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Article ; Online: COVID-19 digital contact tracing worked - heed the lessons for future pandemics.

Salathé, Marcel

2023 Volume 619, Issue 7968, Page(s) 31–33

MeSH term(s)	Humans ; Contact Tracing/methods ; COVID-19/diagnosis ; COVID-19/epidemiology ; COVID-19/transmission ; Mobile Applications ; Pandemics/prevention & control ; Privacy
Language	English
Publishing date	2023-07-03
Publishing country	England
Document type	News
ZDB-ID	120714-3
ISSN	1476-4687 ; 0028-0836
ISSN (online)	1476-4687
ISSN	0028-0836
DOI	10.1038/d41586-023-02130-6
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Article ; Online: Digital epidemiology: what is it, and where is it going?

Salathé, Marcel

Life sciences, society and policy

2018 Volume 14, Issue 1, Page(s) 1

Abstract: Digital Epidemiology is a new field that has been growing rapidly in the past few years, fueled by the increasing availability of data and computing power, as well as by breakthroughs in data analytics methods. In this short piece, I provide an outlook ... ...

Abstract	Digital Epidemiology is a new field that has been growing rapidly in the past few years, fueled by the increasing availability of data and computing power, as well as by breakthroughs in data analytics methods. In this short piece, I provide an outlook of where I see the field heading, and offer a broad and a narrow definition of the term.
MeSH term(s)	Data Collection/methods ; Electronic Health Records ; Epidemiologic Studies ; Humans
Language	English
Publishing date	2018-01-04
Publishing country	England
Document type	Journal Article
ZDB-ID	2711762-5
ISSN	2195-7819 ; 2195-7819
ISSN (online)	2195-7819
ISSN	2195-7819
DOI	10.1186/s40504-017-0065-7
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: COVID-Twitter-BERT: A natural language processing model to analyse COVID-19 content on Twitter.

Müller, Martin / Salathé, Marcel / Kummervold, Per E

Frontiers in artificial intelligence

2023 Volume 6, Page(s) 1023281

Abstract: Introduction: This study presents COVID-Twitter-BERT (CT-BERT), a transformer-based model that is pre-trained on a large corpus of COVID-19 related Twitter messages. CT-BERT is specifically designed to be used on COVID-19 content, particularly from ... ...

Abstract	Introduction: This study presents COVID-Twitter-BERT (CT-BERT), a transformer-based model that is pre-trained on a large corpus of COVID-19 related Twitter messages. CT-BERT is specifically designed to be used on COVID-19 content, particularly from social media, and can be utilized for various natural language processing tasks such as classification, question-answering, and chatbots. This paper aims to evaluate the performance of CT-BERT on different classification datasets and compare it with BERT-LARGE, its base model. Methods: The study utilizes CT-BERT, which is pre-trained on a large corpus of COVID-19 related Twitter messages. The authors evaluated the performance of CT-BERT on five different classification datasets, including one in the target domain. The model's performance is compared to its base model, BERT-LARGE, to measure the marginal improvement. The authors also provide detailed information on the training process and the technical specifications of the model. Results: The results indicate that CT-BERT outperforms BERT-LARGE with a marginal improvement of 10-30% on all five classification datasets. The largest improvements are observed in the target domain. The authors provide detailed performance metrics and discuss the significance of these results. Discussion: The study demonstrates the potential of pre-trained transformer models, such as CT-BERT, for COVID-19 related natural language processing tasks. The results indicate that CT-BERT can improve the classification performance on COVID-19 related content, especially on social media. These findings have important implications for various applications, such as monitoring public sentiment and developing chatbots to provide COVID-19 related information. The study also highlights the importance of using domain-specific pre-trained models for specific natural language processing tasks. Overall, this work provides a valuable contribution to the development of COVID-19 related NLP models.
Language	English
Publishing date	2023-03-14
Publishing country	Switzerland
Document type	Journal Article
ISSN	2624-8212
ISSN (online)	2624-8212
DOI	10.3389/frai.2023.1023281
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Article ; Online: Dynamics of social media behavior before and after SARS-CoV-2 infection.

Durazzi, Francesco / Pichard, François / Remondini, Daniel / Salathé, Marcel

Frontiers in public health

2023 Volume 10, Page(s) 1069931

Abstract: Introduction: Online social media have been both a field of research and a source of data for research since the beginning of the COVID-19 pandemic. In this study, we aimed to determine how and whether the content of tweets by Twitter users reporting ... ...

