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Applied Sciences
Special Issues
Wildlife Diseases Worldwide: Understanding and Crisis Response
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Wildlife Diseases Worldwide: Understanding and Crisis Response

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Environmental Sciences".

Deadline for manuscript submissions: closed (20 August 2022) | Viewed by 2755

Special Issue Editors

Prof. Dr. Xiaolong Wang

E-Mail Website
Guest Editor
College of Wildlife and Protected Area, Northeast Forestry University, Harbin 150040, China
Interests: conservation medicine
Dr. Haoning Wang

E-Mail Website
Assistant Guest Editor
School of Geography and Tourism, Harbin University, Harbin 150086, China
Interests: wildlife disease

Special Issue Information

Dear Colleagues,

Wildlife-related reverse zoonoses have been challenging public health, the safety of the livestock industry, and ecology in recent decades. The pathogenesis, epidemiology, and other aspects of these reverse zoonoses are greatly different from those of other diseases and thus call for new studies to develop a deeper understanding. We strive to bridge the gaps in science and quickly report findings on the regulation of wildlife related reverse zoonoses through active information and experience sharing.

The purpose of this Special Issue is therefore to enrich our understanding of this field of science and accelerate the development of effective control measures based on the progress of basic theories and regulation. Particular attention will be given to those studies that provide significant knowledge around all the fields associated with wildlife-related reverse zoonoses.

Prof. Dr. Xiaolong Wang
Dr. Haoning Wang
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. Applied Sciences is an international peer-reviewed open access semimonthly 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 2400 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

  • wildlife diseases
  • zoonosis
  • pathogenesis
  • bio-phylogenesis
  • epidemiology
  • virology
  • parasite

Published Papers (1 paper)

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Research

24 pages, 4259 KiB  
Article
Predictive Model of Lyme Disease Epidemic Process Using Machine Learning Approach
by Dmytro Chumachenko, Pavlo Piletskiy, Marya Sukhorukova and Tetyana Chumachenko
Appl. Sci. 2022, 12(9), 4282; https://0-doi-org.brum.beds.ac.uk/10.3390/app12094282 - 23 Apr 2022
Cited by 7 | Viewed by 2284
Abstract
Lyme disease is the most prevalent tick-borne disease in Eastern Europe. This study focuses on the development of a machine learning model based on a neural network for predicting the dynamics of the Lyme disease epidemic process. A retrospective analysis of the Lyme [...] Read more.
Lyme disease is the most prevalent tick-borne disease in Eastern Europe. This study focuses on the development of a machine learning model based on a neural network for predicting the dynamics of the Lyme disease epidemic process. A retrospective analysis of the Lyme disease cases reported in the Kharkiv region, East Ukraine, between 2010 and 2017 was performed. To develop the neural network model of the Lyme disease epidemic process, a multilayered neural network was used, and the backpropagation algorithm or the generalized delta rule was used for its learning. The adequacy of the constructed forecast was tested on real statistical data on the incidence of Lyme disease. The learning of the model took 22.14 s, and the mean absolute percentage error is 3.79%. A software package for prediction of the Lyme disease incidence on the basis of machine learning has been developed. Results of the simulation have shown an unstable epidemiological situation of Lyme disease, which requires preventive measures at both the population level and individual protection. Forecasting is of particular importance in the conditions of hostilities that are currently taking place in Ukraine, including endemic territories. Full article
(This article belongs to the Special Issue Wildlife Diseases Worldwide: Understanding and Crisis Response)
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