Preview

Problems of Particularly Dangerous Infections

Advanced search

The Experience in Using the MaxEnt Model to Rank the Territory of the Caspian Sandy Natural Plague Focus (43) according to the Risk of Epizooty Registration

https://doi.org/10.21055/0370-1069-2024-1-135-140

Abstract

The aim of this work was to rank the territory of the Caspian sandy natural plague focus (43) by the risk of epizooty emergence using the MaxEnt model.

Materials and methods. The archival data on epizootic manifestations of plague over the past 35 years, aggregated by the Stavropol Anti-Plague Institute of the Rospotrebnadzor, the Dagestan, Elista, Astrakhan PCSs of the Rospotrebnadzor, were used for model design. 615 archive plague detection points were converted into the coordinate system (1980–2015). 87 publicly available bioclimatic variables BioClim were deployed to construct the MaxEnt model. Applied weather and climatic factors of the BioClim database are averaged over a multiyear period.

Results and discussion. The MaxEnt model has a very high degree of reliability (AUC=0.975), with a sufficiently high predictive ability (AUC=0.973). According to the generated model, the Caspian sandy natural plague focus has a heterogeneous structure in terms of the probability of epizooty registration and can be divided into five zones. The most significant factors for the model are the following indicators: the average temperature of the wettest quarter, solar radiation in November, the average temperature of the driest quarter, the amount of precipitation in the coldest quarter, wind speed in May, the amount of precipitation in the wettest quarter, and the average air temperature in September. The data obtained allow for targeted search for plague epizootics and can be used to adjust boundaries of a surveyed natural focus in the future.

About the Authors

U. M. Ashibokov
Stavropol Research Anti-Plague Institute
Russian Federation

Umar M. Ashibokov,

13–15, Sovetskaya St., Stavropol, 355035



V. M. Dubyansky
Stavropol Research Anti-Plague Institute
Russian Federation

13–15, Sovetskaya St., Stavropol, 355035



O. V. Semenko
Stavropol Research Anti-Plague Institute
Russian Federation

13–15, Sovetskaya St., Stavropol, 355035



A. Yu. Gazieva
Stavropol Research Anti-Plague Institute
Russian Federation

13–15, Sovetskaya St., Stavropol, 355035



O. A. Belova
Stavropol Research Anti-Plague Institute
Russian Federation

13–15, Sovetskaya St., Stavropol, 355035



A. A. Kes’yan
Dagestan Plague Control Station
Russian Federation

13d, Gagarina St., Makhachkala, 367000



A. Kh. Khalidov
Dagestan Plague Control Station
Russian Federation

13d, Gagarina St., Makhachkala, 367000



A. A. Vetoshkin
Dagestan Plague Control Station
Russian Federation

13d, Gagarina St., Makhachkala, 367000



N. V. Viktorova
Astrakhan Plague Control Station
Russian Federation

3a, Kubanskaya St., Astrakhan, 414000



A. A. Kulik
Elista Plague Control Station
Russian Federation

PO Box 28, Main Post Office, Elista, 358000



References

1. Bekturganova M.B., Litvinenko M.Yu., Makhovykh I.A., Nemilostev N.D., Ponomarenko A.S., Ruder V.P., Sartin S.A., Shokanova D.K., Shchukina V.N. [The main areas of application of remote sensing data in the North Kazakhstan Region]. Aktual’nye Voprosy Sovremennoi Nauki [Relevant Issues of Modern Science]. 2013; (29):34–49.

2. Kitron U., Kazmierczak J.J. Spatial analysis of the distribution of Lyme disease in Wisconsin. Am. J. Epidemiol. 1997; 145(6):558–66. DOI: 10.1093/oxfordjournals.aje.a009145.

3. Thomson M.C., Connor S.J., Milligan P.J., Flasse S.P. The ecology of malaria – as seen from Eart-observation satellites. Ann. Trop. Med. Parasitol. 1996; 90(3):243–64. DOI: 10.1080/00034983.1996.11813050.

4. Hielkama J.U., Roffey J., Tucker C.J. Assessment of ecological conditions associated with the 1980/81 desert locust plague upsurge in West Africa using environmental satellite data. Int. J. Remote Sens. 1986; 7(11):1609–22.