Abstract	Introduction: Online social media have been both a field of research and a source of data for research since the beginning of the COVID-19 pandemic. In this study, we aimed to determine how and whether the content of tweets by Twitter users reporting SARS-CoV-2 infections changed over time. Methods: We built a regular expression to detect users reporting being infected, and we applied several Natural Language Processing methods to assess the emotions, topics, and self-reports of symptoms present in the timelines of the users. Results: Twelve thousand one hundred and twenty-one twitter users matched the regular expression and were considered in the study. We found that the proportions of health-related, symptom-containing, and emotionally non-neutral tweets increased after users had reported their SARS-CoV-2 infection on Twitter. Our results also show that the number of weeks accounting for the increased proportion of symptoms was consistent with the duration of the symptoms in clinically confirmed COVID-19 cases. Furthermore, we observed a high temporal correlation between self-reports of SARS-CoV-2 infection and officially reported cases of the disease in the largest English-speaking countries. Discussion: This study confirms that automated methods can be used to find digital users publicly sharing information about their health status on social media, and that the associated data analysis may supplement clinical assessments made in the early phases of the spread of emerging diseases. Such automated methods may prove particularly useful for newly emerging health conditions that are not rapidly captured in the traditional health systems, such as the long term sequalae of SARS-CoV-2 infections.
MeSH term(s)	Humans ; COVID-19/epidemiology ; Social Media ; SARS-CoV-2 ; Pandemics ; Social Behavior
Language	English
Publishing date	2023-02-23
Publishing country	Switzerland
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2711781-9
ISSN	2296-2565 ; 2296-2565
ISSN (online)	2296-2565
ISSN	2296-2565
DOI	10.3389/fpubh.2022.1069931
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Article ; Online: Timeliness of online COVID-19 reports from official sources.

Espinosa, Laura / Altunina, Olesia / Salathé, Marcel

Frontiers in public health

2023 Volume 10, Page(s) 1027812

Abstract: Introduction: Making epidemiological indicators for COVID-19 publicly available through websites and social media can support public health experts in the near-real-time monitoring of the situation worldwide, and in the establishment of rapid response ... ...

Abstract	Introduction: Making epidemiological indicators for COVID-19 publicly available through websites and social media can support public health experts in the near-real-time monitoring of the situation worldwide, and in the establishment of rapid response and public health measures to reduce the consequences of the pandemic. Little is known, however, about the timeliness of such sources. Here, we assess the timeliness of official public COVID-19 sources for the WHO regions of Europe and Africa. Methods: We monitored official websites and social media accounts for updates and calculated the time difference between daily updates on COVID-19 cases. We covered a time period of 52 days and a geographic range of 62 countries, 28 from the WHO African region and 34 from the WHO European region. Results: The most prevalent categories were social media updates only (no website reporting) in the WHO African region (32.7% of the 1,092 entries), and updates in both social media and websites in the WHO European region (51.9% of the 884 entries for EU/EEA countries, and 73.3% of the 884 entries for non-EU/EEA countries), showing an overall clear tendency in using social media as an official source to report on COVID-19 indicators. We further show that the time difference for each source group and geographical region were statistically significant in all WHO regions, indicating a tendency to focus on one of the two sources instead of using both as complementary sources. Discussion: Public health communication via social media platforms has numerous benefits, but it is worthwhile to do it in combination with other, more traditional means of communication, such as websites or offline communication.
MeSH term(s)	Humans ; COVID-19/epidemiology ; SARS-CoV-2 ; Pandemics ; Communication ; Europe/epidemiology
Language	English
Publishing date	2023-01-24
Publishing country	Switzerland
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2711781-9
ISSN	2296-2565 ; 2296-2565
ISSN (online)	2296-2565
ISSN	2296-2565
DOI	10.3389/fpubh.2022.1027812
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Article ; Online: Digital Pharmacovigilance and Disease Surveillance: Combining Traditional and Big-Data Systems for Better Public Health.

Salathé, Marcel

The Journal of infectious diseases

2016 Volume 214, Issue suppl_4, Page(s) S399–S403

Abstract: The digital revolution has contributed to very large data sets (ie, big data) relevant for public health. The two major data sources are electronic health records from traditional health systems and patient-generated data. As the two data sources have ... ...

Abstract	The digital revolution has contributed to very large data sets (ie, big data) relevant for public health. The two major data sources are electronic health records from traditional health systems and patient-generated data. As the two data sources have complementary strengths-high veracity in the data from traditional sources and high velocity and variety in patient-generated data-they can be combined to build more-robust public health systems. However, they also have unique challenges. Patient-generated data in particular are often completely unstructured and highly context dependent, posing essentially a machine-learning challenge. Some recent examples from infectious disease surveillance and adverse drug event monitoring demonstrate that the technical challenges can be solved. Despite these advances, the problem of verification remains, and unless traditional and digital epidemiologic approaches are combined, these data sources will be constrained by their intrinsic limits.
MeSH term(s)	Data Collection/methods ; Epidemiological Monitoring ; Humans ; Information Storage and Retrieval/methods ; Pharmacovigilance
Language	English
Publishing date	2016--01
Publishing country	United States
Document type	Journal Article ; Review
ZDB-ID	3019-3
ISSN	1537-6613 ; 0022-1899
ISSN (online)	1537-6613
ISSN	0022-1899
DOI	10.1093/infdis/jiw281
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Article: Rapid and scalable scans at 21 m/s in optical low-coherence reflectometry.

