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Comer L, Donelle L, Hiebert B, Smith MJ, Kothari A, Stranges S, Gilliland J, Long J, Burkell J, Shelley JJ, Hall J, Shelley J, Cooke T, Ngole Dione M, Facca D. Short- and Long-Term Predicted and Witnessed Consequences of Digital Surveillance During the COVID-19 Pandemic: Scoping Review. JMIR Public Health Surveill 2024; 10:e47154. [PMID: 38788212 DOI: 10.2196/47154] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2023] [Revised: 08/23/2023] [Accepted: 03/20/2024] [Indexed: 05/26/2024] Open
Abstract
BACKGROUND The COVID-19 pandemic has prompted the deployment of digital technologies for public health surveillance globally. The rapid development and use of these technologies have curtailed opportunities to fully consider their potential impacts (eg, for human rights, civil liberties, privacy, and marginalization of vulnerable groups). OBJECTIVE We conducted a scoping review of peer-reviewed and gray literature to identify the types and applications of digital technologies used for surveillance during the COVID-19 pandemic and the predicted and witnessed consequences of digital surveillance. METHODS Our methodology was informed by the 5-stage methodological framework to guide scoping reviews: identifying the research question; identifying relevant studies; study selection; charting the data; and collating, summarizing, and reporting the findings. We conducted a search of peer-reviewed and gray literature published between December 1, 2019, and December 31, 2020. We focused on the first year of the pandemic to provide a snapshot of the questions, concerns, findings, and discussions emerging from peer-reviewed and gray literature during this pivotal first year of the pandemic. Our review followed the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) reporting guidelines. RESULTS We reviewed a total of 147 peer-reviewed and 79 gray literature publications. Based on our analysis of these publications, we identified a total of 90 countries and regions where digital technologies were used for public health surveillance during the COVID-19 pandemic. Some of the most frequently used technologies included mobile phone apps, location-tracking technologies, drones, temperature-scanning technologies, and wearable devices. We also found that the literature raised concerns regarding the implications of digital surveillance in relation to data security and privacy, function creep and mission creep, private sector involvement in surveillance, human rights, civil liberties, and impacts on marginalized groups. Finally, we identified recommendations for ethical digital technology design and use, including proportionality, transparency, purpose limitation, protecting privacy and security, and accountability. CONCLUSIONS A wide range of digital technologies was used worldwide to support public health surveillance during the COVID-19 pandemic. The findings of our analysis highlight the importance of considering short- and long-term consequences of digital surveillance not only during the COVID-19 pandemic but also for future public health crises. These findings also demonstrate the ways in which digital surveillance has rendered visible the shifting and blurred boundaries between public health surveillance and other forms of surveillance, particularly given the ubiquitous nature of digital surveillance. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID) RR2-https://doi.org/10.1136/bmjopen-2021-053962.
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Affiliation(s)
- Leigha Comer
- Arthur Labatt Family School of Nursing, Western University, London, ON, Canada
| | - Lorie Donelle
- Arthur Labatt Family School of Nursing, Western University, London, ON, Canada
- School of Nursing, University of South Carolina, Columbia, SC, United States
| | - Bradley Hiebert
- Arthur Labatt Family School of Nursing, Western University, London, ON, Canada
| | - Maxwell J Smith
- School of Health Studies, Western University, London, ON, Canada
| | - Anita Kothari
- School of Health Studies, Western University, London, ON, Canada
| | - Saverio Stranges
- Department of Epidemiology and Biostatistics, Schulich School of Medicine & Dentistry, Western University, London, ON, Canada
- Departments of Family Medicine and Medicine, Schulich School of Medicine & Dentistry, Western University, London, ON, Canada
- The Africa Institute, Western University, London, ON, Canada
- Department of Clinical Medicine and Surgery, University of Naples Federico II, Naples, Italy
| | - Jason Gilliland
- Department of Geography and Environment, Western University, London, ON, Canada
| | - Jed Long
- Department of Geography and Environment, Western University, London, ON, Canada
| | - Jacquelyn Burkell
- Faculty of Information and Media Studies, Western University, London, ON, Canada
| | | | - Jodi Hall
- Arthur Labatt Family School of Nursing, Western University, London, ON, Canada
| | - James Shelley
- Faculty of Health Sciences, Western University, London, ON, Canada
| | - Tommy Cooke
- Surveillance Studies Centre, Queen's University, Kingston, ON, Canada
| | | | - Danica Facca
- Faculty of Information and Media Studies, Western University, London, ON, Canada
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Dias-Karunaratne N, Whop L, Ward J, Vujovich-Dunn C, Amin J, Dakiniewich A, Dyda A. Representation of marginalised populations in digital surveillance for notifiable conditions in Australia: a systematic review. Perspect Public Health 2024; 144:162-173. [PMID: 38509693 PMCID: PMC11103913 DOI: 10.1177/17579139241237101] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/22/2024]
Abstract
AIM This study aims to establish whether digital surveillance methods for notifiable diseases in Australia collect and report data in relation to marginalised populations. METHODS The literature was systematically reviewed to identify primary research studies published between January 2005 and July 2023. Studies were included if they described an Australian digital surveillance system for notifiable conditions. The results were synthesised with a focus on evaluating the collection and reporting of data in relation to marginalised populations. RESULTS A total of 13 articles reporting on seven surveillance systems were identified. Influenza and adverse events following immunisation were the two most common notifiable conditions monitored. A total of six surveillance systems encompassing 16 articles reported information on sub-populations. Of these, three surveillance systems (nine articles) included data on marginalised populations. CONCLUSION The data collected or reported in relation to sub-groups that characterise diversity in terms of health care needs, access, and marginalised populations are minimal. It is recommended that a set of equity and reporting principles is established for the future creation and use of any digital surveillance system.
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Affiliation(s)
- N Dias-Karunaratne
- School of Public Health, The University of Queensland, Brisbane, QLD, Australia
| | - L Whop
- National Centre for Aboriginal and Torres Strait Islander Wellbeing Research, Australian National University, Canberra, ACT, Australia
| | - J Ward
- Poche Centre for Indigenous Health, The University of Queensland, Brisbane, QLD, Australia
| | - C Vujovich-Dunn
- The Kirby Institute, University of New South Wales, Sydney, NSW, Australia
| | - J Amin
- Department of Health Science, Macquarie University, Sydney, NSW, Australia
| | - A Dakiniewich
- School of Public Health, The University of Queensland, Brisbane, QLD, Australia
| | - A Dyda
- School of Public Health, The University of Queensland, 288 Herston Road, Herston, Brisbane, QLD 4072, Australia
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Clark EC, Neumann S, Hopkins S, Kostopoulos A, Hagerman L, Dobbins M. Changes to Public Health Surveillance Methods Due to the COVID-19 Pandemic: Scoping Review. JMIR Public Health Surveill 2024; 10:e49185. [PMID: 38241067 PMCID: PMC10837764 DOI: 10.2196/49185] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2023] [Revised: 09/06/2023] [Accepted: 12/07/2023] [Indexed: 01/23/2024] Open
Abstract
BACKGROUND Public health surveillance plays a vital role in informing public health decision-making. The onset of the COVID-19 pandemic in early 2020 caused a widespread shift in public health priorities. Global efforts focused on COVID-19 monitoring and contact tracing. Existing public health programs were interrupted due to physical distancing measures and reallocation of resources. The onset of the COVID-19 pandemic intersected with advancements in technologies that have the potential to support public health surveillance efforts. OBJECTIVE This scoping review aims to explore emergent public health surveillance methods during the early COVID-19 pandemic to characterize the impact of the pandemic on surveillance methods. METHODS A scoping search was conducted in multiple databases and by scanning key government and public health organization websites from March 2020 to January 2022. Published papers and gray literature that described the application of new or revised approaches to public health surveillance were included. Papers that discussed the implications of novel public health surveillance approaches from ethical, legal, security, and equity perspectives were also included. The surveillance subject, method, location, and setting were extracted from each paper to identify trends in surveillance practices. Two public health epidemiologists were invited to provide their perspectives as peer reviewers. RESULTS Of the 14,238 unique papers, a total of 241 papers describing novel surveillance methods and changes to surveillance methods are included. Eighty papers were review papers and 161 were single studies. Overall, the literature heavily featured papers detailing surveillance of COVID-19 transmission (n=187). Surveillance of other infectious diseases was also described, including other pathogens (n=12). Other public health topics included vaccines (n=9), mental health (n=11), substance use (n=4), healthy nutrition (n=1), maternal and child health (n=3), antimicrobial resistance (n=2), and misinformation (n=6). The literature was dominated by applications of digital surveillance, for example, by using big data through mobility tracking and infodemiology (n=163). Wastewater surveillance was also heavily represented (n=48). Other papers described adaptations to programs or methods that existed prior to the COVID-19 pandemic (n=9). The scoping search also found 109 papers that discuss the ethical, legal, security, and equity implications of emerging surveillance methods. The peer reviewer public health epidemiologists noted that additional changes likely exist, beyond what has been reported and available for evidence syntheses. CONCLUSIONS The COVID-19 pandemic accelerated advancements in surveillance and the adoption of new technologies, especially for digital and wastewater surveillance methods. Given the investments in these systems, further applications for public health surveillance are likely. The literature for surveillance methods was dominated by surveillance of infectious diseases, particularly COVID-19. A substantial amount of literature on the ethical, legal, security, and equity implications of these emerging surveillance methods also points to a need for cautious consideration of potential harm.
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Affiliation(s)
- Emily C Clark
- National Collaborating Centre for Methods and Tools, Hamilton, ON, Canada
| | - Sophie Neumann
- National Collaborating Centre for Methods and Tools, Hamilton, ON, Canada
| | - Stephanie Hopkins
- National Collaborating Centre for Methods and Tools, Hamilton, ON, Canada
| | - Alyssa Kostopoulos
- National Collaborating Centre for Methods and Tools, Hamilton, ON, Canada
| | - Leah Hagerman
- National Collaborating Centre for Methods and Tools, Hamilton, ON, Canada
| | - Maureen Dobbins
- National Collaborating Centre for Methods and Tools, Hamilton, ON, Canada
- School of Nursing, McMaster University, Hamilton, ON, Canada
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Pan X, Hounye AH, Zhao Y, Cao C, Wang J, Abidi MV, Hou M, Xiong L, Chai X. A Digital Mask-Voiceprint System for Postpandemic Surveillance and Tracing Based on the STRONG Strategy. J Med Internet Res 2023; 25:e44795. [PMID: 37856760 PMCID: PMC10660213 DOI: 10.2196/44795] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2022] [Revised: 09/28/2023] [Accepted: 10/18/2023] [Indexed: 10/21/2023] Open
Abstract
Lockdowns and border closures due to COVID-19 imposed mental, social, and financial hardships in many societies. Living with the virus and resuming normal life are increasingly being advocated due to decreasing virus severity and widespread vaccine coverage. However, current trends indicate a continued absence of effective contingency plans to stop the next more virulent variant of the pandemic. The COVID-19-related mask waste crisis has also caused serious environmental problems and virus spreads. It is timely and important to consider how to precisely implement surveillance for the dynamic clearance of COVID-19 and how to efficiently manage discarded masks to minimize disease transmission and environmental hazards. In this viewpoint, we sought to address this issue by proposing an appropriate strategy for intelligent surveillance of infected cases and centralized management of mask waste. Such an intelligent strategy against COVID-19, consisting of wearable mask sample collectors (masklect) and voiceprints and based on the STRONG (Spatiotemporal Reporting Over Network and GPS) strategy, could enable the resumption of social activities and economic recovery and ensure a safe public health environment sustainably.
