Special Issue "Data-Driven Modelling of Infectious Diseases"

A special issue of Data (ISSN 2306-5729). This special issue belongs to the section "Computational Biology, Bioinformatics, and Biomedical Data Science".

Deadline for manuscript submissions: closed (30 June 2021).

Special Issue Editor

Dr. Matloob Khushi
E-Mail Website
Guest Editor
School of Computer Science, The University of Sydney, NSW 2006 Australia
Interests: Machine Learning; Bioinformatics

Special Issue Information

Dear Colleagues,

The frequent occurrence of infectious diseases continues to presents substantial challenges to public health under global urbanization. How to improve the surveillance systems, transmission and control for an emerging infectious disease under the condition of urbanization has become an urgent problem. Applying mathematical modelling in the epidemics spread was first investigated in 1927. Although there is no guarantee that the current mathematical models are completely consistent with the actual propagation process, the extremely close and accurate simulation is still of great help. This could guide policy makers on adjusting measures to reduce the risk of bigger outbreaks.

Topics of the research papers for this Special Issue include but are not limited to: data-driven models for forecasting and controlling of the epidemics; visualization and analytics tools that overcome cost and resource barriers to achieve data needs; coordinating on data sharing among groups; statistical or AI-driven models to control or contain outbreaks.

Dr. Matloob Khushi
Guest Editor

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 papers will be 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 1400 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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Research

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Article
The Hierarchical Classifier for COVID-19 Resistance Evaluation
Data 2021, 6(1), 6; https://doi.org/10.3390/data6010006 - 15 Jan 2021
Cited by 1 | Viewed by 1087
Abstract
Finding dependencies in the data requires the analysis of relations between dozens of parameters of the studied process and hundreds of possible sources of influence on this process. Dependencies are nondeterministic and therefore modeling requires the use of statistical methods for analyzing random [...] Read more.
Finding dependencies in the data requires the analysis of relations between dozens of parameters of the studied process and hundreds of possible sources of influence on this process. Dependencies are nondeterministic and therefore modeling requires the use of statistical methods for analyzing random processes. Part of the information is often hidden from observation or not monitored. That is why many difficulties have arisen in the process of analyzing the collected information. The paper aims to find frequent patterns and parameters affected by COVID-19. The novelty of the paper is hierarchical architecture comprises supervised and unsupervised methods. It allows the development of an ensemble of the methods based on k-means clustering and classification. The best classifiers from the ensemble are random forest with 500 trees and XGBoost. Classification for separated clusters gives us higher accuracy on 4% in comparison with dataset analysis. The proposed approach can be used also for personalized medicine decision support in other domains. The features selection allows us to analyze the following features with the highest impact on COVID-19: age, sex, blood group, had influenza. Full article
(This article belongs to the Special Issue Data-Driven Modelling of Infectious Diseases)
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Article
Non-Spatial Data towards Spatially Located News about COVID-19: A Semi-Automated Aggregator of Pandemic Data from (Social) Media within the Olomouc Region, Czechia
Data 2020, 5(3), 76; https://doi.org/10.3390/data5030076 - 30 Aug 2020
Viewed by 1111
Abstract
The article describes the process of aggregation of media-based data about the coronavirus pandemic in the Olomouc region, the Czech Republic. Originally non-spatially located news from different sources and various platforms (government, social media, news portals) were automatically aggregated into a centralized database. [...] Read more.
The article describes the process of aggregation of media-based data about the coronavirus pandemic in the Olomouc region, the Czech Republic. Originally non-spatially located news from different sources and various platforms (government, social media, news portals) were automatically aggregated into a centralized database. The application “COVID-map” is an interactive web map solution which visualizes records from the database in a spatial way. The COVID-map has been developed within the Ad hoc online hackathon as an academic project at the Department of Geoinformatics, Palacký University Olomouc, Czech Republic. Alongside spatially localized data, the map application collects statistical data from official sources e.g., from the governmental crisis management office. The impact of the application was immediate. Within a few days after the launch, tens of thousands users per day visited the COVID-map. It has been published by regional and national media. The COVID-map solution could be considered as a suitable implementation of the correctly used cartographical method for the example of the coronavirus pandemic. Full article
(This article belongs to the Special Issue Data-Driven Modelling of Infectious Diseases)
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Review

