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Digital technologies have been central to efforts to respond to the COVID-19 pandemic. In this context, a range of literature has reported on developments regarding the implementation of new digital technologies for COVID-19–related surveillance, prevention, and control.
In this study, scoping reviews of academic and nonacademic literature were undertaken to obtain an overview of the evidence regarding digital innovations implemented to address key public health functions in the context of the COVID-19 pandemic. This study aimed to expand on the work of existing reviews by drawing on additional data sources (including nonacademic sources) by considering literature published over a longer time frame and analyzing data in terms of the number of unique
We conducted a scoping review of the academic literature published between January 1, 2020, and September 15, 2020, supplemented by a further scoping review of selected nonacademic literature published between January 1, 2020, and October 13, 2020. Both reviews followed the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) approach.
A total of 226 academic articles and 406 nonacademic articles were included. The included articles provided evidence of 561 (academic literature) and 497 (nonacademic literature) unique digital innovations. The most common implementation settings for digital innovations were the United States, China, India, and the United Kingdom. Technologies most commonly used by digital innovations were those belonging to the high-level technology group of
Digital innovations implemented in response to the COVID-19 pandemic have been wide ranging in terms of their implementation settings, the digital technologies used, and the public health functions addressed. However, evidence gathered through this study also points to a range of barriers that have affected the successful implementation of digital technologies for public health functions. It is also evident that many digital innovations implemented in response to the COVID-19 pandemic are yet to be formally evaluated or assessed.
Digital technologies, such as artificial intelligence (AI), robotics, and wearables, have been widely used in worldwide efforts to respond to the COVID-19 pandemic. In this context, a range of studies has reported on developments regarding the implementation of new digital technologies for COVID-19–related surveillance, prevention, and control. To consolidate this literature, several reviews have been undertaken [
In this study, we present the findings of a further scoping review on the implementation of digital technologies in response to the COVID-19 pandemic. The scoping review expands on the work of existing reviews by drawing on additional data sources while also considering literature published over a longer time frame (ie, January to September 2020). Although focusing on academic literature, the scoping review also goes beyond existing reviews by presenting evidence from a complementary review of nonacademic sources, including web-based technology-related news sources and news feeds (covering news articles, press releases, and blogs). The incorporation of wider nonacademic sources into this review allows for the consideration of technological developments in the private or public sector, which are not necessarily oriented toward research publications, thus helping to capture more up-to-date information on the implementation of digital technologies in response to the COVID-19 pandemic.
This scoping review goes beyond existing reviews by using the concept of
The specific research questions addressed by this scoping review were as follows:
What are the main characteristics of the literature reporting on digital innovations used in the context of the COVID-19 pandemic?
What has been the geographical setting of the digital innovations implemented in response to the COVID-19 pandemic?
What types of implemented digital innovations have been discussed in the academic and nonacademic literature in relation to COVID-19–related surveillance, prevention, and control?
Which key public health functions have been addressed by the digital innovations implemented in response to the COVID-19 pandemic?
The scoping review followed the approach specified in PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) checklist [
For the academic literature search, we developed and ran a search strategy in 2 bibliographic databases—EMBASE and Scopus—using the same strategy for both databases. The search terms used in this study are presented in
For the nonacademic literature search, we ran a search strategy using the news aggregation software Feedly [
Articles captured by both searches were screened against defined inclusion and exclusion criteria to determine their eligibility for the study.
We extracted data from the included articles using Microsoft Excel extraction templates: one template for the review of academic literature and one for the review of nonacademic literature. Both extraction templates included columns to capture information relating to the core research questions regarding the types and nature of implemented digital innovations, as well as broader information regarding the article type and identified barriers to implementing digital innovations in the discussed countries and regions. Where possible, drop-down menus were used to limit the range of responses that could be submitted, thereby facilitating data filtering and analysis. To ensure a consistent extraction approach, the 2 project teams conducted pilot extraction exercises using a small number of articles. The used extraction templates and drop-down menus are presented in
To analyze the extracted data, we used the software package R. Descriptive quantitative analysis focused on statistical and graphical summaries for each column of data captured using drop-down menus in the extraction template, together with relevant cross-analyses. Data extracted on barriers to the implementation of digital innovations were analyzed qualitatively.