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Affiliation(s)
- Xiaogao Pan
- Department of Emergency Medicine, Second Xiangya Hospital, Central South University, Changsha, China
- Emergency Medicine and Difficult Diseases Institute, Central South University, Changsha, China
| | | | - Yuqi Zhao
- Department of Gastroenterology, Second Xiangya Hospital, Central South University, Changsha, China
| | - Cong Cao
- School of Mathematics and Statistics, Central South University, Changsha, China
| | - Jiaoju Wang
- School of Mathematics and Statistics, Central South University, Changsha, China
| | - Mimi Venunye Abidi
- General Surgery Department, Second Xiangya Hospital, Central South University, Changsha, China
| | - Muzhou Hou
- School of Mathematics and Statistics, Central South University, Changsha, China
| | - Li Xiong
- General Surgery Department, Second Xiangya Hospital, Central South University, Changsha, China
| | - Xiangping Chai
- Department of Emergency Medicine, Second Xiangya Hospital, Central South University, Changsha, China
- Emergency Medicine and Difficult Diseases Institute, Central South University, Changsha, China
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OWUSU ISAAC, ACHEAMPONG GIDEONKWARTENG, AKYEREKO ERNEST, AGYEI NIIARYEETEY, ASHONG MAWUFEMOR, AMOFA ISAAC, MPANGAH REBECCAANN, KENU ERNEST, ABOAGYE RICHARDGYAN, ADU COLLINS, AGYEMANG KINGSLEY, NSIAH-ASARE ANTHONY, ASIEDU-BEKOE FRANKLIN. The role of digital surveillance during outbreaks: the Ghana experience from COVID-19 response. J Public Health Afr 2023; 14:2755. [PMID: 38020270 PMCID: PMC10658462 DOI: 10.4081/jphia.2023.2755] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Accepted: 08/30/2023] [Indexed: 12/01/2023] Open
Abstract
Over the years, Ghana has made notable strides in adopting digital approaches to address societal challenges and meet demands. While the health sector, particularly the disease surveillance structure, has embraced digitization to enhance case detection, reporting, analysis, and information dissemination, critical aspects remain to be addressed. Although the Integrated Disease Surveillance and Response (IDSR) structure has experienced remarkable growth in digitization, certain areas require further attention as was observed during the COVID-19 pandemic. Ghana during the COVID-19 pandemic, recognized the importance of leveraging digital technologies to bolster the public health response. To this end, Ghana implemented various digital surveillance tools to combat the pandemic. These included the 'Surveillance Outbreak Response Management and Analysis System (SORMAS)', the digitalized health declaration form, ArcGIS Survey123, Talkwalker, 'Lightwave Health information Management System' (LHIMS), and the 'District Health Information Management System (DHIMS)'. These digital systems significantly contributed to the country's success in responding to the COVID-19 pandemic. One key area where digital systems have proved invaluable is in the timely production of daily COVID-19 situational updates. This task would have been arduous and delayed if reliant solely on paper-based forms, which hinder efficient reporting to other levels within the health system. By adopting these digital systems, Ghana has been able to overcome such challenges and provide up-to-date information for making informed public health decisions. This paper attempts to provide an extensive description of the digital systems currently employed to enhance Ghana's paper-based disease surveillance system in the context of its response to COVID-19. The article explores the strengths and challenges or limitations associated with these digital systems for responding to outbreaks, offering valuable lessons that can be learned from their implementation.
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Affiliation(s)
| | | | - ERNEST AKYEREKO
- Ghana Health Service, Headquarters
- Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, Enschede, The Netherlands
| | | | | | | | | | - ERNEST KENU
- Ghana Field Epidemiology and Laboratory Training Program, School of Public Health, University of Ghana
| | - RICHARD GYAN ABOAGYE
- Department of Family and Community Health, Fred N. Binka School of Public Health, University of Health and Allied Sciences, Hohoe, Ghana
| | - COLLINS ADU
- College of Public Health, Medical and Veterinary Sciences, James Cook University, Townsville, Queensland, Australia
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Comer L, Donelle L, Ngole M, Shelley JJ, Kothari A, Smith M, Shelley JM, Stranges S, Hiebert B, Gilliland J, Burkell J, Cooke T, Hall J, Long J. An investigation of media reports of digital surveillance within the first year of the COVID-19 pandemic. Front Digit Health 2023; 5:1215685. [PMID: 37564881 PMCID: PMC10411532 DOI: 10.3389/fdgth.2023.1215685] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2023] [Accepted: 07/10/2023] [Indexed: 08/12/2023] Open
Abstract
Introduction The COVID-19 pandemic prompted a surge in digital public health surveillance worldwide, with limited opportunities to consider the effectiveness or impact of digital surveillance. The news media shape public understanding of topics of importance, contributing to our perception of priority issues. This study investigated news media reports published during the first year of the pandemic to understand how the use and consequences of digital surveillance technologies were reported on. Methods A media content analysis of 34 high- to low-income countries was completed. The terms "COVID-19," "surveillance," "technologies," and "public health" were used to retrieve and inductively code media reports. Results Of the 1,001 reports, most were web-based or newspaper sources on the development and deployment of technologies directed at contact tracing, enforcing quarantine, predicting disease spread, and allocating resources. Technology types included mobile apps, wearable devices, "smart" thermometers, GPS/Bluetooth, facial recognition, and security cameras. Repurposed data from social media, travel cards/passports, and consumer purchases also provided surveillance insight. Media reports focused on factors impacting surveillance success (public participation and data validity) and the emerging consequences of digital surveillance on human rights, function creep, data security, and trust. Discussion Diverse digital technologies were developed and used for public health surveillance during the first year of the COVID-19 pandemic. The use of these technologies and witnessed or anticipated consequences were reported by a variety of media sources worldwide. The news media are an important public health information resource, as media outlets contribute to directing public understanding and shaping priority public health surveillance issues. Our findings raise important questions around how journalists decide which aspects of public health crises to report on and how these issues are discussed.
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Affiliation(s)
- Leigha Comer
- Arthur Labatt Family School of Nursing, Western University, London, ON, Canada
| | - Lorie Donelle
- Arthur Labatt Family School of Nursing, Western University, London, ON, Canada
- College of Nursing, University of South Carolina, Columbia, SC, United States
| | - Marionette Ngole
- Arthur Labatt Family School of Nursing, Western University, London, ON, Canada
| | | | - Anita Kothari
- School of Health Studies, Western University, London, ON, Canada
| | - Maxwell Smith
- School of Health Studies, Western University, London, ON, Canada
| | - James M. Shelley
- Faculty of Health Sciences, Western University, London, ON, Canada
| | - Saverio Stranges
- Department of Epidemiology and Biostatistics, Schulich School of Medicine and Dentistry, Western University, London, ON, Canada
- Departments of Family Medicine and Medicine, Schulich School of Medicine and Dentistry, Western University, London, ON, Canada
- The Africa Institute, Western University, London, ON, Canada
- Department of Precision Health, Luxembourg Institute of Health, Strassen, Luxembourg
| | - Brad Hiebert
- Arthur Labatt Family School of Nursing, Western University, London, ON, Canada
| | - Jason Gilliland
- Department of Geography and Environment, Western University, London, ON, Canada
| | - Jacquelyn Burkell
- Faculty of Information and Media Studies, Western University, London, ON, Canada
| | - Tommy Cooke
- Surveillance Studies Centre, Queen’s University, Kingston, ON, Canada
| | - Jodi Hall
- School of Nursing, Fanshawe College, London, ON, Canada
| | - Jed Long
- Department of Geography and Environment, Western University, London, ON, Canada
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Tian Y, Zhang W, Duan L, McDonald W, Osgood N. Comparison of pretrained transformer-based models for influenza and COVID-19 detection using social media text data in Saskatchewan, Canada. Front Digit Health 2023; 5:1203874. [PMID: 37448834 PMCID: PMC10338115 DOI: 10.3389/fdgth.2023.1203874] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2023] [Accepted: 06/02/2023] [Indexed: 07/15/2023] Open
Abstract
Background The use of social media data provides an opportunity to complement traditional influenza and COVID-19 surveillance methods for the detection and control of outbreaks and informing public health interventions. Objective The first aim of this study is to investigate the degree to which Twitter users disclose health experiences related to influenza and COVID-19 that could be indicative of recent plausible influenza cases or symptomatic COVID-19 infections. Second, we seek to use the Twitter datasets to train and evaluate the classification performance of Bidirectional Encoder Representations from Transformers (BERT) and variant language models in the context of influenza and COVID-19 infection detection. Methods We constructed two Twitter datasets using a keyword-based filtering approach on English-language tweets collected from December 2016 to December 2022 in Saskatchewan, Canada. The influenza-related dataset comprised tweets filtered with influenza-related keywords from December 13, 2016, to March 17, 2018, while the COVID-19 dataset comprised tweets filtered with COVID-19 symptom-related keywords from January 1, 2020, to June 22, 2021. The Twitter datasets were cleaned, and each tweet was annotated by at least two annotators as to whether it suggested recent plausible influenza cases or symptomatic COVID-19 cases. We then assessed the classification performance of pre-trained transformer-based language models, including BERT-base, BERT-large, RoBERTa-base, RoBERT-large, BERTweet-base, BERTweet-covid-base, BERTweet-large, and COVID-Twitter-BERT (CT-BERT) models, on each dataset. To address the notable class imbalance, we experimented with both oversampling and undersampling methods. Results The influenza dataset had 1129 out of 6444 (17.5%) tweets annotated as suggesting recent plausible influenza cases. The COVID-19 dataset had 924 out of 11939 (7.7%) tweets annotated as inferring recent plausible COVID-19 cases. When compared against other language models on the COVID-19 dataset, CT-BERT performed the best, supporting the highest scores for recall (94.8%), F1(94.4%), and accuracy (94.6%). For the influenza dataset, BERTweet models exhibited better performance. Our results also showed that applying data balancing techniques such as oversampling or undersampling method did not lead to improved model performance. Conclusions Utilizing domain-specific language models for monitoring users' health experiences related to influenza and COVID-19 on social media shows improved classification performance and has the potential to supplement real-time disease surveillance.
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Niesen S, Ramon D, Spencer-Hwang R, Sinclair R. The Relationship Between Face Mask Use and Face-Touching Frequency in Public Areas: Naturalistic Study. Interact J Med Res 2023; 12:e43308. [PMID: 37094229 PMCID: PMC10262021 DOI: 10.2196/43308] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2022] [Revised: 02/17/2023] [Accepted: 04/03/2023] [Indexed: 04/05/2023] Open
Abstract
BACKGROUND Throughout the COVID-19 pandemic in the United States, a major public health goal has been reducing the spread of the virus, with particular emphasis on reducing transmission from person to person. Frequent face touching can transmit viral particles from one infected person and subsequently infect others in a public area. This raises an important concern about the use of face masks and their relationship with face-touching behaviors. One concern discussed during the pandemic is that wearing a mask, and different types of masks, could increase face touching because there is a need to remove the mask to smoke, drink, eat, etc. To date, there have been few studies that have assessed this relationship between mask wearing and the frequency of face touching relative to face-touching behaviors. OBJECTIVE This study aimed to compare the frequency of face touching in people wearing a mask versus not wearing a mask in high-foot traffic urban outdoor areas. The purpose of this study was to assess if mask wearing was associated with increased face touching. METHODS Public webcam videos from 4 different cities in New York, New Jersey, Louisiana, and Florida were used to collect data. Face touches were recorded as pedestrians passed under the webcam. Adult pedestrians wearing masks were compared to those not wearing masks. Quantitative measures of frequency, duration, site of touch, and oral activities were recorded. Linear regression analysis was used to assess the association between mask use and face touching. RESULTS Of the 490 observed subjects, 241 (49.2%) were wearing a mask properly and 249 (50.8%) were not. In the unmasked group, 33.7% (84/249) were wearing it improperly, covering the mouth only. Face touching occurred in 11.4% (56/490) of the masked group and 17.6% (88/490) in the unmasked group. Of those who touched their face, 61.1% (88/144) of people were not wearing a mask. The most common site of face touching was the perioral region in both groups. Both the masked and unmasked group had a frequency of face touching for 0.03 touches/s. Oral activities such as eating or smoking increased face touching in the unmasked group. CONCLUSIONS Contrary to expectations, non-mask-wearing subjects touched their face more frequently than those who were wearing a mask. This finding is substantial because wearing a face mask had a negative association with face touching. When wearing a mask, individuals are less likely to be spreading and ingesting viral particles. Therefore, wearing a mask is more effective in preventing the spread of viral particles.
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Affiliation(s)
- Sydney Niesen
- San Diego State University, San Diego, CA, United States
| | - Daniel Ramon
- Loma Linda University, Loma Linda, CA, United States
| | | | - Ryan Sinclair
- Loma Linda University School of Public Health, Loma Linda, CA, United States
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Shausan A, Nazarathy Y, Dyda A. Emerging data inputs for infectious diseases surveillance and decision making. Front Digit Health 2023; 5:1131731. [PMID: 37082524 PMCID: PMC10111015 DOI: 10.3389/fdgth.2023.1131731] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2022] [Accepted: 03/20/2023] [Indexed: 04/07/2023] Open
Abstract
Infectious diseases create a significant health and social burden globally and can lead to outbreaks and epidemics. Timely surveillance for infectious diseases is required to inform both short and long term public responses and health policies. Novel data inputs for infectious disease surveillance and public health decision making are emerging, accelerated by the COVID-19 pandemic. These include the use of technology-enabled physiological measurements, crowd sourcing, field experiments, and artificial intelligence (AI). These technologies may provide benefits in relation to improved timeliness and reduced resource requirements in comparison to traditional methods. In this review paper, we describe current and emerging data inputs being used for infectious disease surveillance and summarize key benefits and limitations.
