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Book ; Thesis: Untersuchungen zur Wirkdauer und Wirkstärke eines langwirksamen beta 2 -Sympathomimetikums (Salmeterol) im Vergleich mit kurzwirksamen beta 2 -Sympathomimetika (Fenoterol und Salbutamol) bei schweren Formen obstruktiver Atemwegserkrankungen

Vollmer, Michaela

1996

Author's details	vorgelegt von Michaela Vollmer
Language	German
Size	V, 156 Bl. : graph. Darst.
Document type	Book ; Thesis
Thesis / German Habilitation thesis	Bochum, Univ., Diss., 1997
HBZ-ID	HT007530051
Database	Catalogue ZB MED Medicine, Health

In stock of ZB MED Cologne/Königswinter

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Article ; Online: Quantifying Changes in Vaccine Coverage in Mainstream Media as a Result of the COVID-19 Outbreak: Text Mining Study.

Christensen, Bente / Laydon, Daniel / Chelkowski, Tadeusz / Jemielniak, Dariusz / Vollmer, Michaela / Bhatt, Samir / Krawczyk, Konrad

JMIR infodemiology

2022 Volume 2, Issue 2, Page(s) e35121

Abstract: Background: Achieving herd immunity through vaccination depends upon the public's acceptance, which in turn relies on their understanding of its risks and benefits. The fundamental objective of public health messaging on vaccines is therefore the clear ... ...

Abstract	Background: Achieving herd immunity through vaccination depends upon the public's acceptance, which in turn relies on their understanding of its risks and benefits. The fundamental objective of public health messaging on vaccines is therefore the clear communication of often complex information and, increasingly, the countering of misinformation. The primary outlet shaping public understanding is mainstream online news media, where coverage of COVID-19 vaccines was widespread. Objective: We used text-mining analysis on the front pages of mainstream online news to quantify the volume and sentiment polarization of vaccine coverage. Methods: We analyzed 28 million articles from 172 major news sources across 11 countries between July 2015 and April 2021. We employed keyword-based frequency analysis to estimate the proportion of overall articles devoted to vaccines. We performed topic detection using BERTopic and named entity recognition to identify the leading subjects and actors mentioned in the context of vaccines. We used the Vader Python module to perform sentiment polarization quantification of all collated English-language articles. Results: The proportion of front-page articles mentioning vaccines increased from 0.1% to 4% with the outbreak of COVID-19. The number of negatively polarized articles increased from 6698 in 2015-2019 to 28,552 in 2020-2021. However, overall vaccine coverage before the COVID-19 pandemic was slightly negatively polarized (57% negative), whereas coverage during the pandemic was positively polarized (38% negative). Conclusions: Throughout the pandemic, vaccines have risen from a marginal to a widely discussed topic on the front pages of major news outlets. Mainstream online media has been positively polarized toward vaccines, compared with mainly negative prepandemic vaccine news. However, the pandemic was accompanied by an order-of-magnitude increase in vaccine news that, due to low prepandemic frequency, may contribute to a perceived negative sentiment. These results highlight important interactions between the volume of news and overall polarization. To the best of our knowledge, our work is the first systematic text mining study of front-page vaccine news headlines in the context of COVID-19.
Language	English
Publishing date	2022-09-20
Publishing country	Canada
Document type	Journal Article
ISSN	2564-1891
ISSN (online)	2564-1891
DOI	10.2196/35121
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article: Report 20: Using Mobility to Estimate the Transmission Intensity of COVID-19 in Italy: A Subnational Analysis with Future Scenarios

Vollmer, Michaela Mishra Swapnil Unwin Juliette Gandy Axel Mellan Thomas A. / Imperial College London, https www imperial ac uk

Abstract: From the Introduction: Following the emergence of a novel coronavirus (SARS-CoV-2) and its spread outside of China, Italy was the first European country to be hit by COVID-19 [coronavirus disease 2019] [ ] In this report we analyse the incidence of death ...

Abstract	From the Introduction: Following the emergence of a novel coronavirus (SARS-CoV-2) and its spread outside of China, Italy was the first European country to be hit by COVID-19 [coronavirus disease 2019] [ ] In this report we analyse the incidence of death reported across the 20 Italian regions, and along with the observed relative changes in regional movement, assess how interventions have impacted the transmissibility of SARS-CoV-2 We provide estimates of the number of deaths averted by the implementation of the control measures, the expected proportion of population infected (as of 1st May 2020), and explore the potential impact that the relaxation of the current interventions could have on disease transmission in the future Understanding what impact the relaxation of the currently implemented NPIs ('exit strategies') will have on transmission is critical in guiding policy decisions to manage the transmission of COVID-19 in the so-called 'Phase 2' COVID-19 (Disease);Epidemics
Keywords	covid19
Publisher	WHO
Document type	Article
Note	WHO #Covidence: #740912
Database	COVID19

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Article: Report 29: The Impact of the COVID-19 Epidemic on All-Cause Attendances to Emergency Departments in Two Large London Hospitals: An Observational Study

Vollmer, Michaela Kont Mara D. / Flaxman, Seth Radhakrishnan Sreejith Bhatt Samir Imperial College London https www imperial ac uk

Abstract: From the Introduction: To tackle the COVID-19 [coronavirus disease 2019] epidemic, fundamental changes to the provision of health and social services have been instituted in England As a result, the NHS [National Health Service] undertook an ... ...