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Review
SARS-CoV-2 Persistence: Data Summary up to Q2 2020
Data 2020, 5(3), 81; https://doi.org/10.3390/data5030081 - 09 Sep 2020
Cited by 18 | Viewed by 1417
Abstract
The coronavirus pandemic is causing confusion in the world. This confusion also affects the different guidelines adopted by each country. The persistence of Coronavirus, responsible for coronavirus disease 2019 (Covid-19) has been evaluated by different articles, but it is still not well-defined, and [...] Read more.
The coronavirus pandemic is causing confusion in the world. This confusion also affects the different guidelines adopted by each country. The persistence of Coronavirus, responsible for coronavirus disease 2019 (Covid-19) has been evaluated by different articles, but it is still not well-defined, and the method of diffusion is unclear. The aim of this manuscript is to underline new Coronavirus persistence features on different environments and surfaces. The scientific literature is still poor on this topic and research is mainly focused on therapy and diagnosis, rather than the characteristics of the virus. These data could be an aid to summarize virus features and formulate new guidelines and anti-spread strategies. Full article
(This article belongs to the Special Issue Data-Driven Modelling of Infectious Diseases)
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Other

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Data Descriptor
A State-Level Socioeconomic Data Collection of the United States for COVID-19 Research
Data 2020, 5(4), 118; https://doi.org/10.3390/data5040118 - 11 Dec 2020
Cited by 2 | Viewed by 1686
Abstract
The outbreak of COVID-19 from late 2019 not only threatens the health and lives of humankind but impacts public policies, economic activities, and human behavior patterns significantly. To understand the impact and better prepare for future outbreaks, socioeconomic factors play significant roles in [...] Read more.
The outbreak of COVID-19 from late 2019 not only threatens the health and lives of humankind but impacts public policies, economic activities, and human behavior patterns significantly. To understand the impact and better prepare for future outbreaks, socioeconomic factors play significant roles in (1) determinant analysis with health care, environmental exposure and health behavior; (2) human mobility analyses driven by policies; (3) economic pressure and recovery analyses for decision making; and (4) short to long term social impact analysis for equity, justice and diversity. To support these analyses for rapid impact responses, state level socioeconomic factors for the United States of America (USA) are collected and integrated into topic-based indicators, including (1) the daily quantitative policy stringency index; (2) dynamic economic indices with multiple time frequency of GDP, international trade, personal income, employment, the housing market, and others; (3) the socioeconomic determinant baseline of the demographic, housing financial situation and medical resources. This paper introduces the measurements and metadata of relevant socioeconomic data collection, along with the sharing platform, data warehouse framework and quality control strategies. Different from existing COVID-19 related data products, this collection recognized the geospatial and dynamic factor as essential dimensions of epidemiologic research and scaled down the spatial resolution of socioeconomic data collection from country level to state level of the USA with a standard data format and high quality. Full article
(This article belongs to the Special Issue Data-Driven Modelling of Infectious Diseases)
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Data Descriptor
An Environmental Data Collection for COVID-19 Pandemic Research
Data 2020, 5(3), 68; https://doi.org/10.3390/data5030068 - 03 Aug 2020
Cited by 10 | Viewed by 2939
Abstract
The COVID-19 viral disease surfaced at the end of 2019 and quickly spread across the globe. To rapidly respond to this pandemic and offer data support for various communities (e.g., decision-makers in health departments and governments, researchers in academia, public citizens), the National [...] Read more.
The COVID-19 viral disease surfaced at the end of 2019 and quickly spread across the globe. To rapidly respond to this pandemic and offer data support for various communities (e.g., decision-makers in health departments and governments, researchers in academia, public citizens), the National Science Foundation (NSF) spatiotemporal innovation center constructed a spatiotemporal platform with various task forces including international researchers and implementation strategies. Compared to similar platforms that only offer viral and health data, this platform views virus-related environmental data collection (EDC) an important component for the geospatial analysis of the pandemic. The EDC contains environmental factors either proven or with potential to influence the spread of COVID-19 and virulence or influence the impact of the pandemic on human health (e.g., temperature, humidity, precipitation, air quality index and pollutants, nighttime light (NTL)). In this platform/framework, environmental data are processed and organized across multiple spatiotemporal scales for a variety of applications (e.g., global mapping of daily temperature, humidity, precipitation, correlation of the pandemic to the mean values of climate and weather factors by city). This paper introduces the raw input data, construction and metadata of reprocessed data, and data storage, as well as the sharing and quality control methodologies of the COVID-19 related environmental data collection. Full article
(This article belongs to the Special Issue Data-Driven Modelling of Infectious Diseases)
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