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Mapping the risks of Peste des Petits Ruminants spread in the Republic of Kazakhstan
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  • Sarsenbay Abdrakhmanov,
  • Yersyn Mukhanbetkaliev,
  • Akhmetzhan Sultanov,
  • Gulzhan Yessembekova,
  • Sergey Borovikov,
  • Aidar Namet,
  • Abdykalyk Abishov,
  • Andres Perez,
  • Fedor Korennoy
Sarsenbay Abdrakhmanov
S Seifullin atyndagy Kazak agrotekhnikalyk universiteti
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Yersyn Mukhanbetkaliev
S Seifullin atyndagy Kazak agrotekhnikalyk universiteti
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Akhmetzhan Sultanov
Kazakh Research Veterinary Institute
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Gulzhan Yessembekova
S Seifullin atyndagy Kazak agrotekhnikalyk universiteti
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Sergey Borovikov
S Seifullin atyndagy Kazak agrotekhnikalyk universiteti
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Aidar Namet
Kazakh Research Veterinary Institute
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Abdykalyk Abishov
LTD NPC DiaVak-ABN
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Andres Perez
University of Minnesota
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Fedor Korennoy
Federal Center for Animal Health FGBI 'ARRIAH'
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Abstract

Peste des petits ruminants (PPR) is a viral transboundary disease of small ruminants that causes significant damage to agriculture. The disease has not been previously registered in the Republic of Kazakhstan (RK). This paper presents an assessment of the susceptibility of the RK territory to the spread of this disease in case of its importation from infected countries. Ordinary Least Squares (OLS) and Geographically Weighted Regression (GWR) models trained on the PPR outbreaks in China were used to rank municipal districts of the RK in terms of the risk of PPR spread. Spatial density of outbreaks was used as a risk indicator while a number of socio-economic, landscape and climatic indicators were considered as explanatory variables. The Exploratory Regression tool was used to reveal a best combination of independent variables based on specified thresholds of R-squared, variables’ multicollinearity and residuals’ normality and autocorrelation. The small ruminants’ density, the maximum green vegetation fraction, the annual mean temperature, the road length and density as well as the cattle density were the most significant factors. Both OLS and GWR demonstrated nearly similar model performance providing a global adjusted R-squared of 0.61. Applied to the RK, the models show the greatest risk of PPR spread in the south-eastern and northern regions of the country, especially within Almaty, Zhambyl, Turkistan, West Kazakhstan and East Kazakhstan regions. As part of the study, a country-wise survey was carried out to collect data on the distribution of livestock population the RK, which resulted in compiling a complete geo-database of small ruminants’ holdings in the country. The research results can be used to form a national strategy for the prevention of the importation and spread of PPR in Kazakhstan through targeted monitoring in high-risk areas.

Peer review status:IN REVISION

29 Sep 2020Submitted to Transboundary and Emerging Diseases
29 Sep 2020Submission Checks Completed
29 Sep 2020Assigned to Editor
12 Oct 2020Reviewer(s) Assigned
08 Dec 2020Review(s) Completed, Editorial Evaluation Pending
08 Dec 2020Editorial Decision: Revise Major
30 Mar 20211st Revision Received
30 Mar 2021Submission Checks Completed
30 Mar 2021Assigned to Editor
06 Apr 2021Reviewer(s) Assigned
10 May 2021Review(s) Completed, Editorial Evaluation Pending
12 May 2021Editorial Decision: Revise Minor