Abstract	From the Introduction: To tackle the COVID-19 [coronavirus disease 2019] epidemic, fundamental changes to the provision of health and social services have been instituted in England As a result, the NHS [National Health Service] undertook an unprecedented rearrangement of their resources, with specific measures including the postponing of non-urgent elective procedures and video-triaging patients for referral to hospital services [ ] Perhaps largely as a result of the widespread implementation of non-pharmaceutical interventions in England (and elsewhere), the country has seen a steady reduction in the daily number of COVID-19 cases and deaths However, national data show that the number of attendances to accident and emergency (ED) services (i e consultant-led, 24-hour services including resuscitation units) have decreased nationally by approximately 50% across all England regions Moreover, concerns have emerged that attendances to such emergency services remain low, even as the COVID-19 cases have dropped [ ] In this report, we use administrative patient level clinical hospital records from two large London hospitals from Imperial College Healthcare NHS Trust to analyse trends in attendances to ED departments and emergency admissions pre- and post-implementation of lock-down policies in England Hospitals--Emergency services;COVID-19 (Disease);Public health
Keywords	covid19
Publisher	WHO
Document type	Article
Note	WHO #Covidence: #740910
Database	COVID19

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Book ; Online: Predicting Crop Yield With Machine Learning

Pathak, Deepak / Miranda, Miro / Mena, Francisco / Sanchez, Cristhian / Helber, Patrick / Bischke, Benjamin / Habelitz, Peter / Najjar, Hiba / Siddamsetty, Jayanth / Arenas, Diego / Vollmer, Michaela / Charfuelan, Marcela / Nuske, Marlon / Dengel, Andreas

An Extensive Analysis Of Input Modalities And Models On a Field and sub-field Level

2023

Abstract: We introduce a simple yet effective early fusion method for crop yield prediction that handles multiple input modalities with different temporal and spatial resolutions. We use high-resolution crop yield maps as ground truth data to train crop and ... ...

Abstract	We introduce a simple yet effective early fusion method for crop yield prediction that handles multiple input modalities with different temporal and spatial resolutions. We use high-resolution crop yield maps as ground truth data to train crop and machine learning model agnostic methods at the sub-field level. We use Sentinel-2 satellite imagery as the primary modality for input data with other complementary modalities, including weather, soil, and DEM data. The proposed method uses input modalities available with global coverage, making the framework globally scalable. We explicitly highlight the importance of input modalities for crop yield prediction and emphasize that the best-performing combination of input modalities depends on region, crop, and chosen model. Comment: 4 pages, 1 figure, 3 tables, IEEE IGARSS 2023
Keywords	Computer Science - Computer Vision and Pattern Recognition ; Computer Science - Artificial Intelligence ; Computer Science - Machine Learning ; ACM-class: J.2
Subject code	004
Publishing date	2023-08-17
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article: Is the cure really worse than the disease? The health impacts of lockdowns during COVID-19.

Meyerowitz-Katz, Gideon / Bhatt, Samir / Ratmann, Oliver / Brauner, Jan Markus / Flaxman, Seth / Mishra, Swapnil / Sharma, Mrinank / Mindermann, Sören / Bradley, Valerie / Vollmer, Michaela / Merone, Lea / Yamey, Gavin

BMJ global health

2021 Volume 6, Issue 8

MeSH term(s)	COVID-19 ; Communicable Disease Control ; Humans ; Quarantine ; SARS-CoV-2
Language	English
Publishing date	2021-07-19
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ISSN	2059-7908
ISSN	2059-7908
DOI	10.1136/bmjgh-2021-006653
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: A unified machine learning approach to time series forecasting applied to demand at emergency departments.

Vollmer, Michaela A C / Glampson, Ben / Mellan, Thomas / Mishra, Swapnil / Mercuri, Luca / Costello, Ceire / Klaber, Robert / Cooke, Graham / Flaxman, Seth / Bhatt, Samir

BMC emergency medicine

2021 Volume 21, Issue 1, Page(s) 9

Abstract: Background: There were 25.6 million attendances at Emergency Departments (EDs) in England in 2019 corresponding to an increase of 12 million attendances over the past ten years. The steadily rising demand at EDs creates a constant challenge to provide ... ...