In extracting data on digital innovations, we coded data on the specific digital technologies that have been used within these innovations, as well as the technology groups to which these technologies belong. The used coding approach drew on an earlier scoping review of the use of digital technologies for the prevention, surveillance, and control of infectious diseases. In this study, specific technologies identified in the literature were clustered into high-level technology groups of similar or conceptually related digital technologies [
Advanced manufacturing technologies
3D printing
Autonomous devices and systems
Drones
Robotics
Blockchain or distributed ledger technology
Blockchain or distributed ledger technology
Cloud computing or cloud-based networks
Cloud computing or cloud-based networks
Cognitive technologies
Artificial intelligence
Expert systems
Machine learning
Natural language processing
Facial recognition
(Artificial) neural networks
Crowdsourcing platforms
Crowdsourcing
Data analytics (including big data)
Data mining
Data analytics
Big data
Health informatics
Parallel computing
Social media and mobile data analysis
eHealth
Digital health, eHealth, and mobile health
Electronic health records
Telemedicine
Imaging and sensing technologies (including Geographic Information System)
Geographic Information System
Image processing
Infrared sensing
Satellite communication or imaging (including earth observation and remote sensing)
Immersive technologies
Virtual or augmented reality
Integrated and ubiquitous fixed and mobile networks
Cellular networks
SMS text message communications
Smartphone apps
Bluetooth
Smartphones and tablet computing devices
Web-based tools and platforms
Web-based learning platforms
Web-based self-assessment tools
Information management tools
Social media
Microblogging platforms
Blogging platforms
Instant messaging platforms
Networking platforms
Photograph or video-sharing platforms
Internet of Things
Internet of Things
Wireless sensor networks
Biosensors
Nanotechnology and microsystems
Digital DNA, RNA or protein analysis
Lab-on-chip
Nanotechnology
Quantum computing
Quantum computing
Simulation
Mathematical models or simulations
Wearables (including ingestibles)
Wearables (including smart fabrics and ingestibles)
We also coded each digital innovation as fulfilling ≥1 of the following seven key public health functions: (1) screening and diagnostics, (2) surveillance and monitoring, (3) contact tracing, (4) forecasting, (5) signal or outbreak detection and validation, (6) pandemic response, and (7) communication and collaboration. The use of these public health key functions followed a mapping of the European Centre for Disease Prevention and Control’s priorities against the 10 essential public health operations of the World Health Organization’s Regional Office for Europe [
As with the classification of technologies, our data extraction template included columns to record instances in which a digital innovation addressed >1 key public health function. For example, digital innovations performing surveillance and monitoring functions and signal or outbreak detection and validation were coded with both these public health functions. The coding of key public health functions was based on data extracted from the academic or nonacademic sources being reviewed. The emphasis within the article guided the assessment of how the codes were applied. The collation of data from multiple sources on the same innovations allowed us to capture where digital innovations addressed >2 key public health functions.
Identifying (including self-identifying) COVID-19 symptoms and the presence of SARS-CoV-2 in individuals
Systematic collection and analysis of relevant data such as SARS-CoV-2 infection rates and excess deaths along with ongoing monitoring of COVID-19 symptoms or adherence to COVID-19 restrictions at the individual and population levels
Identifying and alerting people who have been in contact with someone diagnosed with COVID-19 and who are therefore at high risk of having been exposed to SARS-CoV-2, so that they can take appropriate and sometimes mandated action (eg, self-isolating)
Predicting COVID-19 infections or health outcomes at the individual and population levels
Detecting and validating outbreaks of COVID-19
Responses to the pandemic that have helped widen safe access to and management of essential resources required by individuals and populations for COVID-19 prevention and response
Communication involves informing, educating, and empowering individuals and populations about COVID-19, and collaboration refers to working together across disciplines or sectors to share knowledge and improve the collective COVID-19 response
The search of academic databases returned a total of 5018 articles, of which 1408 (28.06%) were duplicates. Title and abstract screening of the remaining 3610 articles resulted in 3309 (91.66%) articles being excluded, with 301 (8.34%) deemed eligible for full-text review. Through targeted searches, we also included an additional 6 articles. Of these 307 articles, 81 (26.4%) were excluded during data extraction and analysis, resulting in 226 (73.6%) unique articles being included in the review of the academic literature.
