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Novel Epidemiological Tools for Disease Control and Prevention in Food...
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Novel Epidemiological Tools for Disease Control and Prevention in Food Animals

A special issue of Animals (ISSN 2076-2615). This special issue belongs to the section "Animal System and Management".

Deadline for manuscript submissions: 30 September 2024 | Viewed by 2640

Special Issue Editor

Dr. Laura Huber

E-Mail Website
Guest Editor
Department of Pathobiology, College of Veterinary Medicine, Auburn University, Auburn, AL 36849, USA
Interests: surveillance; machine-learning; forecasting models; surveys; sequencing database; big data analysis; environmental sampling; microbiology; parasitology; antimicrobial resistance; emerging infectious diseases; economy
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We would like to invite you to contribute a paper to be included in the Special Issue “Novel Epidemiological Tools for Disease Control and Prevention in Food Animals”. This Special Issue will focus on novel epidemiological tools to rapidly identify, contain, and predict diseases in food animals. We aim to find applicable tools to improve the economy; reduce contamination and waste; reduce the spread of disease between animals, humans and the environment; and enhance animal welfare. The issue will build from the existing literature to enhance the applicability of methods in the field.

We hope that you accept this offer and help us build a Special Issue that will enhance animal production and welfare. Your valuable contribution will be deeply appreciated.

Dr. Laura Huber
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 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. Animals 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

  • surveillance
  • machine learning
  • forecasting models
  • surveys
  • sequencing database
  • big data analysis
  • environmental sampling
  • microbiology
  • parasitology
  • antimicrobial resistance
  • emerging infectious diseases
  • economy

Published Papers (2 papers)

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Research

17 pages, 4027 KiB  
Article
Epidemiology and Scenario Simulations of the Middle East Respiratory Syndrome Corona Virus (MERS-CoV) Disease Spread and Control for Dromedary Camels in United Arab Emirates (UAE)
by Magdi Mohamed Ali, Eihab Fathelrahman, Adil I. El Awad, Yassir M. Eltahir, Raeda Osman, Youssef El-Khatib, Rami H. AlRifai, Mohamed El Sadig, Abdelmalik Ibrahim Khalafalla and Aaron Reeves
Animals 2024, 14(3), 362; https://doi.org/10.3390/ani14030362 - 23 Jan 2024
Viewed by 1049
Abstract
Middle East Respiratory Syndrome (MERS-CoV) is a coronavirus-caused viral respiratory infection initially detected in Saudi Arabia in 2012. In UAE, high seroprevalence (97.1) of MERS-CoV in camels was reported in several Emirate of Abu Dhabi studies, including camels in zoos, public escorts, and [...] Read more.
Middle East Respiratory Syndrome (MERS-CoV) is a coronavirus-caused viral respiratory infection initially detected in Saudi Arabia in 2012. In UAE, high seroprevalence (97.1) of MERS-CoV in camels was reported in several Emirate of Abu Dhabi studies, including camels in zoos, public escorts, and slaughterhouses. The objectives of this research include simulation of MERS-CoV spread using a customized animal disease spread model (i.e., customized stochastic model for the UAE; analyzing the MERS-CoV spread and prevalence based on camels age groups and identifying the optimum control MERS-CoV strategy. This study found that controlling animal mobility is the best management technique for minimizing epidemic length and the number of affected farms. This study also found that disease dissemination differs amongst camels of three ages: camel kids under the age of one, young camels aged one to four, and adult camels aged four and up; because of their immunological state, kids, as well as adults, had greater infection rates. To save immunization costs, it is advised that certain age groups be targeted and that intense ad hoc unexpected vaccinations be avoided. According to the study, choosing the best technique must consider both efficacy and cost. Full article
(This article belongs to the Special Issue Novel Epidemiological Tools for Disease Control and Prevention in Food Animals)
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13 pages, 2417 KiB  
Article
Identification of Pre-Emptive Biosecurity Zone Areas for Highly Pathogenic Avian Influenza Based on Machine Learning-Driven Risk Analysis
by Kwang-Myung Jeon, Jinwoo Jung, Chang-Min Lee and Dae-Sung Yoo
Animals 2023, 13(23), 3728; https://doi.org/10.3390/ani13233728 - 1 Dec 2023
Viewed by 1294
Abstract
Over the last decade, highly pathogenic avian influenza (HPAI) has severely affected poultry production systems across the globe. In particular, massive pre-emptive depopulation of all poultry within a certain distance has raised concerns regarding animal welfare and food security. Thus, alternative approaches to [...] Read more.
Over the last decade, highly pathogenic avian influenza (HPAI) has severely affected poultry production systems across the globe. In particular, massive pre-emptive depopulation of all poultry within a certain distance has raised concerns regarding animal welfare and food security. Thus, alternative approaches to reducing unnecessary depopulation, such as risk-based depopulation, are highly demanded. This paper proposes a data-driven method to generate a rule table and risk score for each farm to identify preventive measures against HPAI. To evaluate the proposed method, 105 cases of HPAI occurring in a total of 381 farms in Jeollanam-do from 2014 to 2023 were evaluated. The accuracy of preventive measure identification was assessed for each case using both the conventional culling method and the proposed data-driven method. The evaluation showed that the proposed method achieved an accuracy of 84.19%, significantly surpassing the previous 10.37%. The result was attributed to the proposed method reducing the false-positive rate by 83.61% compared with the conventional method, thereby enhancing the reliability of identification. The proposed method is expected to be utilized in selecting farms for monitoring and management of HPAI. Full article
(This article belongs to the Special Issue Novel Epidemiological Tools for Disease Control and Prevention in Food Animals)
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