Abstract	Background: There were 25.6 million attendances at Emergency Departments (EDs) in England in 2019 corresponding to an increase of 12 million attendances over the past ten years. The steadily rising demand at EDs creates a constant challenge to provide adequate quality of care while maintaining standards and productivity. Managing hospital demand effectively requires an adequate knowledge of the future rate of admission. We develop a novel predictive framework to understand the temporal dynamics of hospital demand. Methods: We compare and combine state-of-the-art forecasting methods to predict hospital demand 1, 3 or 7 days into the future. In particular, our analysis compares machine learning algorithms to more traditional linear models as measured in a mean absolute error (MAE) and we consider two different hyperparameter tuning methods, enabling a faster deployment of our models without compromising performance. We believe our framework can readily be used to forecast a wide range of policy relevant indicators. Results: We find that linear models often outperform machine learning methods and that the quality of our predictions for any of the forecasting horizons of 1, 3 or 7 days are comparable as measured in MAE. Our approach is able to predict attendances at these emergency departments one day in advance up to a mean absolute error of ±14 and ±10 patients corresponding to a mean absolute percentage error of 6.8% and 8.6% respectively. Conclusions: Simple linear methods like generalized linear models are often better or at least as good as ensemble learning methods like the gradient boosting or random forest algorithm. However, though sophisticated machine learning methods are not necessarily better than linear models, they improve the diversity of model predictions so that stacked predictions can be more robust than any single model including the best performing one.
MeSH term(s)	Emergency Service, Hospital ; Forecasting ; Hospitalization ; Humans ; Linear Models ; Machine Learning
Language	English
Publishing date	2021-01-18
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2050431-7
ISSN	1471-227X ; 1471-227X
ISSN (online)	1471-227X
ISSN	1471-227X
DOI	10.1186/s12873-020-00395-y
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Quantifying changes in vaccine coverage in mainstream media as a result of COVID-19 outbreak

Christensen, Bente / Laydon, Daniel J / Chelkowski, Tadeusz / Jemielniak, Dariusz / Vollmer, Michaela / Bhatt, Samir / Krawczyk, Konrad

medRxiv

Abstract: Background: Achieving vaccine-derived herd immunity depends on public acceptance of vaccination, which in turn relies on people9s understanding of its risks and benefits. The fundamental objective of public health messaging on vaccines is therefore the ... ...

Abstract	Background: Achieving vaccine-derived herd immunity depends on public acceptance of vaccination, which in turn relies on people9s understanding of its risks and benefits. The fundamental objective of public health messaging on vaccines is therefore the clear and concise communication of often complex information, and increasingly the countering of misinformation. The primary outlet shaping societal understanding is the mainstream online news media. There was widespread media coverage of the multiple vaccines that were rapidly developed in response to COVID-19. We studied vaccine coverage on the front pages of mainstream online news, using text-mining analysis to quantify the amount of information and sentiment polarization of vaccine coverage delivered to readers. Methods: We analyzed 28 million articles from 172 major news sources, across 11 countries between July 2015 and April 2021. We employed keyword-based frequency analysis to estimate the proportion of coverage given to vaccines in our dataset. We performed topic detection using BERTopic and Named Entity Recognition to identify the leading subjects and actors mentioned in the context of vaccines. We used the Vader Python module to perform sentiment polarization quantification of all our English-language articles. Results: We find that the proportion of headlines mentioning vaccines on the front pages of international major news sites increased from 0.1% to 3.8% with the outbreak of COVID-19. The absolute number of negatively polarized articles increased from a total of 6,698 before the COVID-19 outbreak 2015-2019 compared to 28,552 in 2020-2021. Overall, however, before the COVID-19 pandemic, vaccine coverage was slightly negatively polarized (57% negative) whereas with the outbreak, the coverage was primarily positively polarized (38% negative). Conclusions: Because of COVID-19, vaccines have risen from a marginal topic to a widely discussed topic on the front pages of major news outlets. Despite a perceived rise in hesitancy, the mainstream online media, i.e. the primary information source to most individuals, has been strongly positive compared to pre-pandemic vaccine news, which was mainly negative. However, the pandemic was accompanied with an order of magnitude increase in vaccine news volume that due to pre-pandemic low frequency sampling bias may contribute to a perceived negative sentiment. These results highlight the important interactions between the volume of news and overall polarisation. To the best of our knowledge, our work is the first systematic text mining study of vaccines in the context of COVID-19.
Keywords	covid19
Language	English
Publishing date	2021-11-11
Publisher	Cold Spring Harbor Laboratory Press
Document type	Article ; Online
DOI	10.1101/2021.11.07.21266018
Database	COVID19

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Article ; Online: The impact of the COVID-19 pandemic on patterns of attendance at emergency departments in two large London hospitals: an observational study.