For the review of nonacademic literature, the Feedly-based literature search returned a total of 4537 articles, of which 144 (3.17%) were duplicates. Title screening of the remaining 4393 articles resulted in 3904 (88.87%) articles being excluded, with 489 (11.13%) included for full-text review. We also included an additional 23 articles through targeted searching (n=10, 43% articles) and web scraping (n=13, 57% articles). Of these 512 articles, 107 (20.9%) were excluded during the data extraction and analysis. This resulted in 79.2% (406/512) unique nonacademic articles being included. PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) flow diagrams for the 2 scoping reviews are presented in
PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) flow diagrams for the review of academic literature (left) and nonacademic literature (right; review time frame for academic literature: January 1, 2020, to September 15, 2020; review time frame for nonacademic literature: January 1, 2020, to October 13, 2020).
All articles included in the review of academic literature fell into one of the following 3 article types: brief journal comment, editorial, letter, or opinion (98/226, 43.4% of articles), research articles (86/226, 38.1% articles), and detailed journal perspective, policy review, or practice reviews (42/226, 18.6% of articles). The 86 articles categorized as research articles were further analyzed in terms of their study type, with the following results: 67 (78%) primary studies, 2 (2%) mathematical models or simulations, 2 (2%) scoping reviews, and 14 (16%) other literature reviews. The study type of one of the research articles was unclear. All articles included in the review of nonacademic literature were news articles, blog posts, or press releases.
The publication dates of the included articles by month are shown in
For articles identified by the review of academic literature, we analyzed the geographical location of key contributors’ (first, last, and corresponding authors’) organizations. The country with the highest number of key contributor organizational affiliations was the United States (64/226, 28.3% of articles). Other countries with a high number of key contributor organizational affiliations included China (23/226, 10.2% of articles), the United Kingdom (20/226, 8.8% of articles), and India (13/226, 5.8% of articles).
Number of articles by publication date (by month) in the review of academic literature and nonacademic literature (review time frame for academic literature: January 1, 2020, to September 15, 2020; review time frame for nonacademic literature: January 1, 2020, to October 13, 2020)a.
Publication date | Included articles, n (%) | |
|
||
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January | 0 (0) |
|
February | 3 (1.3) |
|
March | 10 (4.4) |
|
April | 38 (16.8) |
|
May | 39 (17.3) |
|
June | 46 (20.4) |
|
July | 39 (17.3) |
|
August | 41 (18.1) |
|
September | 10 (4.4) |
|
||
|
January | 0 (0) |
|
February | 6 (1.5) |
|
March | 66 (16.3) |
|
April | 108 (26.7) |
|
May | 76 (18.8) |
|
June | 36 (8.9) |
|
July | 31 (7.7) |
|
August | 33 (8.1) |
|
September | 38 (9.4) |
|
October | 11 (2.7) |
aDue to rounding, the percentages for the nonacademic review do not add up to 100.
bFor one article within the review of nonacademic literature, no publication date was recorded. Although 406 articles were reviewed, the articles listed against months in the table therefore add up to 405.
Top 10 countries in which key contributors’ organizations were based in the review of academic literature (review time frame: January 1, 2020, to September 15, 2020; N=226).
Country of key contributors’ organization | Included articles, n (%) |
United States | 64 (28.3) |
China (mainland) | 23 (10.2) |
United Kingdom | 20 (8.8) |
India | 13 (5.8) |
Australia | 8 (3.5) |
France | 8 (3.5) |
Taiwan | 8 (3.5) |
Singapore | 7 (3.1) |
South Korea | 7 (3.1) |
Spain | 6 (2.7) |
Through our review of the academic literature, we identified 561 instances of the implementation of digital innovations to tackle the COVID-19 pandemic. Our review of the nonacademic literature identified 497 digital innovations. The 2 reviews were conducted independently. As such, the digital innovations identified by the review of nonacademic literature are not necessarily presented as unique from those identified by the review of academic literature. Although there is likely to be some crossover between the innovations captured by the 2 reviews, it is also the case that the more experimental review of nonacademic literature has captured some innovations, in particular those developed by private companies, that have not been captured within the review of academic literature, particularly as developments occurred rapidly during the first few months of the pandemic in 2020.