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Affiliation(s)
- Aminath Shausan
- School of Public Health, The University of Queensland, Brisbane, QLD, Australia
- School of Mathematics and Physics, The University of Queensland, Brisbane, QLD, Australia
| | - Yoni Nazarathy
- School of Mathematics and Physics, The University of Queensland, Brisbane, QLD, Australia
| | - Amalie Dyda
- School of Public Health, The University of Queensland, Brisbane, QLD, Australia
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Ueda M, Watanabe K, Sueki H. Emotional Distress During COVID-19 due to Mental Health Conditions and Economic Vulnerability: Retrospective Analysis of Survey-Linked Twitter Data With a Semisupervised Machine Learning Algorithm. J Med Internet Res 2023; 25:e44965. [PMID: 36809798 PMCID: PMC10022650 DOI: 10.2196/44965] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2022] [Revised: 01/30/2023] [Accepted: 02/21/2023] [Indexed: 02/23/2023] Open
Abstract
BACKGROUND Monitoring the psychological conditions of social media users during rapidly developing public health crises, such as the COVID-19 pandemic, using their posts on social media has rapidly gained popularity as a relatively easy and cost-effective method. However, the characteristics of individuals who created these posts are largely unknown, making it difficult to identify groups of individuals most affected by such crises. In addition, large annotated data sets for mental health conditions are not easily available, and thus, supervised machine learning algorithms can be infeasible or too costly. OBJECTIVE This study proposes a machine learning framework for the real-time surveillance of mental health conditions that does not require extensive training data. Using survey-linked tweets, we tracked the level of emotional distress during the COVID-19 pandemic by the attributes and psychological conditions of social media users in Japan. METHODS We conducted online surveys of adults residing in Japan in May 2022 and collected their basic demographic information, socioeconomic status, and mental health conditions, along with their Twitter handles (N=2432). We computed emotional distress scores for all the tweets posted by the study participants between January 1, 2019, and May 30, 2022 (N=2,493,682) using a semisupervised algorithm called latent semantic scaling (LSS), with higher values indicating higher levels of emotional distress. After excluding users by age and other criteria, we examined 495,021 (19.85%) tweets generated by 560 (23.03%) individuals (age 18-49 years) in 2019 and 2020. We estimated fixed-effect regression models to examine their emotional distress levels in 2020 relative to the corresponding weeks in 2019 by the mental health conditions and characteristics of social media users. RESULTS The estimated level of emotional distress of our study participants increased in the week when school closure started (March 2020), and it peaked at the beginning of the state of emergency (estimated coefficient=0.219, 95% CI 0.162-0.276) in early April 2020. Their level of emotional distress was unrelated to the number of COVID-19 cases. We found that the government-induced restrictions disproportionately affected the psychological conditions of vulnerable individuals, including those with low income, precarious employment, depressive symptoms, and suicidal ideation. CONCLUSIONS This study establishes a framework to implement near-real-time monitoring of the emotional distress level of social media users, highlighting a great potential to continuously monitor their well-being using survey-linked social media posts as a complement to administrative and large-scale survey data. Given its flexibility and adaptability, the proposed framework is easily extendable for other purposes, such as detecting suicidality among social media users, and can be used on streaming data for continuous measurement of the conditions and sentiment of any group of interest.
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Affiliation(s)
- Michiko Ueda
- Department of Public Administration and International Affairs, The Maxwell School of Citizenship and Public Affairs, Syracuse University, Syracuse, NY, United States.,Center for Policy Research, The Maxwell School of Citizenship and Public Affairs, Syracuse University, Syracuse, NY, United States
| | - Kohei Watanabe
- Waseda Institute for Advanced Study, Waseda University, Tokyo, Japan
| | - Hajime Sueki
- Faculty of Human Sciences, Wako University, Tokyo, Japan
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Chong KC, Li K, Guo Z, Jia KM, Leung EYM, Zhao S, Hung CT, Yam CHK, Chow TY, Dong D, Wang H, Wei Y, Yeoh EK. Dining-Out Behavior as a Proxy for the Superspreading Potential of SARS-CoV-2 Infections: Modeling Analysis. JMIR Public Health Surveill 2023; 9:e44251. [PMID: 36811849 PMCID: PMC9994464 DOI: 10.2196/44251] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2022] [Revised: 02/01/2023] [Accepted: 02/14/2023] [Indexed: 02/24/2023] Open
Abstract
BACKGROUND While many studies evaluated the reliability of digital mobility metrics as a proxy of SARS-CoV-2 transmission potential, none examined the relationship between dining-out behavior and the superspreading potential of COVID-19. OBJECTIVE We employed the mobility proxy of dining out in eateries to examine this association in Hong Kong with COVID-19 outbreaks highly characterized by superspreading events. METHODS We retrieved the illness onset date and contact-tracing history of all laboratory-confirmed cases of COVID-19 from February 16, 2020, to April 30, 2021. We estimated the time-varying reproduction number (Rt) and dispersion parameter (k), a measure of superspreading potential, and related them to the mobility proxy of dining out in eateries. We compared the relative contribution to the superspreading potential with other common proxies derived by Google LLC and Apple Inc. RESULTS A total of 6391 clusters involving 8375 cases were used in the estimation. A high correlation between dining-out mobility and superspreading potential was observed. Compared to other mobility proxies derived by Google and Apple, the mobility of dining-out behavior explained the highest variability of k (ΔR-sq=9.7%, 95% credible interval: 5.7% to 13.2%) and Rt (ΔR-sq=15.7%, 95% credible interval: 13.6% to 17.7%). CONCLUSIONS We demonstrated that there was a strong link between dining-out behaviors and the superspreading potential of COVID-19. The methodological innovation suggests a further development using digital mobility proxies of dining-out patterns to generate early warnings of superspreading events.
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Affiliation(s)
- Ka Chun Chong
- Centre for Health Systems and Policy Research, Jockey Club School of Public Health and Primary Care, The Chinese University of Hong Kong, New Territories, Hong Kong
| | - Kehang Li
- Centre for Health Systems and Policy Research, Jockey Club School of Public Health and Primary Care, The Chinese University of Hong Kong, New Territories, Hong Kong
| | - Zihao Guo
- Centre for Health Systems and Policy Research, Jockey Club School of Public Health and Primary Care, The Chinese University of Hong Kong, New Territories, Hong Kong
| | - Katherine Min Jia
- Center for Communicable Disease Dynamics, Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, United States
| | - Eman Yee Man Leung
- Centre for Health Systems and Policy Research, Jockey Club School of Public Health and Primary Care, The Chinese University of Hong Kong, New Territories, Hong Kong
| | - Shi Zhao
- Centre for Health Systems and Policy Research, Jockey Club School of Public Health and Primary Care, The Chinese University of Hong Kong, New Territories, Hong Kong
| | - Chi Tim Hung
- Centre for Health Systems and Policy Research, Jockey Club School of Public Health and Primary Care, The Chinese University of Hong Kong, New Territories, Hong Kong
| | - Carrie Ho Kwan Yam
- Centre for Health Systems and Policy Research, Jockey Club School of Public Health and Primary Care, The Chinese University of Hong Kong, New Territories, Hong Kong
| | - Tsz Yu Chow
- Centre for Health Systems and Policy Research, Jockey Club School of Public Health and Primary Care, The Chinese University of Hong Kong, New Territories, Hong Kong
| | - Dong Dong
- Centre for Health Systems and Policy Research, Jockey Club School of Public Health and Primary Care, The Chinese University of Hong Kong, New Territories, Hong Kong
| | - Huwen Wang
- Centre for Health Systems and Policy Research, Jockey Club School of Public Health and Primary Care, The Chinese University of Hong Kong, New Territories, Hong Kong
| | - Yuchen Wei
- Centre for Health Systems and Policy Research, Jockey Club School of Public Health and Primary Care, The Chinese University of Hong Kong, New Territories, Hong Kong
| | - Eng Kiong Yeoh
- Centre for Health Systems and Policy Research, Jockey Club School of Public Health and Primary Care, The Chinese University of Hong Kong, New Territories, Hong Kong
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MacIntyre CR, Chen X, Kunasekaran M, Quigley A, Lim S, Stone H, Paik HY, Yao L, Heslop D, Wei W, Sarmiento I, Gurdasani D. Artificial intelligence in public health: the potential of epidemic early warning systems. J Int Med Res 2023; 51:3000605231159335. [PMID: 36967669 PMCID: PMC10052500 DOI: 10.1177/03000605231159335] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/29/2023] Open
Abstract
The use of artificial intelligence (AI) to generate automated early warnings in epidemic surveillance by harnessing vast open-source data with minimal human intervention has the potential to be both revolutionary and highly sustainable. AI can overcome the challenges faced by weak health systems by detecting epidemic signals much earlier than traditional surveillance. AI-based digital surveillance is an adjunct to-not a replacement of-traditional surveillance and can trigger early investigation, diagnostics and responses at the regional level. This narrative review focuses on the role of AI in epidemic surveillance and summarises several current epidemic intelligence systems including ProMED-mail, HealthMap, Epidemic Intelligence from Open Sources, BlueDot, Metabiota, the Global Biosurveillance Portal, Epitweetr and EPIWATCH. Not all of these systems are AI-based, and some are only accessible to paid users. Most systems have large volumes of unfiltered data; only a few can sort and filter data to provide users with curated intelligence. However, uptake of these systems by public health authorities, who have been slower to embrace AI than their clinical counterparts, is low. The widespread adoption of digital open-source surveillance and AI technology is needed for the prevention of serious epidemics.
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Affiliation(s)
- Chandini Raina MacIntyre
- Biosecurity Program, The Kirby Institute, Faculty of Medicine, University of New South Wales, Sydney, Australia
- College of Public Service & Community Solutions, Arizona State University, Tempe, United States
| | - Xin Chen
- Biosecurity Program, The Kirby Institute, Faculty of Medicine, University of New South Wales, Sydney, Australia
| | - Mohana Kunasekaran
- Biosecurity Program, The Kirby Institute, Faculty of Medicine, University of New South Wales, Sydney, Australia
| | - Ashley Quigley
- Biosecurity Program, The Kirby Institute, Faculty of Medicine, University of New South Wales, Sydney, Australia
| | - Samsung Lim
- Biosecurity Program, The Kirby Institute, Faculty of Medicine, University of New South Wales, Sydney, Australia
- School of Civil and Environmental Engineering, University of New South Wales, Sydney, Australia
| | - Haley Stone
- Biosecurity Program, The Kirby Institute, Faculty of Medicine, University of New South Wales, Sydney, Australia
| | - Hye-Young Paik
- School of Computer Science and Engineering, Faulty of Engineering, University of New South Wales, Sydney, Australia
| | - Lina Yao
- School of Computer Science and Engineering, Faulty of Engineering, University of New South Wales, Sydney, Australia
| | - David Heslop
- School of Population Health, Faculty of Medicine, University of New South Wales, Sydney, Australia
| | - Wenzhao Wei
- Biosecurity Program, The Kirby Institute, Faculty of Medicine, University of New South Wales, Sydney, Australia
| | - Ines Sarmiento
- Biosecurity Program, The Kirby Institute, Faculty of Medicine, University of New South Wales, Sydney, Australia
| | - Deepti Gurdasani
- William Harvey Research Institute, Queen Mary University of London, United Kingdom
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Majmundar A, Pérez C, Huerta M, Unger JB, Allem JP. Describing Memes Referencing Vaping: Thematic Analysis. Subst Use Misuse 2022; 58:306-310. [PMID: 36585016 DOI: 10.1080/10826084.2022.2161316] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Abstract
Background: Memes, images or videos with text overlay that embody a concept or belief about the contemporary society, are endemic to Internet culture, are popular among youth and diffuse rapidly across social media platforms. E-cigarettes and vaping have grown in popularity in the era of Internet culture however there is little research describing the intersection of memes and vaping. This is an important gap in the literature as memes may be part of the broader online e-cigarette information landscape that can normalize vaping among young people. Memes could also point to emerging trends in product preferences. This study content analyzed memes to identify key themes, characters and vape products depicted therein. Methods: Data were drawn from a sub-reddit devoted to vaping-related memes. Memes were electronically copied from the forum to analyze (n = 527). Using an inductive approach, the research team identified 14 themes. Results: In-group communication (n = 202, 38.33%) was the most predominant theme followed by Critique of vaping regulations and public perceptions (n = 76, 14.42%), and Vape device modifications and hacks (n = 62, 11.76%). Memes included Cartoons (n = 124, 23.53%), Celebrities (n = 75, 14.23%), and Fictional characters (n = 53, 10.06%). Memes referenced Tanks or mods (n = 120, 22.77%), Component parts (n = 96, 18.22%) and E-liquids/Nicotine salts (n = 81, 15.37%). Conclusion: Memes referenced in-group communication and cartoons among other youth friendly images, raising concern about the potential to normalize vaping-related behaviors. Future research should monitor emerging vape devices and determine the impact of memes on attitudes and behaviors among adolescents and young adults. Implications: Given the popularity and reach of memes among youth, continuous monitoring of vaping-related memes may reveal aspects that may be addressed in vaping prevention campaigns.
