reaction of the population of the Russian Arctic to the pandemic by analyzing the structure of search queries and the intensity of movement in city streets. We reveal the specifics of the spread of COVID-19 in the Arctic and estimate the impact of the pandemic on the natural population change and human mobility in the Arctic Zone. We calculate excess mortality at the regional and municipal levels. Based on the vaccination rates, we draw conclusions about the prospects for further development of the pandemic. The results obtained can be used for development of socio-demographic policy measures and construction of demographic forecasts for the Northern and Arctic territories.

The value of network data confirmed by the Covid-19 epidemic and its expanded usages

Data driven analysis is proven to create a competitive advantage to business. Governments and nonprofit organizations also turn to Big Data to harness its benefits and use it for social good. Among different types of data sources, location data collected from mobile networks is especially valuable for its representativeness, real-time observation, and versatility. There is a distinction between mobile positioning data (MPD) generated by the exchanges between mobile devices and the core network;versus over-the-top or system-level location data collecting individual GPS location. MPD is composed of all mobile network events regardless of the mobile phone brand, operating system, app usage, frequency bands or mobile generation;it is uniform and ubiquitous. Getting the best out of MPD relies on the knowledge of how to create an advanced algorithm for homogeneously processing this massive, complex data into insightful indicators. Anonymized and aggregated MPD enables the testing of multiple combinations with other data sources, fully abiding by GDPR, to arrive at innovative solutions. These unique insights can help tackle societal challenges (the state of mobile data for social good June 2017 GSMA, UN Global pulse). It can help to establish accurate statistics about population movements, density, location, social patterns, finances, and ambient environmental conditions. This article demonstrates how MPD has been used to help combat Covid-19 in Europe, the Middle East, and Africa. Furthermore, depending on the future direction, MPD and data analysis can serve powering economic development as well as working toward the Sustainable Development Goals, whilst respecting data privacy.

Quantitative data about deaths due to COVID-19 and comparability between countries: An approach through the case of Spain

Mortality statistics tend to be inaccurate because of the imperfections related to individual deaths' recording. Recently, the COVID-19 pandemic has brought controversies regarding the quantification of deaths in many countries. Mainly, controversies were fueled by the sudden change of the criteria being applied, the limited testing and tracing capacities, and the collapse of the healthcare system. This work analyses the case of Spain, which constitutes one of the European countries with the highest number of cases and deaths during the 'first wave', and where these numbers were highly controversial. It provides a discussion about the coherence, traceability, and limitations of quantitative data sources, as a basis to improve the quality of the data and its comparability between different countries and over time. Official data sources and non-official data sources are considered. Finally, suggestions of improvement and research needs are gathered, for the reliability of mortality data as a way to enhance learning and resilience for future crises. © 2021 Information Systems for Crisis Response and Management, ISCRAM. All rights reserved.

Data and Digital Solutions to Support Surveillance Strategies in the Context of the COVID-19 Pandemic.

Background: In order to prevent spread and improve control of infectious diseases, public health experts need to closely monitor human and animal populations. Infectious disease surveillance is an established, routine data collection process essential for early warning, rapid response, and disease control. The quantity of data potentially useful for early warning and surveillance has increased exponentially due to social media and other big data streams. Digital epidemiology is a novel discipline that includes harvesting, analysing, and interpreting data that were not initially collected for healthcare needs to enhance traditional surveillance. During the current COVID-19 pandemic, the importance of digital epidemiology complementing traditional public health approaches has been highlighted. Objective: The aim of this paper is to provide a comprehensive overview for the application of data and digital solutions to support surveillance strategies and draw implications for surveillance in the context of the COVID-19 pandemic and beyond. Methods: A search was conducted in PubMed databases. Articles published between January 2005 and May 2020 on the use of digital solutions to support surveillance strategies in pandemic settings and health emergencies were evaluated. Results: In this paper, we provide a comprehensive overview of digital epidemiology, available data sources, and components of 21st-century digital surveillance, early warning and response, outbreak management and control, and digital interventions. Conclusions: Our main purpose was to highlight the plausible use of new surveillance strategies, with implications for the COVID-19 pandemic strategies and then to identify opportunities and challenges for the successful development and implementation of digital solutions during non-emergency times of routine surveillance, with readiness for early-warning and response for future pandemics. The enhancement of traditional surveillance systems with novel digital surveillance methods opens a direction for the most effective framework for preparedness and response to future pandemics.

Availability and quality of publicly available health workforce data sources in Australia: a scoping review protocol.

INTRODUCTION: The health workforce is an integral component of the healthcare system. Comprehensive, high-quality data on the health workforce are essential to identifying gaps in health service provision, as well as informing future health workforce and health services planning, and health policy. While many data sources are used in Australia for these purposes, the quality of the data sources with respect to relevance, accessibility and accuracy is not clear. METHODS AND ANALYSIS: This scoping review aims to identify and appraise publicly available data sources describing the Australian health workforce. The review will include any data source (eg, registry, administrative database and survey) or document reporting a data source (eg, journal article, report) on the Australian health workforce, which is publicly available and describes the characteristics of the workforce. The search will be conducted in 10 bibliographic databases and the grey literature using an iterative process. Screening of titles and abstracts will be undertaken by two investigators, independently, using Covidence software. Any disagreement between investigators will be resolved by a third investigator. Documents/data sources identified as potentially eligible will be retrieved in full text and reviewed following the same process. Data will be extracted using a customised data extraction tool. A customised appraisal tool will be used to assess the relevance, accessibility and accuracy of included data sources. ETHICS AND DISSEMINATION: The scoping review is a secondary analysis of existing, publicly available data sources and does not require ethics approval. The findings of this scoping review will further our understanding of the quality and availability of data sources used for health workforce and health services planning in Australia. The results will be submitted for publication in peer-reviewed journals and presented at conferences targeted at health workforce and public health topics.

Measuring changes in alcohol use in Finland and Norway during the COVID-19 pandemic: Comparison between data sources.

OBJECTIVES: To examine (1) how a rapid data collection using a convenience sample fares in estimating change in alcohol consumption when compared to more conventional data sources, and (2) how alcohol consumption changed in Finland and Norway during the first months of the COVID-19 pandemic. METHODS: Three different types of data sources were used for the 2nd quarter of 2020 and 2019: sales statistics combined with data on unrecorded consumption; the rapid European Alcohol Use and COVID-19 (ESAC) survey (Finland: n = 3800, Norway: n = 17,092); and conventional population surveys (Finland: n = 2345, Norway: n1 = 1328, n2 = 2189, n3 = 25,708). Survey measures of change were retrospective self-reports. RESULTS: The statistics indicate that alcohol consumption decreased in Finland by 9%, while little change was observed in Norway. In all surveys, reporting a decrease in alcohol use was more common than reporting an increase (ratios 2-2.6 in Finland, 1.3-2 in Norway). Compared to conventional surveys, in the ESAC survey fewer respondents reported no change and past-year alcohol consumption was higher. CONCLUSION: The rapid survey using convenience sampling gave similar results on change in drinking as conventional surveys but higher past-year drinking, suggesting self-selection effects. Aspects of the pandemic driving alcohol consumption down were equally strong or stronger than those driving it up.