The identified digital innovations were analyzed by the geographical context in which they were implemented at both the regional and country levels. In the academic literature, 66.5% (373/561) of digital innovations were implemented in non-EU/EEA countries, with 21.4% (120/561) of digital innovations being implemented within EU/EEA countries. Approximately 12.1% (68/561) of digital innovations identified in our review were implemented worldwide. The countries with the highest number of implemented digital innovations, according to academic literature, were the United States of America (107/561, 19.1% of digital innovations), China (71/561, 12.7% of digital innovations), and India (28/561, 5% of digital innovations). The most common EU or EEA country implementation settings were France (18/561, 3.2% of digital innovations), Spain (18/561, 3.2% of digital innovations), and Italy (12/561, 2.1% of digital innovations; see
Top 10 countries in which the highest number of digital innovations have been implemented in the review of academic literature and nonacademic literature (review time frame for academic literature: January 1, 2020, to September 15, 2020; review time frame for nonacademic literature: January 1, 2020, to October 13, 2020).
Implementation setting | Digital innovations, n (%) | |
|
||
|
United States | 107 (19.1) |
|
China | 71 (12.7) |
|
India | 28 (5.0) |
|
United Kingdom | 25 (4.5) |
|
France | 18 (3.2) |
|
Spain | 18 (3.2) |
|
South Korea | 16 (2.9) |
|
Singapore | 13 (2.3) |
|
Canada | 12 (2.1) |
|
Italy | 12 (2.1) |
|
||
|
United States | 141 (28.4) |
|
United Kingdom | 60 (12.1) |
|
China | 38 (7.6) |
|
Italy | 13 (2.6) |
|
Spain | 11 (2.2) |
|
Germany | 10 (2) |
|
Singapore | 10 (2) |
|
France | 8 (1.6) |
|
India | 8 (1.6) |
|
Australia, Israel, and The Netherlands | 7 (1.4) |
Evidence from the nonacademic literature supports this overarching picture, with most of the identified digital innovations implemented in non-EU or EEA countries (320/497, 64.4% of digital innovations) and a smaller number of digital innovations implemented within the EU or EEA (89/497, 17.9% of digital innovations). According to the nonacademic literature, the most common implementation settings were the United States (141/497, 28.4% of digital innovations), the United Kingdom (60/497, 12.1% of digital innovations), and China (38/497, 7.6% of digital innovations), with Italy (13/497, 2.6% of digital innovations), Spain (11/497, 2.2% of digital innovations), Germany (10/497, 2% of innovations), and France (8/497, 1.6% of digital innovations) being the most common EU or EEA country implementation settings (
The digital innovations were also analyzed in terms of the types of digital technology they incorporated. As explained previously, we analyzed innovations in terms of the specific digital technologies they incorporated and the high-level technology groups to which these specific technologies belonged.
In the academic literature, the most commonly implemented high-level technology group was
In the nonacademic literature,
Number of digital innovations using each high-level technology group in the review of academic literature (left) and nonacademic literature (right; review time frame for academic literature: January 1, 2020, to September 15, 2020; review time frame for nonacademic literature: January 1, 2020, to October 13, 2020). GIS: Geographic Information Systems.
The key public health function addressed by the highest number of digital innovations in the academic literature was communication and collaboration (264/561, 47.1% of digital innovations addressed this public health function;
Number of digital innovations addressing each key public health function in the review of academic literature and nonacademic literature (review time frame for academic literature: January 1, 2020, to September 15, 2020; review time frame for nonacademic literature: January 1, 2020, to October 13, 2020)a.
Key public health function | Digital innovations, n (%) | ||
|
|||
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Communication and collaboration | 264 (47.1) | |
|
Surveillance and monitoring | 199 (35.5) | |
|
Pandemic response | 126 (22.5) | |
|
Screening and diagnostics | 103 (18.4) | |
|
Contact tracing | 77 (13.7) | |
|
Forecasting | 30 (5.3) | |
|
Signal or outbreak detection and validation | 8 (1.4) | |
|
|||
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Surveillance and monitoring | 197 (39.6) | |
|
Pandemic response | 169 (34.0) | |
|
Screening and diagnostics | 167 (33.6) | |
|
Communication and collaboration | 130 (26.2) | |
|
Contact tracing | 52 (10.5) | |
|
Forecasting | 27 (5.4) | |
|
Signal or outbreak detection and validation | 6 (1.2) |
aFor both the academic and nonacademic review, the number of digital innovations add up to more than the overall sample size (N) and the percentages add up to more than 100. This is because each digital innovation could be assigned more than one key public health function in our review.