5. Rahman A., Kogan F., Roytman L., Goldberg M., Guo W. Modelling and prediction of malaria vector distribution in Bangladesh from remote-sensing data. Int. J. Remote Sens. 2011. 32(5):1233–51. DOI: 10.1080/01431160903527447.

6. Addink E.A., de Jong S.M., Davis S.A., Dubyanskiy V., Leirs H. Using very high spatial resolution remote sensing to monitor and combat outbreaks of bubonic plague in Kazakhstan. In: Anais XIV Simpósio Brasileiro de Sensoriamento Remoto. Natal, Brasil, 25–30 abril 2009. INPE. P. 7529–36.

7. Burdelov L.A, Dubyansky V.M., Davis S., Addink E.A., de Jong S.M., Ageyev V.S., Leirs H., Stenseth N.C., Begon M., Heier L., Meka-Mechenko V.G., Pole D.S., Sapozhnikov V.I., Alipbaev A.K. [Prospects for the use of remote sensing in plague surveillance]. Karantinnye i Zoonoznye Infektsii v Kazakhstane [Quarantine and Zoonotic Infections in Kazakhstan]. 2007; (1-2):11–7.

8. Dubyansky V.M. [The concept of using GIS technologies and remote sensing in plague surveillance]. Vrach i Informatsionnye Tekhnologii [Information Technologies for the Physician]. 2012; (2):42–6.

9. Rall Yu.M. [Paleogenesis of natural plague foci in connection with the geography of rodent carriers]. In: Voronov A.G., Strautman F.I., editors. [Problems of Zoogeography of the Land]. Lvov: Publishing house of the Lvov University; 1958. P. 216–20.

10. Popova A.Yu., Kutyrev V.V., editors. [Atlas of Natural Plague Foci of Russia and Foreign Countries]. Kaliningrad; “Poligrafych”; 2022. 348 p.

11. Onishchenko G.G., Kutyrev V.V., editors. [Natural Plague Foci in the Territory of Caucasus, Caspian Sea Region, Central Asia and Siberia]. Moscow: “Medicine”; 2004. 191 p.

12. Phillips S.J., Dudík M. Modeling of species distributions with Maxent: new extrension and a comprehensive evaluation. Ecography. 2008; 31(2):161–75. DOI: 10.1111/j.0906-7590.2008.5203.x.

13. Phillips S.J., Anderson R.P., Schapire R.E. Maximum entropy modeling of species geographic distributions. Ecol. Modell. 2006; 190(3-4):231–59. DOI: 10.1016/j.ecolmodel.2005.03.026.

14. Fick S.E., Hijmans R.J. WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas. Int. J. Climatol. 2017; 37(12):4302–15. DOI: 10.1002/joc.5086.

15. Kriticos D.J., Jarošik V., Ota N. Extending the suite of BIOCLIM variables: a proposed registry system and case study using principal components analysis. Methods Ecol. Evol. 2014; 5(9):956– 60. DOI: 10.1111/2041-210X.12244.

16. Araújo M.B., Pearson R.G., Thuiller W., Erhard M. Validation of species-climate impact models under climate change. Glob. Change Biol. 2005; 11(9):1504–13. DOI: 10.1111/j.1365-2486.2005.01000.x.

17. Dubyansky V.M., Khalidov A.Kh. [Ecological and epizootiological differentiation of natural plague foci]. Problemy Osobo Opasnykh Infektsii [Problems of Particularly Dangerous Infections]. 2021; (4):62–6. DOI: 10.21055/0370-1069-2021-4-62-66.


Review

For citations:


Ashibokov U.M., Dubyansky V.M., Semenko O.V., Gazieva A.Yu., Belova O.A., Kes’yan A.A., Khalidov A.Kh., Vetoshkin A.A., Viktorova N.V., Kulik A.A. The Experience in Using the MaxEnt Model to Rank the Territory of the Caspian Sandy Natural Plague Focus (43) according to the Risk of Epizooty Registration. Problems of Particularly Dangerous Infections. 2024;(1):135-140. (In Russ.) https://doi.org/10.21055/0370-1069-2024-1-135-140

Views: 398


Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.


ISSN 0370-1069 (Print)
ISSN 2658-719X (Online)