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Health Informatics in the Age of COVID-19
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Health Informatics in the Age of COVID-19

A special issue of Data (ISSN 2306-5729). This special issue belongs to the section "Information Systems and Data Management".

Deadline for manuscript submissions: closed (30 November 2022) | Viewed by 10676

Special Issue Editors

Prof. Dr. Rüdiger Pryss

E-Mail Website
Guest Editor
Institute of Clinical Epidemiology and Biometry, University of Würzburg, 97070 Würzburg, Germany
Interests: medical informatics; mobile crowdsensing; mHealth; health services research; expert systems; medical data science
Special Issues, Collections and Topics in MDPI journals
Dr. Marc Schickler

E-Mail Website
Guest Editor
Institute for Databases and Information Systems (DBIS), Universität Ulm, Ulm, Germany
Interests: Mobile App Development; Smart Business Process (integration of sensors/context); Mobile business processes
Dr. Felix Beierle

E-Mail Website
Guest Editor
Institute of Clinical Epidemiology and Biometry (ICE-B), University of Würzburg, 97078 Würzburg, Germany
Interests: mobile health (mhealth); mobile applications; medical informatics; just-in-time adaptive interventions (JITAI); data science; Machine learning; ubiquitous computing; Recommender Systems
Prof. Dr. Johannes Schobel

E-Mail Website
Guest Editor
DigiHealth Institute, Neu-Ulm University of Applied Sciences, 89231 Neu-Ulm, Germany
Interests: API design; Web APIs; cloud services; healthcare services; mobile data collection; medical information systems; medical informatics; process management; information systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The advent of COVID-19 has significantly influenced health informatics research. Especially mobile apps have had to be developed in record time, new developments for the healthcare systems have had to be implemented in many countries, while at the same time regulatory requirements have become more stringent in many parts of the world. Above all, the topic of improved interoperability was identified as a key requirement to develop sustainable solutions during the COVID-19 era. Only if data can be quickly evaluated and compared between different institutions and modalities, we are able to technically deal with demands of a pandemic, like COVID-19.

Telemedicine solutions also needed to be improved in many places in a very short time, for example, to provide psychotherapy on at least the same scale as before COVID-19. Furthermore, modern data science methods to cope with the generated volumes of data that were generated during COVID-19 in a comparatively short time were required as well. In particular, offering suitable visualization methods for medical data was a huge demand. For example, COVID-19 dashboards were demanded by healthcare professionals and governmental institutions to monitor intensive care unit cases. Many more examples and fields of health informatics research could be mentioned. Therefore, we would like to use this Special Issue to call for articles on solutions that have emerged specifically during and because of COVID-19.

  • Interoperability (Standards, Solutions, Obstacles, Case Reports, etc.)
  • Digital Processes and Procedures (Process Documentation, Process Optimization, etc.)
  • Application of Artificial Intelligence Methods (Machine Learning, Data-driven Decision Support, etc.)
  • Telemedicine (mHealth, eHealth, etc.)
  • Visualization of Medical Data (Dashboards, etc.)
  • Data Collection in situ (Multi-modal Data Fusion, Ecological Data like Ecological Momentary Assessments, etc.)
  • Data Processing (Smart Devices, Sensors, Data Acquisition, Data Curation, Collection Procedures, ETL, Data Quality, etc.)
  • Data Set Descriptions (including big data sources like Twitter, Facebook etc.)
  • Data Privacy and Protection (including regulatory aspects of different countries, GDPR, etc.)
  • Use Cases (Mental Health, Contact Tracing, etc.)

Prof. Dr. Rüdiger Pryss
Dr. Marc Schickler
Dr. Felix Beierle
Prof. Dr. Johannes Schobel
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Data is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (5 papers)