The public health function addressed most commonly by digital innovations in the nonacademic literature was surveillance and monitoring (197/497, 39.6% of digital innovations), followed by pandemic response (169/497, 34% of digital innovations) and screening and diagnostics (167/497, 33.6% of digital innovations;
For each country in which digital innovations were implemented, we cross-analyzed the number of high-level technology groups with which implemented innovations were associated. According to the academic literature, China had implemented digital innovations across the largest number of high-level technology groups (digital innovations were implemented with technologies from 15/17, 88% of the high-level technology groups). This was followed by the United States (14/17, 82% of high-level technology groups) and the United Kingdom (13/17, 76% of high-level technology groups). The EU or EEA countries with digital innovations covering the highest number of technology groups were France (11/17, 65% of high-level technology groups) and Italy (9/17, 53% of high-level technology groups). In the nonacademic literature review, the countries implementing technologies covering the largest number of high-level technology groups were the United States (15/17, 88% of high-level technology groups), the United Kingdom (12/17, 71% of high-level technology groups), China (10/17, 59% of high-level technology groups), and Italy (10/17, 59% of high-level technology groups). Tables presenting a further analysis of the implementation setting and high-level technology groups are presented in
For each key public health function, we cross-analyzed the high-level technology groups with which digital innovations were most associated. For communication and collaboration (the public health function addressed most frequently by digital innovations in the academic literature), most digital innovations incorporated technologies within the following high-level technology groups:
Following the COVID-19 pandemic, actors worldwide turned to digital technologies to assist the public health response. This study suggests that the most common implementation settings for digital innovations implemented to tackle the COVID-19 pandemic were the United States, the United Kingdom, China, and India. Meanwhile, within the EU/EEA region, Italy, Spain, Germany, and France were the most common implementation settings. The study suggested that a high number of digital innovations implemented in response to the COVID-19 pandemic used technologies within the
To the best of our knowledge, this study is the first to provide a broad overview of the geographical context in which digital technologies have been implemented in response to the COVID-19 pandemic. Mbunge et al [
This study’s findings on the types of digital technologies used by digital innovations have commonality with the results of other reviews on digital technologies and public health in response to the COVID-19 pandemic. In their review, for example, Vargo et al [
The findings of this study also illustrate certain differences from other reviews. For example, in the study by Vargo et al [
The study’s finding that digital technologies introduced in response to the COVID-19 pandemic are principally oriented toward 4 public health functions—communication and collaboration, surveillance and monitoring, screening and diagnostics, and pandemic response—is also in line with the findings of other reviews [
In conducting this scoping review, we encountered evidence of a range of barriers to the successful implementation of digital innovation, including potential risks. In addition to the limitations of the technologies themselves, these include investment and financial barriers, infrastructural barriers (including a lack of required physical and network infrastructure), human resource barriers, data availability and quality barriers, social barriers (including low uptake and low access to technologies), ethical barriers (including privacy concerns and risks of increased socioeconomic inequality), security and safety barriers, and legal or regulatory barriers. In their review, Budd et al [
Although this study presents evidence regarding the technologies used by digital innovations, the public health functions addressed, and barriers to implementation, it has not systematically examined the performance of individual technologies or the extent to which technologies have been evaluated or comparatively assessed (see the
Therefore, the rapid proliferation of digital public health technologies in response to the COVID-19 pandemic has underscored the need for further studies to evaluate the performance of emerging digital technologies, as well as rigorous oversight mechanisms [
This study has sought to provide a broad characterization of the evidence regarding the implementation of digital innovations to tackle the COVID-19 pandemic. This study followed a systematic approach in line with the PRISMA checklist for scoping reviews. Drawing on 2 scoping reviews—a review of academic literature, supported by a supplementary, experimental review of nonacademic literature—the review considers evidence from a wide range of sources, from peer-reviewed publications to news articles, press releases, and blogs. Although the methodological approach is well suited to the objectives of the study, it is also subject to several limitations.