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Affiliation(s)
- Anuja Majmundar
- Surveillance and Health Equity Science, American Cancer Society, Inc., Kennesaw, Georgia, USA
| | - Cindy Pérez
- Keck School of Medicine of USC, Los Angeles, California, USA
| | - Marlene Huerta
- Keck School of Medicine of USC, Los Angeles, California, USA
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Leston M, Elson WH, Watson C, Lakhani A, Aspden C, Bankhead CR, Borrow R, Button E, Byford R, Elliot AJ, Fan X, Hoang U, Linley E, Macartney J, Nicholson BD, Okusi C, Ramsay M, Smith G, Smith S, Thomas M, Todkill D, Tsang RS, Victor W, Williams AJ, Williams J, Zambon M, Howsam G, Amirthalingam G, Lopez-Bernal J, Hobbs FDR, de Lusignan S. Representativeness, Vaccination Uptake, and COVID-19 Clinical Outcomes 2020-2021 in the UK Oxford-Royal College of General Practitioners Research and Surveillance Network: Cohort Profile Summary. JMIR Public Health Surveill 2022; 8:e39141. [PMID: 36534462 PMCID: PMC9770023 DOI: 10.2196/39141] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2022] [Revised: 10/27/2022] [Accepted: 11/02/2022] [Indexed: 11/05/2022] Open
Abstract
BACKGROUND The Oxford-Royal College of General Practitioners (RCGP) Research and Surveillance Centre (RSC) is one of Europe's oldest sentinel systems, working with the UK Health Security Agency (UKHSA) and its predecessor bodies for 55 years. Its surveillance report now runs twice weekly, supplemented by online observatories. In addition to conducting sentinel surveillance from a nationally representative group of practices, the RSC is now also providing data for syndromic surveillance. OBJECTIVE The aim of this study was to describe the cohort profile at the start of the 2021-2022 surveillance season and recent changes to our surveillance practice. METHODS The RSC's pseudonymized primary care data, linked to hospital and other data, are held in the Oxford-RCGP Clinical Informatics Digital Hub, a Trusted Research Environment. We describe the RSC's cohort profile as of September 2021, divided into a Primary Care Sentinel Cohort (PCSC)-collecting virological and serological specimens-and a larger group of syndromic surveillance general practices (SSGPs). We report changes to our sampling strategy that brings the RSC into alignment with European Centre for Disease Control guidance and then compare our cohort's sociodemographic characteristics with Office for National Statistics data. We further describe influenza and COVID-19 vaccine coverage for the 2020-2021 season (week 40 of 2020 to week 39 of 2021), with the latter differentiated by vaccine brand. Finally, we report COVID-19-related outcomes in terms of hospitalization, intensive care unit (ICU) admission, and death. RESULTS As a response to COVID-19, the RSC grew from just over 500 PCSC practices in 2019 to 1879 practices in 2021 (PCSC, n=938; SSGP, n=1203). This represents 28.6% of English general practices and 30.59% (17,299,780/56,550,136) of the population. In the reporting period, the PCSC collected >8000 virology and >23,000 serology samples. The RSC population was broadly representative of the national population in terms of age, gender, ethnicity, National Health Service Region, socioeconomic status, obesity, and smoking habit. The RSC captured vaccine coverage data for influenza (n=5.4 million) and COVID-19, reporting dose one (n=11.9 million), two (n=11 million), and three (n=0.4 million) for the latter as well as brand-specific uptake data (AstraZeneca vaccine, n=11.6 million; Pfizer, n=10.8 million; and Moderna, n=0.7 million). The median (IQR) number of COVID-19 hospitalizations and ICU admissions was 1181 (559-1559) and 115 (50-174) per week, respectively. CONCLUSIONS The RSC is broadly representative of the national population; its PCSC is geographically representative and its SSGPs are newly supporting UKHSA syndromic surveillance efforts. The network captures vaccine coverage and has expanded from reporting primary care attendances to providing data on onward hospital outcomes and deaths. The challenge remains to increase virological and serological sampling to monitor the effectiveness and waning of all vaccines available in a timely manner.
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Affiliation(s)
- Meredith Leston
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - William H Elson
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Conall Watson
- Immunisation and Vaccine Preventable Diseases Division, UK Health Security Agency, Colindale, London, United Kingdom
| | - Anissa Lakhani
- Immunisation and Vaccine Preventable Diseases Division, UK Health Security Agency, Colindale, London, United Kingdom
| | - Carole Aspden
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Clare R Bankhead
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Ray Borrow
- Vaccine Evaluation Unit, UK Health Security Agency, Manchester Royal Infirmary, Manchester, United Kingdom
| | - Elizabeth Button
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Rachel Byford
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Alex J Elliot
- Real-time Syndromic Surveillance Team, Field Service, UK Health Security Agency, Birmingham, United Kingdom
| | - Xuejuan Fan
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Uy Hoang
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Ezra Linley
- Vaccine Evaluation Unit, UK Health Security Agency, Manchester Royal Infirmary, Manchester, United Kingdom
| | - Jack Macartney
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Brian D Nicholson
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Cecilia Okusi
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Mary Ramsay
- Immunisation and Vaccine Preventable Diseases Division, UK Health Security Agency, Colindale, London, United Kingdom
| | - Gillian Smith
- Real-time Syndromic Surveillance Team, Field Service, UK Health Security Agency, Birmingham, United Kingdom
| | - Sue Smith
- Real-time Syndromic Surveillance Team, Field Service, UK Health Security Agency, Birmingham, United Kingdom
| | - Mark Thomas
- Royal College of General Practitioners, London, United Kingdom
| | - Dan Todkill
- Real-time Syndromic Surveillance Team, Field Service, UK Health Security Agency, Birmingham, United Kingdom
| | - Ruby Sm Tsang
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - William Victor
- Royal College of General Practitioners, London, United Kingdom
| | - Alice J Williams
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - John Williams
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Maria Zambon
- Reference Microbiology, UK Health Security Agency, Colindale, London, United Kingdom
| | - Gary Howsam
- Royal College of General Practitioners, London, United Kingdom
| | - Gayatri Amirthalingam
- Immunisation and Vaccine Preventable Diseases Division, UK Health Security Agency, Colindale, London, United Kingdom
| | - Jamie Lopez-Bernal
- Immunisation and Vaccine Preventable Diseases Division, UK Health Security Agency, Colindale, London, United Kingdom
| | - F D Richard Hobbs
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Simon de Lusignan
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
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Dehesh P, Baradaran HR, Eshrati B, Motevalian SA, Salehi M, Donyavi T. The Relationship Between Population-Level SARS-CoV-2 Cycle Threshold Values and Trend of COVID-19 Infection: Longitudinal Study. JMIR Public Health Surveill 2022; 8:e36424. [PMID: 36240022 PMCID: PMC9645421 DOI: 10.2196/36424] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2022] [Revised: 05/30/2022] [Accepted: 10/11/2022] [Indexed: 11/05/2022] Open
Abstract
BACKGROUND The distribution of population-level real-time reverse transcription-polymerase chain reaction (RT-PCR) cycle threshold (Ct) values as a proxy of viral load may be a useful indicator for predicting COVID-19 dynamics. OBJECTIVE The aim of this study was to determine the relationship between the daily trend of average Ct values and COVID-19 dynamics, calculated as the daily number of hospitalized patients with COVID-19, daily number of new positive tests, daily number of COVID-19 deaths, and number of hospitalized patients with COVID-19 by age. We further sought to determine the lag between these data series. METHODS The samples included in this study were collected from March 21, 2021, to December 1, 2021. Daily Ct values of all patients who were referred to the Molecular Diagnostic Laboratory of Iran University of Medical Sciences in Tehran, Iran, for RT-PCR tests were recorded. The daily number of positive tests and the number of hospitalized patients by age group were extracted from the COVID-19 patient information registration system in Tehran province, Iran. An autoregressive integrated moving average (ARIMA) model was constructed for the time series of variables. Cross-correlation analysis was then performed to determine the best lag and correlations between the average daily Ct value and other COVID-19 dynamics-related variables. Finally, the best-selected lag of Ct identified through cross-correlation was incorporated as a covariate into the autoregressive integrated moving average with exogenous variables (ARIMAX) model to calculate the coefficients. RESULTS Daily average Ct values showed a significant negative correlation (23-day time delay) with the daily number of newly hospitalized patients (P=.02), 30-day time delay with the daily number of new positive tests (P=.02), and daily number of COVID-19 deaths (P=.02). The daily average Ct value with a 30-day delay could impact the daily number of positive tests for COVID-19 (β=-16.87, P<.001) and the daily number of deaths from COVID-19 (β=-1.52, P=.03). There was a significant association between Ct lag (23 days) and the number of COVID-19 hospitalizations (β=-24.12, P=.005). Cross-correlation analysis showed significant time delays in the average Ct values and daily hospitalized patients between 18-59 years (23-day time delay, P=.02) and in patients over 60 years old (23-day time delay, P<.001). No statistically significant relation was detected in the number of daily hospitalized patients under 5 years old (9-day time delay, P=.27) and aged 5-17 years (13-day time delay, P=.39). CONCLUSIONS It is important for surveillance of COVID-19 to find a good indicator that can predict epidemic surges in the community. Our results suggest that the average daily Ct value with a 30-day delay can predict increases in the number of positive confirmed COVID-19 cases, which may be a useful indicator for the health system.
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Affiliation(s)
- Paria Dehesh
- Department of Epidemiology, School of Public Health, Iran University of Medical Sciences, Tehran, Iran
| | - Hamid Reza Baradaran
- Department of Epidemiology, School of Public Health, Iran University of Medical Sciences, Tehran, Iran
- Ageing Clinical and Experimental Research Team, Institute of Applied Health Sciences, University of Aberdeen, Aberdeen, United Kingdom
| | - Babak Eshrati
- Department of Epidemiology, School of Public Health, Iran University of Medical Sciences, Tehran, Iran
- Preventive Medicine and Public Health Research Center, Tehran, Iran
| | - Seyed Abbas Motevalian
- Department of Epidemiology, School of Public Health, Iran University of Medical Sciences, Tehran, Iran
| | - Masoud Salehi
- Department of Biostatistics, School of Public Health, Iran University of Medical Sciences, Tehran, Iran
| | - Tahereh Donyavi
- Department of Biotechnology, School of Allied Medical Sciences, Iran University of Medical Sciences, Tehran, Iran
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ALOISI A, DE STEFANO V. Essential jobs, remote work and digital surveillance: Addressing the COVID-19 pandemic panopticon. Int Labour Rev 2022; 161:289-314. [PMID: 34548685 PMCID: PMC8444901 DOI: 10.1111/ilr.12219] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/30/2023]
Abstract
An unprecedented COVID-19-induced explosion in digital surveillance has reconfigured power relationships in professional settings. This article critically concentrates on the interplay between technology-enabled intrusive monitoring and the augmentation of managerial prerogatives in physical and digital workplaces. It identifies excessive supervision as the common denominator of "essential" and "remotable" activities, besides discussing the various drawbacks faced by the two categories of workers during (and after) the pandemic. It also assesses the adequacy of the current European Union legal framework in addressing the expansion of data-driven management. Social dialogue, workers' empowerment and digital literacy are identified as effective ways to promote organizational flexibility, well-being and competitiveness.