Ballif, J / Gianotti, R / Chavanne, P / Wälti, R / Salathé, R P

Optics letters

2007 Volume 22, Issue 11, Page(s) 757–759

Abstract: ... measurements with a repetitiveness of 384 Hz and a longitudinal scanning speed of 21 m/s in air over a range ...

Abstract	An optical low-coherence reflectometer is presented that uses a fiber Michelson interferometer with a rotating cube to generate rapid depth scans at a high repetition rate. A folded optical path geometry allows the reference arm to scale up the scan range, scan speed, and scan repetitiveness. Thickness measurements with a repetitiveness of 384 Hz and a longitudinal scanning speed of 21 m/s in air over a range of ~3 mm are demonstrated.
Language	English
Publishing date	2007-03-16
Publishing country	United States
Document type	Journal Article
ISSN	0146-9592
ISSN	0146-9592
DOI	10.1364/ol.22.000757
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Article: Crowdbreaks: Tracking Health Trends Using Public Social Media Data and Crowdsourcing.

Müller, Martin M / Salathé, Marcel

Frontiers in public health

2019 Volume 7, Page(s) 81

Abstract: In the past decade, tracking health trends using social media data has shown great promise, due to a powerful combination of massive adoption of social media around the world, and increasingly potent hardware and software that enables us to work with ... ...

Abstract	In the past decade, tracking health trends using social media data has shown great promise, due to a powerful combination of massive adoption of social media around the world, and increasingly potent hardware and software that enables us to work with these new big data streams. At the same time, many challenging problems have been identified. First, there is often a mismatch between how rapidly online data can change, and how rapidly algorithms are updated, which means that there is limited reusability for algorithms trained on past data as their performance decreases over time. Second, much of the work is focusing on specific issues during a specific past period in time, even though public health institutions would need flexible tools to assess multiple evolving situations in real time. Third, most tools providing such capabilities are proprietary systems with little algorithmic or data transparency, and thus little buy-in from the global public health and research community. Here, we introduce Crowdbreaks, an open platform which allows tracking of health trends by making use of continuous crowdsourced labeling of public social media content. The system is built in a way which automatizes the typical workflow from data collection, filtering, labeling and training of machine learning classifiers and therefore can greatly accelerate the research process in the public health domain. This work describes the technical aspects of the platform, thereby covering the functionalities at its current state and exploring its future use cases and extensions.
Language	English
Publishing date	2019-04-12
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2711781-9
ISSN	2296-2565
ISSN	2296-2565
DOI	10.3389/fpubh.2019.00081
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Article ; Online: A complex story of groundwater abstraction and ecological threats to the Doñana National Park World Heritage Site.

Acreman, Mike / Salathe, Tobias

Nature ecology & evolution

2022 Volume 6, Issue 10, Page(s) 1401–1402

MeSH term(s)	Environmental Monitoring ; Groundwater ; Parks, Recreational
Language	English
Publishing date	2022-07-15
Publishing country	England
Document type	Letter
ISSN	2397-334X
ISSN (online)	2397-334X
DOI	10.1038/s41559-022-01836-6
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Article: Response to Elexacaftor/Tezacaftor/Ivacaftor in people with cystic fibrosis with the N1303K mutation: Case report and review of the literature.

Tupayachi Ortiz, Maria G / Baumlin, Nathalie / Yoshida, Makoto / Salathe, Matthias

Heliyon

2024 Volume 10, Issue 5, Page(s) e26955

Abstract: Cystic fibrosis (CF) is caused by a mutation in the cystic fibrosis transmembrane conductance regulator (CFTR) protein. Thousands of CFTR mutations have been identified, but only a fraction are known to cause CF, with the most common being the ... ...

Abstract	Cystic fibrosis (CF) is caused by a mutation in the cystic fibrosis transmembrane conductance regulator (CFTR) protein. Thousands of CFTR mutations have been identified, but only a fraction are known to cause CF, with the most common being the prototypical class II CFTR mutation F508del. Elexacaftor-Tezacaftor-Ivacaftor (ETI) is a CFTR modulator that significantly increases ppFEV1 and reduces exacerbation frequencies. It is indicated for people with CF (pwCF) 2 years or older with at least one copy of F508del or one copy of the other 177 CFTR mutations that are responsive to ETI based on clinical or
Language	English
Publishing date	2024-02-28
Publishing country	England
Document type	Case Reports
ZDB-ID	2835763-2
ISSN	2405-8440
ISSN	2405-8440
DOI	10.1016/j.heliyon.2024.e26955
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