The first limitation of the study relates to the scope of information sources. For the review of academic literature, we relied on 2 databases, EMBASE and Scopus, supplemented by structured targeted searches using Google Scholar. Similarly, our review of nonacademic literature was also limited in that it only considered articles published by a selected set of information sources. For example, in focusing on information sources available within Feedly, the nonacademic search strategy did not include national public health institute websites (although our search strategy identified several digital innovations developed and implemented by public health institutes). We cannot rule out the possibility that running the searches in additional databases, including those used by other reviews of digital technology use for the COVID-19 pandemic, might have led us to identify further examples of digital innovations implemented in response to the COVID-19 pandemic.
We adopted broad inclusion criteria during study selection to maximize the scope of the included evidence. However, it was also necessary to limit the review’s scope to keep it manageable within the resources available for this study. Our decision to focus on
Another limitation of this scoping review was related to the categories used to code technologies and key public health functions. To extract data, we used drop-down menus to classify digital innovations by technology (specific digital technology and high-level technology groups) and by key public health function. The categories used for the drop-down menus (described earlier as key study variables) were carefully selected after several discussions between team members and drew on earlier studies [
We also faced some technical limitations in analyzing data on the types of nonacademic sources included in the review. Initially, the sources were categorized as news articles, blog posts, or press releases. However, as most sources were news articles, it was decided to merge these 3 categories into one during the analysis stage. The decision also reflected the challenges faced during the export of the included articles into a Microsoft Excel file (a measure taken to mitigate the potential risk of URL changes). With the article content exported to Microsoft Excel, it was not always possible to determine the original format (eg, whether a source was an original news article or an article based on a press release).
Finally, while reviewing evidence on the technologies used by digital innovations, the public health functions addressed, and the key barriers to implementation, a systematic evaluation of the performance of individual technologies and innovations is beyond the scope of this study. The incorporation of an evaluative aspect into the study was not feasible because of the limited amount of information on the performance of digital technologies within the reviewed sources, including the lack of evidence of formal evaluation or assessments undertaken. This study highlights the need for further evaluative studies and oversight mechanisms moving forward.
In this study, scoping reviews of academic and nonacademic sources were used to obtain an overview of the evidence regarding implemented digital innovations to tackle the COVID-19 pandemic. This scoping review sought to gain an understanding of the characteristics of the literature reporting on digital technology use for COVID-19 and an understanding of the number, nature, and geographical distribution of digital innovations implemented during the first 10 months of the COVID-19 pandemic. This study built on the evidence base established by existing reviews by incorporating new sources and approaches to analysis. This study highlighted key trends related to the implementation settings, technologies used, and public health functions addressed by COVID-19–related digital innovations. This study also identified a wide-ranging set of barriers and risks that may affect the effective implementation of digital technologies for the COVID-19 pandemic. The existence of such barriers highlights the need for contextually appropriate technological interventions. Although this study did not critically evaluate the effectiveness of digital innovations, the findings from the broader literature point to the fact that technologies introduced as part of the COVID-19 pandemic response demonstrate varying levels of performance and that, in many cases, technologies have yet to be evaluated or comparatively assessed. These findings highlight the need for further evaluation and oversight mechanisms to balance opportunities and risks.
PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) checklist.
Search terms.
Inclusion and exclusion criteria.
Extraction templates.
Cross-analysis tables.
artificial intelligence
European Union/European Economic Area
Preferred Reporting Items for Systematic Reviews and Meta-Analyses
Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews
JF, GCA, ERG, CF, KIM, HCG, and SG provided input to the development of the scoping review protocols, implementation of the scoping review, analysis of the results, and production of 2 reports on which this manuscript is based. Drafting of the manuscript was led by JF, with contributions from SG and a critical review by HCG. SG was the project leader for this study, with GCA and JF serving as project managers at different points in the project. HCG was the project owner and critically reviewed the manuscript. This study was funded by the European Centre for Disease Prevention and Control.
None declared.