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24 pages, 3375 KiB  
Article
Forecasting Daily COVID-19 Case Counts Using Aggregate Mobility Statistics
by Bulut Boru and M. Emre Gursoy
Data 2022, 7(11), 166; https://doi.org/10.3390/data7110166 - 20 Nov 2022
Cited by 1 | Viewed by 1844
Abstract
The COVID-19 pandemic has impacted the whole world profoundly. For managing the pandemic, the ability to forecast daily COVID-19 case counts would bring considerable benefit to governments and policymakers. In this paper, we propose to leverage aggregate mobility statistics collected from Google’s Community [...] Read more.
The COVID-19 pandemic has impacted the whole world profoundly. For managing the pandemic, the ability to forecast daily COVID-19 case counts would bring considerable benefit to governments and policymakers. In this paper, we propose to leverage aggregate mobility statistics collected from Google’s Community Mobility Reports (CMRs) toward forecasting future COVID-19 case counts. We utilize features derived from the amount of daily activity in different location categories such as transit stations versus residential areas based on the time series in CMRs, as well as historical COVID-19 daily case and test counts, in forecasting future cases. Our method trains optimized regression models for different countries based on dynamic and data-driven selection of the feature set, regression type, and time period that best fit the country under consideration. The accuracy of our method is evaluated on 13 countries with diverse characteristics. Results show that our method’s forecasts are highly accurate when compared to the real COVID-19 case counts. Furthermore, visual analysis shows that the peaks, plateaus and general trends in case counts are also correctly predicted by our method. Full article
(This article belongs to the Special Issue Health Informatics in the Age of COVID-19)
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17 pages, 5231 KiB  
Article
Mobility and Dissemination of COVID-19 in Portugal: Correlations and Estimates from Google’s Mobility Data
by Nelson Mileu, Nuno M. Costa, Eduarda M. Costa and André Alves
Data 2022, 7(8), 107; https://doi.org/10.3390/data7080107 - 31 Jul 2022
Cited by 6 | Viewed by 2018
Abstract
The spread of the coronavirus disease 2019 (COVID-19) has important links with population mobility. Social interaction is a known determinant of human-to-human transmission of infectious diseases and, in turn, population mobility as a proxy of interaction is of paramount importance to analyze COVID-19 [...] Read more.
The spread of the coronavirus disease 2019 (COVID-19) has important links with population mobility. Social interaction is a known determinant of human-to-human transmission of infectious diseases and, in turn, population mobility as a proxy of interaction is of paramount importance to analyze COVID-19 diffusion. Using mobility data from Google’s Community Reports, this paper captures the association between changes in mobility patterns through time and the corresponding COVID-19 incidence at a multi-scalar approach applied to mainland Portugal. Results demonstrate a strong relationship between mobility data and COVID-19 incidence, suggesting that more mobility is associated with more COVID-19 cases. Methodological procedures can be summarized in a multiple linear regression with a time moving window. Model validation demonstrate good forecast accuracy, particularly when we consider the cumulative number of cases. Based on this premise, it is possible to estimate and predict future evolution of the number of COVID-19 cases using near real-time information of population mobility. Full article
(This article belongs to the Special Issue Health Informatics in the Age of COVID-19)
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Other