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Reinhart A, Brooks L, Jahja M, Rumack A, Tang J, Agrawal S, Al Saeed W, Arnold T, Basu A, Bien J, Cabrera ÁA, Chin A, Chua EJ, Clark B, Colquhoun S, DeFries N, Farrow DC, Forlizzi J, Grabman J, Gratzl S, Green A, Haff G, Han R, Harwood K, Hu AJ, Hyde R, Hyun S, Joshi A, Kim J, Kuznetsov A, La Motte-Kerr W, Lee YJ, Lee K, Lipton ZC, Liu MX, Mackey L, Mazaitis K, McDonald DJ, McGuinness P, Narasimhan B, O'Brien MP, Oliveira NL, Patil P, Perer A, Politsch CA, Rajanala S, Rucker D, Scott C, Shah NH, Shankar V, Sharpnack J, Shemetov D, Simon N, Smith BY, Srivastava V, Tan S, Tibshirani R, Tuzhilina E, Van Nortwick AK, Ventura V, Wasserman L, Weaver B, Weiss JC, Whitman S, Williams K, Rosenfeld R, Tibshirani RJ. An open repository of real-time COVID-19 indicators. Proc Natl Acad Sci U S A 2021; 118:e2111452118. [PMID: 34903654 DOI: 10.1073/pnas.2111452118] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/18/2021] [Indexed: 12/20/2022] Open
Abstract
To study the COVID-19 pandemic, its effects on society, and measures for reducing its spread, researchers need detailed data on the course of the pandemic. Standard public health data streams suffer inconsistent reporting and frequent, unexpected revisions. They also miss other aspects of a population’s behavior that are worthy of consideration. We present an open database of COVID signals in the United States, measured at the county level and updated daily. This includes traditionally reported COVID cases and deaths, and many others: measures of mobility, social distancing, internet search trends, self-reported symptoms, and patterns of COVID-related activity in deidentified medical insurance claims. The database provides all signals in a common, easy-to-use format, empowering both public health research and operational decision-making. The COVID-19 pandemic presented enormous data challenges in the United States. Policy makers, epidemiological modelers, and health researchers all require up-to-date data on the pandemic and relevant public behavior, ideally at fine spatial and temporal resolution. The COVIDcast API is our attempt to fill this need: Operational since April 2020, it provides open access to both traditional public health surveillance signals (cases, deaths, and hospitalizations) and many auxiliary indicators of COVID-19 activity, such as signals extracted from deidentified medical claims data, massive online surveys, cell phone mobility data, and internet search trends. These are available at a fine geographic resolution (mostly at the county level) and are updated daily. The COVIDcast API also tracks all revisions to historical data, allowing modelers to account for the frequent revisions and backfill that are common for many public health data sources. All of the data are available in a common format through the API and accompanying R and Python software packages. This paper describes the data sources and signals, and provides examples demonstrating that the auxiliary signals in the COVIDcast API present information relevant to tracking COVID activity, augmenting traditional public health reporting and empowering research and decision-making.
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McDonald DJ, Bien J, Green A, Hu AJ, DeFries N, Hyun S, Oliveira NL, Sharpnack J, Tang J, Tibshirani R, Ventura V, Wasserman L, Tibshirani RJ. Can auxiliary indicators improve COVID-19 forecasting and hotspot prediction? Proc Natl Acad Sci U S A 2021; 118:e2111453118. [PMID: 34903655 PMCID: PMC8713796 DOI: 10.1073/pnas.2111453118] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/02/2021] [Indexed: 02/07/2023] Open
Abstract
Short-term forecasts of traditional streams from public health reporting (such as cases, hospitalizations, and deaths) are a key input to public health decision-making during a pandemic. Since early 2020, our research group has worked with data partners to collect, curate, and make publicly available numerous real-time COVID-19 indicators, providing multiple views of pandemic activity in the United States. This paper studies the utility of five such indicators-derived from deidentified medical insurance claims, self-reported symptoms from online surveys, and COVID-related Google search activity-from a forecasting perspective. For each indicator, we ask whether its inclusion in an autoregressive (AR) model leads to improved predictive accuracy relative to the same model excluding it. Such an AR model, without external features, is already competitive with many top COVID-19 forecasting models in use today. Our analysis reveals that 1) inclusion of each of these five indicators improves on the overall predictive accuracy of the AR model; 2) predictive gains are in general most pronounced during times in which COVID cases are trending in "flat" or "down" directions; and 3) one indicator, based on Google searches, seems to be particularly helpful during "up" trends.
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Affiliation(s)
- Daniel J McDonald
- Department of Statistics, University of British Columbia, Vancouver, BC, Canada V6T 1Z4;
| | - Jacob Bien
- Department of Data Sciences and Operations, University of Southern California, Los Angeles, CA 90089
| | - Alden Green
- Department of Statistics & Data Science, Carnegie Mellon University, Pittsburgh, PA 15213
| | - Addison J Hu
- Department of Statistics & Data Science, Carnegie Mellon University, Pittsburgh, PA 15213
- Machine Learning Department, Carnegie Mellon University, Pittsburgh, PA 15213
| | - Nat DeFries
- Machine Learning Department, Carnegie Mellon University, Pittsburgh, PA 15213
| | - Sangwon Hyun
- Department of Data Sciences and Operations, University of Southern California, Los Angeles, CA 90089
| | - Natalia L Oliveira
- Department of Statistics & Data Science, Carnegie Mellon University, Pittsburgh, PA 15213
- Machine Learning Department, Carnegie Mellon University, Pittsburgh, PA 15213
| | - James Sharpnack
- Department of Statistics, University of California, Davis, CA 95616
| | - Jingjing Tang
- Computational Biology Department, Carnegie Mellon University, Pittsburgh, PA 15213
| | - Robert Tibshirani
- Department of Statistics, Stanford University, Stanford, CA 94305
- Department of Biomedical Data Science, Stanford University, Stanford, CA 94305
| | - Valérie Ventura
- Department of Statistics & Data Science, Carnegie Mellon University, Pittsburgh, PA 15213
| | - Larry Wasserman
- Department of Statistics & Data Science, Carnegie Mellon University, Pittsburgh, PA 15213
- Machine Learning Department, Carnegie Mellon University, Pittsburgh, PA 15213
| | - Ryan J Tibshirani
- Department of Statistics & Data Science, Carnegie Mellon University, Pittsburgh, PA 15213
- Machine Learning Department, Carnegie Mellon University, Pittsburgh, PA 15213
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Cawley C, Bergey F, Mehl A, Finckh A, Gilsdorf A. Novel Methods in the Surveillance of Influenza-Like Illness in Germany Using Data From a Symptom Assessment App (Ada): Observational Case Study. JMIR Public Health Surveill 2021; 7:e26523. [PMID: 34734836 PMCID: PMC8722671 DOI: 10.2196/26523] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2021] [Revised: 08/04/2021] [Accepted: 08/16/2021] [Indexed: 11/13/2022] Open
Abstract
Background Participatory epidemiology is an emerging field harnessing consumer data entries of symptoms. The free app Ada allows users to enter the symptoms they are experiencing and applies a probabilistic reasoning model to provide a list of possible causes for these symptoms. Objective The objective of our study is to explore the potential contribution of Ada data to syndromic surveillance by comparing symptoms of influenza-like illness (ILI) entered by Ada users in Germany with data from a national population-based reporting system called GrippeWeb. Methods We extracted data for all assessments performed by Ada users in Germany over 3 seasons (2017/18, 2018/19, and 2019/20) and identified those with ILI (report of fever with cough or sore throat). The weekly proportion of assessments in which ILI was reported was calculated (overall and stratified by age group), standardized for the German population, and compared with trends in ILI rates reported by GrippeWeb using time series graphs, scatterplots, and Pearson correlation coefficient. Results In total, 2.1 million Ada assessments (for any symptoms) were included. Within seasons and across age groups, the Ada data broadly replicated trends in estimated weekly ILI rates when compared with GrippeWeb data (Pearson correlation—2017-18: r=0.86, 95% CI 0.76-0.92; P<.001; 2018-19: r=0.90, 95% CI 0.84-0.94; P<.001; 2019-20: r=0.64, 95% CI 0.44-0.78; P<.001). However, there were differences in the exact timing and nature of the epidemic curves between years. Conclusions With careful interpretation, Ada data could contribute to identifying broad ILI trends in countries without existing population-based monitoring systems or to the syndromic surveillance of symptoms not covered by existing systems.
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Kostkova P, Saigí-Rubió F, Eguia H, Borbolla D, Verschuuren M, Hamilton C, Azzopardi-Muscat N, Novillo-Ortiz D. Data and Digital Solutions to Support Surveillance Strategies in the Context of the COVID-19 Pandemic. Front Digit Health 2021; 3:707902. [PMID: 34713179 PMCID: PMC8522016 DOI: 10.3389/fdgth.2021.707902] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2021] [Accepted: 06/30/2021] [Indexed: 12/23/2022] Open
Abstract
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.
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Affiliation(s)
- Patty Kostkova
- UCL Centre for Digital Public Health in Emergencies (dPHE), Institute for Risk and Disaster Reduction, University College London, London, United Kingdom
| | - Francesc Saigí-Rubió
- Faculty of Health Sciences, Universitat Oberta de Catalunya, Barcelona, Spain.,Interdisciplinary Research Group on ICTs, Barcelona, Spain
| | - Hans Eguia
- Faculty of Health Sciences, Universitat Oberta de Catalunya, Barcelona, Spain.,SEMERGEN New Technologies Working Group, Madrid, Spain
| | - Damian Borbolla
- Department of Biomedical Informatics, University of Utah, Salt Lake City, UT, United States
| | - Marieke Verschuuren
- Division of Country Health Policies and Systems, Regional Office for Europe, World Health Organization, Copenhagen, Denmark
| | - Clayton Hamilton
- Division of Country Health Policies and Systems, Regional Office for Europe, World Health Organization, Copenhagen, Denmark
| | - Natasha Azzopardi-Muscat
- Division of Country Health Policies and Systems, Regional Office for Europe, World Health Organization, Copenhagen, Denmark
| | - David Novillo-Ortiz
- Division of Country Health Policies and Systems, Regional Office for Europe, World Health Organization, Copenhagen, Denmark
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21
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De Ridder D, Loizeau AJ, Sandoval JL, Ehrler F, Perrier M, Ritch A, Violot G, Santolini M, Greshake Tzovaras B, Stringhini S, Kaiser L, Pradeau JF, Joost S, Guessous I. Detection of Spatiotemporal Clusters of COVID-19-Associated Symptoms and Prevention Using a Participatory Surveillance App: Protocol for the @choum Study. JMIR Res Protoc 2021; 10:e30444. [PMID: 34449403 PMCID: PMC8496683 DOI: 10.2196/30444] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2021] [Revised: 07/18/2021] [Accepted: 07/19/2021] [Indexed: 01/23/2023] Open
Abstract
BACKGROUND The early detection of clusters of infectious diseases such as the SARS-CoV-2-related COVID-19 disease can promote timely testing recommendation compliance and help to prevent disease outbreaks. Prior research revealed the potential of COVID-19 participatory syndromic surveillance systems to complement traditional surveillance systems. However, most existing systems did not integrate geographic information at a local scale, which could improve the management of the SARS-CoV-2 pandemic. OBJECTIVE The aim of this study is to detect active and emerging spatiotemporal clusters of COVID-19-associated symptoms, and to examine (a posteriori) the association between the clusters' characteristics and sociodemographic and environmental determinants. METHODS This report presents the methodology and development of the @choum (English: "achoo") study, evaluating an epidemiological digital surveillance tool to detect and prevent clusters of individuals (target sample size, N=5000), aged 18 years or above, with COVID-19-associated symptoms living and/or working in the canton of Geneva, Switzerland. The tool is a 5-minute survey integrated into a free and secure mobile app (CoronApp-HUG). Participants are enrolled through a comprehensive communication campaign conducted throughout the 12-month data collection phase. Participants register to the tool by providing electronic informed consent and nonsensitive information (gender, age, geographically masked addresses). Symptomatic participants can then report COVID-19-associated symptoms at their onset (eg, symptoms type, test date) by tapping on the @choum button. Those who have not yet been tested are offered the possibility to be informed on their cluster status (information returned by daily automated clustering analysis). At each participation step, participants are redirected to the official COVID-19 recommendations websites. Geospatial clustering analyses are performed using the modified space-time density-based spatial clustering of applications with noise (MST-DBSCAN) algorithm. RESULTS The study began on September 1, 2020, and will be completed on February 28, 2022. Multiple tests performed at various time points throughout the 5-month preparation phase have helped to improve the tool's user experience and the accuracy of the clustering analyses. A 1-month pilot study performed among 38 pharmacists working in 7 Geneva-based pharmacies confirmed the proper functioning of the tool. Since the tool's launch to the entire population of Geneva on February 11, 2021, data are being collected and clusters are being carefully monitored. The primary study outcomes are expected to be published in mid-2022. CONCLUSIONS The @choum study evaluates an innovative participatory epidemiological digital surveillance tool to detect and prevent clusters of COVID-19-associated symptoms. @choum collects precise geographic information while protecting the user's privacy by using geomasking methods. By providing an evidence base to inform citizens and local authorities on areas potentially facing a high COVID-19 burden, the tool supports the targeted allocation of public health resources and promotes testing. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID) DERR1-10.2196/30444.