Vollmer, Michaela A C / Radhakrishnan, Sreejith / Kont, Mara D / Flaxman, Seth / Bhatt, Samir / Costelloe, Ceire / Honeyford, Kate / Aylin, Paul / Cooke, Graham / Redhead, Julian / Sanders, Alison / Mangan, Helen / White, Peter J / Ferguson, Neil / Hauck, Katharina / Nayagam, Shevanthi / Perez-Guzman, Pablo N

BMC health services research

2021 Volume 21, Issue 1, Page(s) 1008

Abstract: Background: Hospitals in England have undergone considerable change to address the surge in demand imposed by the COVID-19 pandemic. The impact of this on emergency department (ED) attendances is unknown, especially for non-COVID-19 related emergencies.! ...

Abstract	Background: Hospitals in England have undergone considerable change to address the surge in demand imposed by the COVID-19 pandemic. The impact of this on emergency department (ED) attendances is unknown, especially for non-COVID-19 related emergencies. Methods: This analysis is an observational study of ED attendances at the Imperial College Healthcare NHS Trust (ICHNT). We calibrated auto-regressive integrated moving average time-series models of ED attendances using historic (2015-2019) data. Forecasted trends were compared to present year ICHNT data for the period between March 12, 2020 (when England implemented the first COVID-19 public health measure) and May 31, 2020. We compared ICHTN trends with publicly available regional and national data. Lastly, we compared hospital admissions made via the ED and in-hospital mortality at ICHNT during the present year to the historic 5-year average. Results: ED attendances at ICHNT decreased by 35% during the period after the first lockdown was imposed on March 12, 2020 and before May 31, 2020, reflecting broader trends seen for ED attendances across all England regions, which fell by approximately 50% for the same time frame. For ICHNT, the decrease in attendances was mainly amongst those aged < 65 years and those arriving by their own means (e.g. personal or public transport) and not correlated with any of the spatial dependencies analysed such as increasing distance from postcode of residence to the hospital. Emergency admissions of patients without COVID-19 after March 12, 2020 fell by 48%; we did not observe a significant change to the crude mortality risk in patients without COVID-19 (RR 1.13, 95%CI 0.94-1.37, p = 0.19). Conclusions: Our study findings reflect broader trends seen across England and give an indication how emergency healthcare seeking has drastically changed. At ICHNT, we find that a larger proportion arrived by ambulance and that hospitalisation outcomes of patients without COVID-19 did not differ from previous years. The extent to which these findings relate to ED avoidance behaviours compared to having sought alternative emergency health services outside of hospital remains unknown. National analyses and strategies to streamline emergency services in England going forward are urgently needed.
MeSH term(s)	COVID-19 ; Communicable Disease Control ; Emergency Service, Hospital ; Hospitals ; Humans ; London ; Pandemics ; Retrospective Studies ; SARS-CoV-2
Language	English
Publishing date	2021-09-23
Publishing country	England
Document type	Journal Article ; Observational Study
ZDB-ID	2050434-2
ISSN	1472-6963 ; 1472-6963
ISSN (online)	1472-6963
ISSN	1472-6963
DOI	10.1186/s12913-021-07008-9
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Estimating the effects of non-pharmaceutical interventions on COVID-19 in Europe.

Flaxman, Seth / Mishra, Swapnil / Gandy, Axel / Unwin, H Juliette T / Mellan, Thomas A / Coupland, Helen / Whittaker, Charles / Zhu, Harrison / Berah, Tresnia / Eaton, Jeffrey W / Monod, Mélodie / Ghani, Azra C / Donnelly, Christl A / Riley, Steven / Vollmer, Michaela A C / Ferguson, Neil M / Okell, Lucy C / Bhatt, Samir

Nature

2020 Volume 584, Issue 7820, Page(s) 257–261

Abstract: Following the detection of the new ... ...

Abstract	Following the detection of the new coronavirus
MeSH term(s)	Basic Reproduction Number ; COVID-19 ; Coronavirus Infections/epidemiology ; Coronavirus Infections/mortality ; Coronavirus Infections/prevention & control ; Coronavirus Infections/transmission ; Europe/epidemiology ; Humans ; Pandemics/prevention & control ; Pneumonia, Viral/epidemiology ; Pneumonia, Viral/mortality ; Pneumonia, Viral/prevention & control ; Pneumonia, Viral/transmission
Keywords	covid19
Language	English
Publishing date	2020-06-08
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	120714-3
ISSN	1476-4687 ; 0028-0836
ISSN (online)	1476-4687
ISSN	0028-0836
DOI	10.1038/s41586-020-2405-7
Database	MEDical Literature Analysis and Retrieval System OnLINE

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