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9 pages, 2326 KiB  
Brief Report
An Analysis by State on The Effect of Movement Control Order (MCO) 3.0 Due to COVID-19 on Malaysians’ Mental Health: Evidence from Google Trends
by Nicholas Tze Ping Pang, Assis Kamu, Chong Mun Ho, Walton Wider and Mathias Wen Leh Tseu
Data 2022, 7(11), 163; https://doi.org/10.3390/data7110163 - 17 Nov 2022
Cited by 1 | Viewed by 1823
Abstract
Due to significant social and economic upheavals brought on by the COVID-19 pandemic, there is a great deal of psychological pain. Google Trends data have been seen as a corollary measure to assess population-wide trends via observing trends in search results. Judicious analysis [...] Read more.
Due to significant social and economic upheavals brought on by the COVID-19 pandemic, there is a great deal of psychological pain. Google Trends data have been seen as a corollary measure to assess population-wide trends via observing trends in search results. Judicious analysis of Google Trends data can have both analytical and predictive capacities. This study aimed to compare nation-wide and inter-state trends in mental health before and after the Malaysian Movement Control Order 3.0 (MCO 3.0) commencing 12 May 2021. This was through assessment of two terms, “stress” and “sleep” in both the Malay and English language. Google Trends daily data between March 6 and 31 May in both 2019 and 2021 was obtained, and both series were re-scaled to be comparable. Searches before and after MCO 3.0 in 2021 were compared to searches before and after the same date in 2019. This was carried out using the differences in difference (DiD) method. This ensured that seasonal variations between states were not the source of our findings. We found that DiD estimates, β_3 for “sleep” and “stress” were not significantly different from zero, implying that MCO 3.0 had no effect on psychological distress in all states. Johor was the only state where the DiD estimates β_3 were significantly different from zero for the search topic ‘Tidur’. For the topic ‘Tekanan’, there were two states with significant DiD estimates, β_3, namely Penang and Sarawak. This study hence demonstrates that there are particular state-level differences in Google Trend search terms, which gives an indicator as to states to prioritise interventions and increase surveillance for mental health. In conclusion, Google Trends is a powerful tool to examine larger population-based trends especially in monitoring public health parameters such as population-level psychological distress, which can facilitate interventions. Full article
(This article belongs to the Special Issue Health Informatics in the Age of COVID-19)
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10 pages, 264 KiB  
Data Descriptor
The COLIBAS Study—COVID-19 Lockdown Effects on Mood, Academic Functioning, Alcohol Consumption, and Perceived Immune Fitness: Data from Buenos Aires University Students
by Pauline A. Hendriksen, Pantea Kiani, Agnese Merlo, Analia Karadayian, Analia Czerniczyniec, Silvia Lores-Arnaiz, Gillian Bruce and Joris C. Verster
Data 2022, 7(9), 131; https://doi.org/10.3390/data7090131 - 14 Sep 2022
Cited by 3 | Viewed by 1806
Abstract
A recent study was conducted in the Netherlands to evaluate the impact of the 2019 coronavirus (COVID-19) pandemic and its associated lockdown periods on academic functioning, mood, and health correlates such as alcohol consumption. The study revealed that lockdowns were associated with a [...] Read more.
A recent study was conducted in the Netherlands to evaluate the impact of the 2019 coronavirus (COVID-19) pandemic and its associated lockdown periods on academic functioning, mood, and health correlates such as alcohol consumption. The study revealed that lockdowns were associated with a significantly poorer mood and a reduced perceived immune fitness. Overall, a reduction was seen in alcohol consumption during the lockdown periods. Academic functioning in terms of performance was unaffected; however, a significant reduction in interactions with other students and teachers was reported. There was, however, great variability between students as follows: both an increase and a reduction in alcohol consumption were reported, as well as improvements and poorer academic functioning. The aim of the current online study was to replicate these findings in Argentina. To this extent, a modified version of the survey was conducted among students at the University of Buenos Aires, which was adapted to the local lockdown measures. The survey assessed possible changes in self-reported academic functioning, mood, and health correlates, such as alcohol consumption, perceived immune functioning, and sleep quality compared to before the COVID-19 pandemic. Retrospective assessments were made for four periods, including (1) the period before COVID-19, (2) the first lockdown period (March–December 2020), (3) summer 2021 (January-March 2021, no lockdown), and (4) the second lockdown (from April 2021 to July 2021). This article describes the content of the survey and the corresponding dataset. The survey was completed by 508 participants. Full article
(This article belongs to the Special Issue Health Informatics in the Age of COVID-19)
10 pages, 480 KiB  
Data Descriptor
COVID-19 Lockdown Effects on Mood, Alcohol Consumption, Academic Functioning, and Perceived Immune Fitness: Data from Young Adults in Germany
by Anna Helin Koyun, Pauline A. Hendriksen, Pantea Kiani, Agnese Merlo, Jessica Balikji, Ann-Kathrin Stock and Joris C. Verster
Data 2022, 7(9), 125; https://doi.org/10.3390/data7090125 - 03 Sep 2022
Cited by 6 | Viewed by 1972
Abstract
Recently, a study was conducted in the Netherlands to evaluate the impact of the coronavirus disease (COVID-19) pandemic and its associated lockdown periods on academic functioning, mood, and health correlates, such as alcohol consumption. The Dutch study revealed that lockdowns were associated with [...] Read more.
Recently, a study was conducted in the Netherlands to evaluate the impact of the coronavirus disease (COVID-19) pandemic and its associated lockdown periods on academic functioning, mood, and health correlates, such as alcohol consumption. The Dutch study revealed that lockdowns were associated with significantly poorer mood and reductions in perceived immune fitness. Overall, a reduction in alcohol consumption during lockdown periods was shown. Academic functioning in terms of self-reported performance was unaffected. However, a significant reduction in interactions with other students and teachers was reported. However, there was considerable variability among students; both increases and reductions in alcohol consumption were reported, as well as both improvements and poorer academic functioning during periods of lockdown. The aim of the current online study was to replicate these findings in Germany. To achieve this, a slightly modified version of the survey was administered among young adults (aged 18 to 35 years old) in Germany. The survey assessed possible changes in self-reported academic functioning, mood, and health correlates, such as smoking and alcohol consumption, perceived immune functioning, and sleep quality during periods of lockdown as compared to periods with no lockdowns. Retrospective assessments were made for five periods, including (1) ‘BP’ (the period before the COVID-19 pandemic), (2) ‘L1’ (the first lockdown period, March–May 2020), (3) ‘NL1’ (the first no-lockdown period, summer 2020), (4) ‘L2’ (the second lockdown, November 2020 to May 2021), and (5) ‘NL2’ (the second no-lockdown period, summer 2021). This article describes the content of the survey and the corresponding dataset. The survey was completed by 371 participants. Full article
(This article belongs to the Special Issue Health Informatics in the Age of COVID-19)
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