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Affiliation(s)
- David De Ridder
- Division of Primary Care, Geneva University Hospitals, Geneva, Switzerland
- Department of Health and Community Medicine, Faculty of Medicine, University of Geneva, Geneva, Switzerland
- Group of Geographic Information Research and Analysis in Population Health, Geneva University Hospitals, Geneva, Switzerland
- Laboratory of Geographic Information Systems, School of Architecture, Civil and Environmental Engineering, Swiss Federal Institute of Technology Lausanne, Lausanne, Switzerland
| | - Andrea Jutta Loizeau
- Division of Primary Care, Geneva University Hospitals, Geneva, Switzerland
- Department of Health and Community Medicine, Faculty of Medicine, University of Geneva, Geneva, Switzerland
| | - José Luis Sandoval
- Group of Geographic Information Research and Analysis in Population Health, Geneva University Hospitals, Geneva, Switzerland
- Department of Oncology, Geneva University Hospitals, Geneva, Switzerland
| | - Frédéric Ehrler
- Direction of Information Systems, Geneva University Hospitals, Geneva, Switzerland
| | - Myriam Perrier
- Direction of Information Systems, Geneva University Hospitals, Geneva, Switzerland
| | - Albert Ritch
- Direction of Information Systems, Geneva University Hospitals, Geneva, Switzerland
| | - Guillemette Violot
- Communication Directorate, Geneva University Hospitals, Geneva, Switzerland
| | - Marc Santolini
- Center for Research and Interdisciplinarity, INSERM U1284, University of Paris, Paris, France
| | | | - Silvia Stringhini
- Division of Primary Care, Geneva University Hospitals, Geneva, Switzerland
- Department of Health and Community Medicine, Faculty of Medicine, University of Geneva, Geneva, Switzerland
| | - Laurent Kaiser
- Department of Health and Community Medicine, Faculty of Medicine, University of Geneva, Geneva, Switzerland
- Division of Infectious Disease and Laboratory of Virology, Geneva University Hospitals, Geneva, Switzerland
- Center for Emerging Viral Diseases, Geneva University Hospitals, Geneva, Switzerland
| | | | - Stéphane Joost
- Group of Geographic Information Research and Analysis in Population Health, Geneva University Hospitals, Geneva, Switzerland
- Laboratory of Geographic Information Systems, School of Architecture, Civil and Environmental Engineering, Swiss Federal Institute of Technology Lausanne, Lausanne, Switzerland
| | - Idris Guessous
- Division of Primary Care, Geneva University Hospitals, Geneva, Switzerland
- Department of Health and Community Medicine, Faculty of Medicine, University of Geneva, Geneva, Switzerland
- Group of Geographic Information Research and Analysis in Population Health, Geneva University Hospitals, Geneva, Switzerland
- Laboratory of Geographic Information Systems, School of Architecture, Civil and Environmental Engineering, Swiss Federal Institute of Technology Lausanne, Lausanne, Switzerland
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22
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Kishore K, Jaswal V, Verma M, Koushal V. Exploring the Utility of Google Mobility Data During the COVID-19 Pandemic in India: Digital Epidemiological Analysis. JMIR Public Health Surveill 2021; 7:e29957. [PMID: 34174780 PMCID: PMC8407437 DOI: 10.2196/29957] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2021] [Revised: 06/10/2021] [Accepted: 06/17/2021] [Indexed: 12/23/2022] Open
Abstract
Background Association between human mobility and disease transmission has been established for COVID-19, but quantifying the levels of mobility over large geographical areas is difficult. Google has released Community Mobility Reports (CMRs) containing data about the movement of people, collated from mobile devices. Objective The aim of this study is to explore the use of CMRs to assess the role of mobility in spreading COVID-19 infection in India. Methods In this ecological study, we analyzed CMRs to determine human mobility between March and October 2020. The data were compared for the phases before the lockdown (between March 14 and 25, 2020), during lockdown (March 25-June 7, 2020), and after the lockdown (June 8-October 15, 2020) with the reference periods (ie, January 3-February 6, 2020). Another data set depicting the burden of COVID-19 as per various disease severity indicators was derived from a crowdsourced API. The relationship between the two data sets was investigated using the Kendall tau correlation to depict the correlation between mobility and disease severity. Results At the national level, mobility decreased from –38% to –77% for all areas but residential (which showed an increase of 24.6%) during the lockdown compared to the reference period. At the beginning of the unlock phase, the state of Sikkim (minimum cases: 7) with a –60% reduction in mobility depicted more mobility compared to –82% in Maharashtra (maximum cases: 1.59 million). Residential mobility was negatively correlated (–0.05 to –0.91) with all other measures of mobility. The magnitude of the correlations for intramobility indicators was comparatively low for the lockdown phase (correlation ≥0.5 for 12 indicators) compared to the other phases (correlation ≥0.5 for 45 and 18 indicators in the prelockdown and unlock phases, respectively). A high correlation coefficient between epidemiological and mobility indicators was observed for the lockdown and unlock phases compared to the prelockdown phase. Conclusions Mobile-based open-source mobility data can be used to assess the effectiveness of social distancing in mitigating disease spread. CMR data depicted an association between mobility and disease severity, and we suggest using this technique to supplement future COVID-19 surveillance.
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Affiliation(s)
- Kamal Kishore
- Postgraduate Institute of Medical Education and Research, Chandigarh, India
| | | | - Madhur Verma
- All India Institute of Medical Sciences, Bathinda, India
| | - Vipin Koushal
- Postgraduate Institute of Medical Education and Research, Chandigarh, India
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23
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Tozzi AE, Gesualdo F, Urbani E, Sbenaglia A, Ascione R, Procopio N, Croci I, Rizzo C. Digital Surveillance Through an Online Decision Support Tool for COVID-19 Over One Year of the Pandemic in Italy: Observational Study. J Med Internet Res 2021; 23:e29556. [PMID: 34292866 PMCID: PMC8366755 DOI: 10.2196/29556] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2021] [Revised: 07/14/2021] [Accepted: 07/18/2021] [Indexed: 01/22/2023] Open
Abstract
BACKGROUND Italy has experienced severe consequences (ie, hospitalizations and deaths) during the COVID-19 pandemic. Online decision support systems (DSS) and self-triage applications have been used in several settings to supplement health authority recommendations to prevent and manage COVID-19. A digital Italian health tech startup, Paginemediche, developed a noncommercial, online DSS with a chat user interface to assist individuals in Italy manage their potential exposure to COVID-19 and interpret their symptoms since early in the pandemic. OBJECTIVE This study aimed to compare the trend in online DSS sessions with that of COVID-19 cases reported by the national health surveillance system in Italy, from February 2020 to March 2021. METHODS We compared the number of sessions by users with a COVID-19-positive contact and users with COVID-19-compatible symptoms with the number of cases reported by the national surveillance system. To calculate the distance between the time series, we used the dynamic time warping algorithm. We applied Symbolic Aggregate approXimation (SAX) encoding to the time series in 1-week periods. We calculated the Hamming distance between the SAX strings. We shifted time series of online DSS sessions 1 week ahead. We measured the improvement in Hamming distance to verify the hypothesis that online DSS sessions anticipate the trends in cases reported to the official surveillance system. RESULTS We analyzed 75,557 sessions in the online DSS; 65,207 were sessions by symptomatic users, while 19,062 were by contacts of individuals with COVID-19. The highest number of online DSS sessions was recorded early in the pandemic. Second and third peaks were observed in October 2020 and March 2021, respectively, preceding the surge in notified COVID-19 cases by approximately 1 week. The distance between sessions by users with COVID-19 contacts and reported cases calculated by dynamic time warping was 61.23; the distance between sessions by symptomatic users was 93.72. The time series of users with a COVID-19 contact was more consistent with the trend in confirmed cases. With the 1-week shift, the Hamming distance between the time series of sessions by users with a COVID-19 contact and reported cases improved from 0.49 to 0.46. We repeated the analysis, restricting the time window to between July 2020 and December 2020. The corresponding Hamming distance was 0.16 before and improved to 0.08 after the time shift. CONCLUSIONS Temporal trends in the number of online COVID-19 DSS sessions may precede the trend in reported COVID-19 cases through traditional surveillance. The trends in sessions by users with a contact with COVID-19 may better predict reported cases of COVID-19 than sessions by symptomatic users. Data from online DSS may represent a useful supplement to traditional surveillance and support the identification of early warning signals in the COVID-19 pandemic.
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Affiliation(s)
- Alberto Eugenio Tozzi
- Multifactorial and Complex Diseases Research Area, Bambino Gesù Children's Hospital IRCCS, Rome, Italy
| | - Francesco Gesualdo
- Multifactorial and Complex Diseases Research Area, Bambino Gesù Children's Hospital IRCCS, Rome, Italy
| | | | | | | | | | - Ileana Croci
- Multifactorial and Complex Diseases Research Area, Bambino Gesù Children's Hospital IRCCS, Rome, Italy
| | - Caterina Rizzo
- Clinical Pathways and Epidemiology Unit, Bambino Gesù Children's Hospital IRCCS, Rome, Italy
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24
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Keshavamurthy R, Thumbi SM, Charles LE. Digital Biosurveillance for Zoonotic Disease Detection in Kenya. Pathogens 2021; 10:pathogens10070783. [PMID: 34206236 PMCID: PMC8308926 DOI: 10.3390/pathogens10070783] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2021] [Revised: 06/15/2021] [Accepted: 06/18/2021] [Indexed: 11/16/2022] Open
Abstract
Infectious disease surveillance is crucial for early detection and situational awareness of disease outbreaks. Digital biosurveillance monitors large volumes of open-source data to flag potential health threats. This study investigates the potential of digital surveillance in the detection of the top five priority zoonotic diseases in Kenya: Rift Valley fever (RVF), anthrax, rabies, brucellosis, and trypanosomiasis. Open-source disease events reported between August 2016 and October 2020 were collected and key event-specific information was extracted using a newly developed disease event taxonomy. A total of 424 disease reports encompassing 55 unique events belonging to anthrax (43.6%), RVF (34.6%), and rabies (21.8%) were identified. Most events were first reported by news media (78.2%) followed by international health organizations (16.4%). News media reported the events 4.1 (±4.7) days faster than the official reports. There was a positive association between official reporting and RVF events (odds ratio (OR) 195.5, 95% confidence interval (CI); 24.01-4756.43, p < 0.001) and a negative association between official reporting and local media coverage of events (OR 0.03, 95% CI; 0.00-0.17, p = 0.030). This study highlights the usefulness of local news in the detection of potentially neglected zoonotic disease events and the importance of digital biosurveillance in resource-limited settings.
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Affiliation(s)
- Ravikiran Keshavamurthy
- Paul G. Allen School for Global Animal Health, Washington State University, Pullman, WA 99164, USA; (R.K.); (S.M.T.)
- Pacific Northwest National Laboratory, Richland, WA 99354, USA
| | - Samuel M. Thumbi
- Paul G. Allen School for Global Animal Health, Washington State University, Pullman, WA 99164, USA; (R.K.); (S.M.T.)
- Center for Epidemiological Modelling and Analysis, Institute of Tropical and Infectious Diseases, University of Nairobi, Nairobi 30197, Kenya
- Institute of Immunology and Infection Research, University of Edinburgh, Edinburgh EH9 3FL, UK
| | - Lauren E. Charles
- Paul G. Allen School for Global Animal Health, Washington State University, Pullman, WA 99164, USA; (R.K.); (S.M.T.)
- Pacific Northwest National Laboratory, Richland, WA 99354, USA
- Correspondence:
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25
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Zimmermann BM, Fiske A, Prainsack B, Hangel N, McLennan S, Buyx A. Early Perceptions of COVID-19 Contact Tracing Apps in German-Speaking Countries: Comparative Mixed Methods Study. J Med Internet Res 2021; 23:e25525. [PMID: 33503000 PMCID: PMC7872326 DOI: 10.2196/25525] [Citation(s) in RCA: 41] [Impact Index Per Article: 13.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2020] [Revised: 12/17/2020] [Accepted: 01/09/2021] [Indexed: 01/07/2023] Open
Abstract
Background The main German-speaking countries (Germany, Austria, and Switzerland) have implemented digital contact tracing apps to assist the authorities with COVID-19 containment strategies. Low user rates for these apps can affect contact tracing and, thus, its usefulness in controlling the spread of the novel coronavirus. Objective This study aimed to assess the early perceptions of people living in the German-speaking countries and compare them with the frames portrayed in the newspapers during the first wave of the COVID-19 pandemic. Methods We conducted qualitative interviews with 159 participants of the SolPan project. Of those, 110 participants discussed contact tracing apps and were included in this study. We analyzed articles regarding contact tracing apps from 12 newspapers in the German-speaking countries. Results Study participants perceived and newspaper coverage in all German-speaking countries framed contact tracing apps as governmental surveillance tools and embedded them in a broader context of technological surveillance. Participants identified trust in authorities, respect of individual privacy, voluntariness, and temporary use of contact tracing apps as prerequisites for democratic compatibility. Newspapers commonly referenced the use of such apps in Asian countries, emphasizing the differences in privacy regulation among these countries. Conclusions The uptake of digital contact tracing apps in German-speaking countries may be undermined due to privacy risks that are not compensated by potential benefits and are rooted in a deeper skepticism towards digital tools. When authorities plan to implement new digital tools and practices in the future, they should be very transparent and proactive in communicating their objectives and the role of the technology—and how it differs from other, possibly similar, tools. It is also important to publicly address ethical, legal, and social issues related to such technologies prior to their launch.
