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Data
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Challenges and Perspectives of Open Data in Modelling Infectious Diseases
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Challenges and Perspectives of Open Data in Modelling Infectious Diseases

A special issue of Data (ISSN 2306-5729).

Deadline for manuscript submissions: closed (31 August 2023) | Viewed by 6741

Special Issue Editors

Dr. Giorgia Lodi

E-Mail Website
Guest Editor
Italian National Council of Research (CNR), Rome, Italy
Interests: open (government) data; linked (open) data; knowledge extraction; ontologies development; distributed systems; big data
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. Francesco Branda

E-Mail Website
Guest Editor
Department of Computer Science, Modeling, Electronics and Systems Engineering (DIMES), University of Calabria, 87036 Rende, Italy
Interests: big data analysis; epidemiological modelling; machine learning; social network analysis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The COVID-19 pandemic has shown how important real-world data (RWD) are for informing public health policy decisions and improving clinical trials. Open data and big data analytics, particularly through artificial intelligence (AI) platforms and data visualization tools, can be used for (i) conducting real-time situational analyses, tracking contacts, and making early and timely diagnoses for effective containment; (ii) ensuring public trust in the government through increased transparency and better communication; (iii) countering misinformation; (iv) identifying and addressing the vulnerabilities and specific needs of vulnerable groups by collecting disaggregated data; and (v) supporting the effective management of medical supplies and requests for medical equipment.

The scope of this Special Issue includes, but is not limited to, the following topics:

  • Big data processing and analytics;
  • Big data models, algorithms, and architectures for public health;
  • Big data applications in medicine, healthcare, etc.;
  • Artificial intelligence;
  • Cognitive computing;
  • Cognitive modeling;
  • Cognitive informatics;
  • Real-time surveillance and early detection of emerging diseases;
  • Spatiotemporal prediction of pandemics;
  • Machine learning and its applications for public health;
  • Application of cognitive computing in health monitoring, bioinformatics, etc.

Dr. Giorgia Lodi
Prof. Dr. Rüdiger Pryss
Dr. Francesco Branda
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.

Keywords

  • surveillance
  • machine learning
  • open data
  • predictive modelling
  • public health

Published Papers (3 papers)

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Editorial

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3 pages, 172 KiB  
Editorial
Challenges and Perspectives of Open Data in Modelling Infectious Diseases
by Francesco Branda and Giorgia Lodi
Data 2023, 8(2), 27; https://doi.org/10.3390/data8020027 - 26 Jan 2023
Viewed by 1342
Abstract
The pandemic challenged the scientific community and governments around the world, who were looking for real-time answers but lacked the data or evidence to guide decision-making [...] Full article
(This article belongs to the Special Issue Challenges and Perspectives of Open Data in Modelling Infectious Diseases)

Research

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12 pages, 496 KiB  
Article
A Preliminary Investigation of a Single Shock Impact on Italian Mortality Rates Using STMF Data: A Case Study of COVID-19
by Maria Francesca Carfora and Albina Orlando
Data 2023, 8(6), 107; https://doi.org/10.3390/data8060107 - 13 Jun 2023
Viewed by 1063
Abstract
Mortality shocks, such as pandemics, threaten the consolidated longevity improvements, confirmed in the last decades for the majority of western countries. Indeed, just before the COVID-19 pandemic, mortality was falling for all ages, with a different behavior according to different ages and countries. [...] Read more.
Mortality shocks, such as pandemics, threaten the consolidated longevity improvements, confirmed in the last decades for the majority of western countries. Indeed, just before the COVID-19 pandemic, mortality was falling for all ages, with a different behavior according to different ages and countries. It is indubitable that the changes in the population longevity induced by shock events, even transitory ones, affecting demographic projections, have financial implications in public spending as well as in pension plans and life insurance. The Short Term Mortality Fluctuations (STMF) data series, providing data of all-cause mortality fluctuations by week within each calendar year for 38 countries worldwide, offers a powerful tool to timely analyze the effects of the mortality shock caused by the COVID-19 pandemic on Italian mortality rates. This dataset, recently made available as a new component of the Human Mortality Database, is described and techniques for the integration of its data with the historical mortality time series are proposed. Then, to forecast mortality rates, the well-known stochastic mortality model proposed by Lee and Carter in 1992 is first considered, to be consistent with the internal processing of the Human Mortality Database, where exposures are estimated by the Lee–Carter model; empirical results are discussed both on the estimation of the model coefficients and on the forecast of the mortality rates. In detail, we show how the integration of the yearly aggregated STMF data in the HMD database allows the Lee–Carter model to capture the complex evolution of the Italian mortality rates, including the higher lethality for males and older people, in the years that follow a large shock event such as the COVID-19 pandemic. Finally, we discuss some key points concerning the improvement of existing models to take into account mortality shocks and evaluate their impact on future mortality dynamics. Full article
(This article belongs to the Special Issue Challenges and Perspectives of Open Data in Modelling Infectious Diseases)
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Other

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24 pages, 5572 KiB  
Data Descriptor
A Large-Scale Dataset of Search Interests Related to Disease X Originating from Different Geographic Regions
by Nirmalya Thakur, Shuqi Cui, Kesha A. Patel, Isabella Hall and Yuvraj Nihal Duggal
Data 2023, 8(11), 163; https://doi.org/10.3390/data8110163 - 26 Oct 2023
Cited by 1 | Viewed by 1917
Abstract
The World Health Organization (WHO) added Disease X to their shortlist of blueprint priority diseases to represent a hypothetical, unknown pathogen that could cause a future epidemic. During different virus outbreaks of the past, such as COVID-19, Influenza, Lyme Disease, and Zika virus, [...] Read more.
The World Health Organization (WHO) added Disease X to their shortlist of blueprint priority diseases to represent a hypothetical, unknown pathogen that could cause a future epidemic. During different virus outbreaks of the past, such as COVID-19, Influenza, Lyme Disease, and Zika virus, researchers from various disciplines utilized Google Trends to mine multimodal components of web behavior to study, investigate, and analyze the global awareness, preparedness, and response associated with these respective virus outbreaks. As the world prepares for Disease X, a dataset on web behavior related to Disease X would be crucial to contribute towards the timely advancement of research in this field. Furthermore, none of the prior works in this field have focused on the development of a dataset to compile relevant web behavior data, which would help to prepare for Disease X. To address these research challenges, this work presents a dataset of web behavior related to Disease X, which emerged from different geographic regions of the world, between February 2018 and August 2023. Specifically, this dataset presents the search interests related to Disease X from 94 geographic regions. These regions were chosen for data mining as these regions recorded significant search interests related to Disease X during this timeframe. The dataset was developed by collecting data using Google Trends. The relevant search interests for all these regions for each month in this time range are available in this dataset. This paper also discusses the compliance of this dataset with the FAIR principles of scientific data management. Finally, an analysis of this dataset is presented to uphold the applicability, relevance, and usefulness of this dataset for the investigation of different research questions in the interrelated fields of Big Data, Data Mining, Healthcare, Epidemiology, and Data Analysis with a specific focus on Disease X. Full article
(This article belongs to the Special Issue Challenges and Perspectives of Open Data in Modelling Infectious Diseases)
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