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Affiliation(s)
- Bettina Maria Zimmermann
- Institute of History and Ethics in Medicine, Technical University Munich, Munich, Germany.,Institute for Biomedical Ethics, University of Basel, Basel, Switzerland
| | - Amelia Fiske
- Institute of History and Ethics in Medicine, Technical University Munich, Munich, Germany
| | - Barbara Prainsack
- Department of Political Science, University of Vienna, Vienna, Austria
| | - Nora Hangel
- Institute of History and Ethics in Medicine, Technical University Munich, Munich, Germany
| | - Stuart McLennan
- Institute of History and Ethics in Medicine, Technical University Munich, Munich, Germany.,Institute for Biomedical Ethics, University of Basel, Basel, Switzerland
| | - Alena Buyx
- Institute of History and Ethics in Medicine, Technical University Munich, Munich, Germany
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26
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Maytin L, Maytin J, Agarwal P, Krenitsky A, Krenitsky J, Epstein RS. Attitudes and Perceptions Toward COVID-19 Digital Surveillance: Survey of Young Adults in the United States. JMIR Form Res 2021; 5:e23000. [PMID: 33347420 PMCID: PMC7800905 DOI: 10.2196/23000] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2020] [Revised: 11/25/2020] [Accepted: 12/19/2020] [Indexed: 12/21/2022] Open
Abstract
BACKGROUND COVID-19 is an international health crisis of particular concern in the United States, which saw surges of infections with the lifting of lockdowns and relaxed social distancing. Young adults have proven to be a critical factor for COVID-19 transmission and are an important target of the efforts to contain the pandemic. Scalable digital public health technologies could be deployed to reduce COVID-19 transmission, but their use depends on the willingness of young adults to participate in surveillance. OBJECTIVE The aim of this study is to determine the attitudes of young adults regarding COVID-19 digital surveillance, including which aspects they would accept and which they would not, as well as to determine factors that may be associated with their willingness to participate in digital surveillance. METHODS We conducted an anonymous online survey of young adults aged 18-24 years throughout the United States in June 2020. The questionnaire contained predominantly closed-ended response options with one open-ended question. Descriptive statistics were applied to the data. RESULTS Of 513 young adult respondents, 383 (74.7%) agreed that COVID-19 represents a public health crisis. However, only 231 (45.1%) agreed to actively share their COVID-19 status or symptoms for monitoring and only 171 (33.4%) reported a willingness to allow access to their cell phone for passive location tracking or contact tracing. CONCLUSIONS Despite largely agreeing that COVID-19 represents a serious public health risk, the majority of young adults sampled were reluctant to participate in digital monitoring to manage the pandemic. This was true for both commonly used methods of public health surveillance (such as contact tracing) and novel methods designed to facilitate a return to normal (such as frequent symptom checking through digital apps). This is a potential obstacle to ongoing containment measures (many of which rely on widespread surveillance) and may reflect a need for greater education on the benefits of public health digital surveillance for young adults.
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Affiliation(s)
- Lauren Maytin
- Epstein Health LLC, Woodcliff Lake, NJ, United States
| | - Jason Maytin
- Epstein Health LLC, Woodcliff Lake, NJ, United States
| | - Priya Agarwal
- Epstein Health LLC, Woodcliff Lake, NJ, United States
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Leitmeyer KC, Espinosa L, Broberg EK, Struelens MJ. Automated digital reporting of clinical laboratory information to national public health surveillance systems, results of a EU/EEA survey, 2018. ACTA ACUST UNITED AC 2021; 25. [PMID: 33006301 PMCID: PMC7531069 DOI: 10.2807/1560-7917.es.2020.25.39.1900591] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
Abstract
BackgroundTimely reporting of microbiology test results is essential for infection management. Automated, machine-to-machine (M2M) reporting of diagnostic and antimicrobial resistance (AMR) data from laboratory information management systems (LIMS) to public health agencies improves timeliness and completeness of communicable disease surveillance.AimWe surveyed microbiology data reporting practices for national surveillance of EU-notifiable diseases in European Union/European Economic Area (EU/EEA) countries in 2018.MethodsEuropean Centre for Disease Prevention and Control (ECDC) National Microbiology and Surveillance Focal Points completed a questionnaire on the modalities and scope of clinical microbiology laboratory data reporting.ResultsComplete data were provided for all 30 EU/EEA countries. Clinical laboratories used a LIMS in 28 countries. LIMS data on EU-notifiable diseases and AMR were M2M-reported to the national level in 14 and nine countries, respectively. In the 14 countries, associated demographic data reported allowed the de-duplication of patient reports. In 13 countries, M2M-reported data were used for cluster detection at the national level. M2M laboratory data reporting had been validated against conventional surveillance methods in six countries, and replaced those in five. Barriers to M2M reporting included lack of information technology support and financial incentives.ConclusionM2M-reported laboratory data were used for national public health surveillance and alert purposes in nearly half of the EU/EEA countries in 2018. Reported data on infectious diseases and AMR varied in extent and disease coverage across countries and laboratories. Improving automated laboratory-based surveillance will depend on financial and regulatory incentives, and harmonisation of health information and communication systems.
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Affiliation(s)
| | - Laura Espinosa
- European Centre for Disease Prevention and Control (ECDC), Stockholm, Sweden
| | | | - Marc Jean Struelens
- European Centre for Disease Prevention and Control (ECDC), Stockholm, Sweden
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- The ECDC National Focal Points laboratory e-reporting survey group members are listed at the end of the article
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Kokkoris MD, Kamleitner B. Would You Sacrifice Your Privacy to Protect Public Health? Prosocial Responsibility in a Pandemic Paves the Way for Digital Surveillance. Front Psychol 2020; 11:578618. [PMID: 33071918 PMCID: PMC7531172 DOI: 10.3389/fpsyg.2020.578618] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2020] [Accepted: 08/20/2020] [Indexed: 11/13/2022] Open
Abstract
Digital surveillance methods, such as location tracking apps on smartphones, have been implemented in many countries during the COVID-19 pandemic, but not much is known about predictors of their acceptance. Could it be that prosocial responsibility, to which authorities appealed in order to enhance compliance with quarantine measures, also increases acceptance of digital surveillance and restrictions of privacy? In their fight against the COVID-19 pandemic, governments around the world communicated that self-isolation and social distancing measures are every citizen’s duty in order to protect the health not only of oneself but also of vulnerable others. We suggest that prosocial responsibility besides motivating people to comply with anti-pandemic measures also undermines people’s valuation of privacy. In an online research conducted with US participants, we examined correlates of people’s willingness to sacrifice individual rights and succumb to surveillance with a particular focus on prosocial responsibility. First, replicating prior research, we found that perceived prosocial responsibility was a powerful predictor of compliance with self-isolation and social distancing measures. Second, going beyond prior research, we found that perceived prosocial responsibility also predicted willingness to accept restrictions of individual rights and privacy, as well as to accept digital surveillance for the sake of public health. While we identify a range of additional predictors, the effects of prosocial responsibility hold after controlling for alternative processes, such as perceived self-risk, impact of the pandemic on oneself, or personal value of freedom. These findings suggest that prosocial responsibility may act as a Trojan horse for privacy compromises.
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Affiliation(s)
- Michail D Kokkoris
- Marketing Department, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Bernadette Kamleitner
- Marketing Department, Institute for Marketing and Consumer Research, WU Vienna University of Economics and Business, Vienna, Austria
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Abstract
Digital technologies are being used to combat the coronavirus disease 2019 (COVID-19) pandemic through a variety of methods, including monitoring compliance with quarantine and contact tracing. These uses of technology are said to promote public health outcomes but risk undermining rights to privacy. In this article we focus on the use of digital technologies for contact tracing, such as the COVIDSafe app used in Australia. We explore the kind of framework that might be used for evaluating the design, deployment and governance of such technologies to ensure they operate in a manner that is proportionate to the ends to be achieved. We conclude that, in addition to issues of privacy, any use of contact tracing technology should address important considerations of efficacy, equity and accountability.
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Affiliation(s)
| | - Jeannie Marie Paterson
- Professor Jeannie Paterson, Melbourne Law School, The University of Melbourne, 185 Pelham Street, Parkville, VIC 3053, Australia.
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Xiong J, Hswen Y, Naslund JA. Digital Surveillance for Monitoring Environmental Health Threats: A Case Study Capturing Public Opinion from Twitter about the 2019 Chennai Water Crisis. Int J Environ Res Public Health 2020; 17:E5077. [PMID: 32674441 DOI: 10.3390/ijerph17145077] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/30/2020] [Revised: 07/02/2020] [Accepted: 07/07/2020] [Indexed: 11/16/2022]
Abstract
Globally, water scarcity has become a common challenge across many regions. Digital surveillance holds promise for monitoring environmental threats to population health due to severe drought. The 2019 Chennai water crisis in India resulted in severe disruptions to social order and daily life, with local residents suffering due to water shortages. This case study explored public opinion captured through the Twitter social media platform, and whether this information could help local governments with emergency response. Sentiment analysis and topic modeling were used to explore public opinion through Twitter during the 2019 Chennai water crisis. The latent Dirichlet allocation (LDA) method identified topics that were most frequently discussed. A naïve Tweet classification method was built, and Twitter posts (called tweets) were allocated to identified topics. Topics were ranked, and corresponding emotions were calculated. A cross-correlation was performed to examine the relationship between online posts about the water crisis and actual rainfall, determined by precipitation levels. During the Chennai water crisis, Twitter users posted content that appeared to show anxiety about the impact of the drought, and also expressed concerns about the government response. Twitter users also mentioned causes for the drought and potential sustainable solutions, which appeared to be mainly positive in tone. Discussion on Twitter can reflect popular public opinion related to emerging environmental health threats. Twitter posts appear viable for informing crisis management as real-time data can be collected and analyzed. Governments and public health officials should adjust their policies and public communication by leveraging online data sources, which could inform disaster prevention measures.
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31
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Yu A. Digital surveillance in post-coronavirus China: A feminist view on the price we pay. Gend Work Organ 2020; 27:774-777. [PMID: 32837009 PMCID: PMC7280578 DOI: 10.1111/gwao.12471] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2020] [Revised: 05/11/2020] [Accepted: 05/12/2020] [Indexed: 11/27/2022]
Affiliation(s)
- Ai Yu
- Institute of Management Studies Goldsmiths University of London
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32
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Bragazzi NL, Mahroum N. Google Trends Predicts Present and Future Plague Cases During the Plague Outbreak in Madagascar: Infodemiological Study. JMIR Public Health Surveill 2019; 5:e13142. [PMID: 30763255 PMCID: PMC6429048 DOI: 10.2196/13142] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2018] [Revised: 01/17/2019] [Accepted: 01/18/2019] [Indexed: 01/08/2023] Open
Abstract
Background Plague is a highly infectious zoonotic disease caused by the bacillus Yersinia pestis. Three major forms of the disease are known: bubonic, septicemic, and pneumonic plague. Though highly related to the past, plague still represents a global public health concern. Cases of plague continue to be reported worldwide. In recent months, pneumonic plague cases have been reported in Madagascar. However, despite such a long-standing and rich history, it is rather difficult to get a comprehensive overview of the general situation. Within the framework of electronic health (eHealth), in which people increasingly search the internet looking for health-related material, new information and communication technologies could enable researchers to get a wealth of data, which could complement traditional surveillance of infectious diseases. Objective In this study, we aimed to assess public reaction regarding the recent plague outbreak in Madagascar by quantitatively characterizing the public’s interest. Methods We captured public interest using Google Trends (GT) and correlated it to epidemiological real-world data in terms of incidence rate and spread pattern. Results Statistically significant positive correlations were found between GT search data and confirmed (R2=0.549), suspected (R2=0.265), and probable (R2=0.518) cases. From a geospatial standpoint, plague-related GT queries were concentrated in Toamasina (100%), Toliara (68%), and Antananarivo (65%). Concerning the forecasting models, the 1-day lag model was selected as the best regression model. Conclusions An earlier digital Web search reaction could potentially contribute to better management of outbreaks, for example, by designing ad hoc interventions that could contain the infection both locally and at the international level, reducing its spread.
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Affiliation(s)
- Nicola Luigi Bragazzi
- Department of Health Sciences, Postgraduate School of Public Health, University of Genoa, Genoa, Italy
| | - Naim Mahroum
- Sackler Faculty of Medicine, Tel-Aviv University, Tel Aviv, Israel
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Abstract
A counterfeit fentanyl crisis is currently underway in the United States. Counterfeit versions of commonly abused prescription drugs laced with fentanyl are being manufactured, distributed, and sold globally, leading to an increase in overdose and death in countries like the United States and Canada. Despite concerns from the U.S. Drug Enforcement Agency regarding covert and overt sale of fentanyls online, no study has examined the role of the Internet and social media on fentanyl illegal marketing and direct-to-consumer access. In response, this study collected and analyzed five months of Twitter data (from June-November 2015) filtered for the keyword "fentanyl" using Amazon Web Services. We then analyzed 28,711 fentanyl-related tweets using text filtering and a machine learning approach called a Biterm Topic Model (BTM) to detect underlying latent patterns or "topics" present in the corpus of tweets. Using this approach we detected a subset of 771 tweets marketing the sale of fentanyls online and then filtered this down to nine unique tweets containing hyperlinks to external websites. Six hyperlinks were associated with online fentanyl classified ads, 2 with illicit online pharmacies, and 1 could not be classified due to traffic redirection. Importantly, the one illicit online pharmacy detected was still accessible and offered the sale of fentanyls and other controlled substances direct-to-consumers with no prescription required at the time of publication of this study. Overall, we detected a relatively small sample of Tweets promoting illegal online sale of fentanyls. However, the detection of even a few online sellers represents a public health danger and a direct violation of law that demands further study.
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Affiliation(s)
- Tim K. Mackey
- Global Health Policy Institute, San Diego, CA, USA
- Division of Global Public Health, University of California, San Diego School of Medicine, San Diego, CA, 92093, USA
- Department of Anesthesiology, University of California San Diego School of Medicine, San Diego, CA, 92093, USA
| | - Janani Kalyanam
- Global Health Policy Institute, San Diego, CA, USA
- Department of Electrical and Computer Engineering, University of California, San Diego, San Diego, CA, 92093, USA
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MacFadden DR, Fisman D, Andre J, Ara Y, Majumder MS, Bogoch II, Daneman N, Wang A, Vavitsas M, Castellani L, Brownstein JS. A Platform for Monitoring Regional Antimicrobial Resistance, Using Online Data Sources: ResistanceOpen. J Infect Dis 2017; 214:S393-S398. [PMID: 28830108 DOI: 10.1093/infdis/jiw343] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Background Our understanding of the global burden of antimicrobial resistance is limited. Complementary approaches to antimicrobial resistance surveillance are needed. Methods We developed a Web-based/mobile platform for aggregating, analyzing, and disseminating regional antimicrobial resistance information. Antimicrobial resistance indices from existing but disparate online sources were identified and abstracted. To validate antimicrobial resistance data, in the absence of regional comparators, US and Canadian indices were aggregated and compared to existing national and state estimates. Measures of variability of antimicrobial susceptibility were determined for the United States and Canada to evaluate magnitudes of differences within countries. Results Over 850 resistance indices globally were identified and abstracted, totaling >5 million isolates, from 340 unique locations. Resistance index coverage spanned 41 countries, 6 continents, 43 of 50 US states, and 8 of 10 Canadian provinces. When compared to reported values, aggregated resistance values for the United States and Canada during 2013 and 2014 demonstrated agreements ranging from 94% to 97%. For the United States, state-specific resistance estimates demonstrated an agreement of 92%. Large differences in antimicrobial susceptibility were seen within countries. Conclusions Using existing nontraditional data sources, we have developed a Web-based platform for aggregating antimicrobial resistance indices to support monitoring of regional antimicrobial resistance patterns.
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Affiliation(s)
- Derek R MacFadden
- Division of Infectious Diseases, University of Toronto, Canada.,Boston Children's Hospital
| | - David Fisman
- Division of Infectious Diseases, University of Toronto, Canada
| | | | | | - Maimuna S Majumder
- Boston Children's Hospital.,Massachusetts Institute of Technology, Cambridge
| | - Isaac I Bogoch
- Division of Infectious Diseases, University of Toronto, Canada
| | - Nick Daneman
- Division of Infectious Diseases, University of Toronto, Canada
| | - Annie Wang
- Division of Infectious Diseases, University of Toronto, Canada
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35
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Althouse BM, Scarpino SV, Meyers LA, Ayers JW, Bargsten M, Baumbach J, Brownstein JS, Castro L, Clapham H, Cummings DAT, Del Valle S, Eubank S, Fairchild G, Finelli L, Generous N, George D, Harper DR, Hébert-Dufresne L, Johansson MA, Konty K, Lipsitch M, Milinovich G, Miller JD, Nsoesie EO, Olson DR, Paul M, Polgreen PM, Priedhorsky R, Read JM, Rodríguez-Barraquer I, Smith DJ, Stefansen C, Swerdlow DL, Thompson D, Vespignani A, Wesolowski A. Enhancing disease surveillance with novel data streams: challenges and opportunities. EPJ Data Sci 2015; 4:17. [PMID: 27990325 PMCID: PMC5156315 DOI: 10.1140/epjds/s13688-015-0054-0] [Citation(s) in RCA: 76] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/07/2023]
Abstract
Novel data streams (NDS), such as web search data or social media updates, hold promise for enhancing the capabilities of public health surveillance. In this paper, we outline a conceptual framework for integrating NDS into current public health surveillance. Our approach focuses on two key questions: What are the opportunities for using NDS and what are the minimal tests of validity and utility that must be applied when using NDS? Identifying these opportunities will necessitate the involvement of public health authorities and an appreciation of the diversity of objectives and scales across agencies at different levels (local, state, national, international). We present the case that clearly articulating surveillance objectives and systematically evaluating NDS and comparing the performance of NDS to existing surveillance data and alternative NDS data is critical and has not sufficiently been addressed in many applications of NDS currently in the literature.
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Affiliation(s)
| | | | - Lauren Ancel Meyers
- Santa Fe Institute, Santa Fe, NM USA
- The University of Texas at Austin, Austin, TX USA
| | | | | | | | - John S Brownstein
- Children’s Hospital Informatics Program, Boston Children’s Hospital, Boston, MA USA
- Department of Pediatrics, Harvard Medical School, Boston, MA USA
- Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, QC Canada
| | - Lauren Castro
- Defense Systems and Analysis Division, Los Alamos National Laboratory, Los Alamos, NM USA
| | - Hannah Clapham
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD USA
| | - Derek AT Cummings
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD USA
| | - Sara Del Valle
- Defense Systems and Analysis Division, Los Alamos National Laboratory, Los Alamos, NM USA
| | - Stephen Eubank
- Virginia BioInformatics Institute and Department of Population Health Sciences, Virginia Tech, Blacksburg, VA USA
| | - Geoffrey Fairchild
- Defense Systems and Analysis Division, Los Alamos National Laboratory, Los Alamos, NM USA
| | - Lyn Finelli
- Influenza Division, Centers for Disease Control and Prevention, Atlanta, GA USA
| | - Nicholas Generous
- Defense Systems and Analysis Division, Los Alamos National Laboratory, Los Alamos, NM USA
| | - Dylan George
- Biomedical Advanced Research and Development Authority (BARDA), Assistant Secretary for Preparedness and Response (ASPR), Department of Health and Human Services, Washington, DC USA
| | - David R Harper
- Chatham House, 10 St James’s Square, London, SW1Y 4LE UK
| | | | - Michael A Johansson
- Division of Vector-Borne Diseases, NCEZID, Centers for Disease Control and Prevention, San Juan, PR USA
| | - Kevin Konty
- Division of Epidemiology, New York City Department of Health and Mental Hygiene, New York, NY USA
| | - Marc Lipsitch
- Communicable Disease Dynamics, Harvard School of Public Health, Boston, MA USA
| | - Gabriel Milinovich
- School of Population Health, The University of Queensland, Brisbane, QLD Australia
| | - Joseph D Miller
- Division of Vector-Borne Diseases, NCEZID, Centers for Disease Control and Prevention, Atlanta, GA USA
| | - Elaine O Nsoesie
- Children’s Hospital Informatics Program, Boston Children’s Hospital, Boston, MA USA
- Department of Pediatrics, Harvard Medical School, Boston, MA USA
| | - Donald R Olson
- Division of Epidemiology, New York City Department of Health and Mental Hygiene, New York, NY USA
| | - Michael Paul
- Department of Computer Science, Johns Hopkins University, Baltimore, MD USA
| | | | - Reid Priedhorsky
- Defense Systems and Analysis Division, Los Alamos National Laboratory, Los Alamos, NM USA
| | - Jonathan M Read
- Department of Epidemiology and Population Health, Institute of Infection and Global Health, University of Liverpool, Liverpool, CH64 7TE UK
- Health Protection Research Unit in Emerging and Zoonotic Infections, NIHR, Liverpool, L69 7BE UK
| | | | - Derek J Smith
- Department of Zoology, University of Cambridge, Cambridge, CB2 3EJ UK
| | | | - David L Swerdlow
- National Center for Immunization and Respiratory Diseases, Centers for Disease Control and Prevention, Atlanta, GA USA
| | | | - Alessandro Vespignani
- Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, MA USA
| | - Amy Wesolowski
- Communicable Disease Dynamics, Harvard School of Public Health, Boston, MA USA
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Noar SM, Althouse BM, Ayers JW, Francis DB, Ribisl KM. Cancer information seeking in the digital age: effects of Angelina Jolie's prophylactic mastectomy announcement. Med Decis Making 2014; 35:16-21. [PMID: 25349187 DOI: 10.1177/0272989x14556130] [Citation(s) in RCA: 43] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
OBJECTIVE . This study used digital surveillance to examine the impact of Angelina Jolie's prophylactic mastectomy announcement on cancer information seeking. METHODS . We analyzed 4 categories of breast cancer-related Internet search queries from 2010 to 2013 in the United States. RESULTS . Compared with the preceding 6 weeks, general information queries were 112% (95% confidence interval [CI], 79-146) higher the day of the announcement and remained 35% (95% CI, 22-49) higher over the week after the editorial. Risk assessment queries were 165% (95% CI, 110-222) higher the day of the announcement and 52% (95% CI, 31-75) higher across the week. Genetics and treatment queries showed little volume before the announcement but increased 2154% (95% CI, 1550-7076) and 9900% (95% CI, 3196-1,064,000) the day of, respectively, and remained higher across the week (812% [95% CI, 402-3913] and 2625% [95% CI, 551-317,000]). All query categories returned to normal volumes by the beginning of the second week. CONCLUSION . Jolie's unique announcement spurred significant information seeking about breast cancer genetic testing and treatment procedures, although the surge in queries returned to preannouncement levels after 1 week. Future research should apply digital methods to advance our understanding of cancer information seeking in the digital age.
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Affiliation(s)
- Seth M Noar
- School of Journalism and Mass Communication, University of North Carolina, Chapel Hill (SMN, DBF),Lineberger Comprehensive Cancer Center, University of North Carolina, Chapel Hill (SMN, KMR)
| | | | - John W Ayers
- Graduate School of Public Health, San Diego State University, San Diego, California (JWA)
| | - Diane B Francis
- School of Journalism and Mass Communication, University of North Carolina, Chapel Hill (SMN, DBF)
| | - Kurt M Ribisl
- Lineberger Comprehensive Cancer Center, University of North Carolina, Chapel Hill (SMN, KMR),Department of Health Behavior, Gillings School of Global Public Health, University of North Carolina, Chapel Hill (KMR)
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Scherm H, Thomas CS, Garrett KA, Olsen JM. Meta-analysis and other approaches for synthesizing structured and unstructured data in plant pathology. Annu Rev Phytopathol 2014; 52:453-76. [PMID: 25001455 DOI: 10.1146/annurev-phyto-102313-050214] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/12/2023]
Abstract
The term data deluge is used widely to describe the rapidly accelerating growth of information in the technical literature, in scientific databases, and in informal sources such as the Internet and social media. The massive volume and increased complexity of information challenge traditional methods of data analysis but at the same time provide unprecedented opportunities to test hypotheses or uncover new relationships via mining of existing databases and literature. In this review, we discuss analytical approaches that are beginning to be applied to help synthesize the vast amount of information generated by the data deluge and thus accelerate the pace of discovery in plant pathology. We begin with a review of meta-analysis as an established approach for summarizing standardized (structured) data across the literature. We then turn to examples of synthesizing more complex, unstructured data sets through a range of data-mining approaches, including the incorporation of 'omics data in epidemiological analyses. We conclude with a discussion of methodologies for leveraging information contained in novel, open-source data sets through web crawling, text mining, and social media analytics, primarily in the context of digital disease surveillance. Rapidly evolving computational resources provide platforms for integrating large and complex data sets, motivating research that will draw on new types and scales of information to address big questions.
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Affiliation(s)
- H Scherm
- Department of Plant Pathology, University of Georgia, Athens, Georgia 30602;
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