EXPERIENCE OF USING MAXIMAL ENTROPY METHOD (MAXENT) FOR ZONING OF THE TERRITORY BY HERS RISK USING NIZHNY NOVGOROD REGION AS AN EXAMPLE

Cover Page


Cite item

Full Text

Abstract

Aim. Zoning of the territory of Nizhny Novgorod region by risk of HFRS infection using Maxent method. Materials and methods. Data from Centre of Hygiene and Epidemiology in Nizhny Novgorod region for each case of the HFRS for 2010 - 2016, data on environment (Bioclim), data on vegetation activity (MODIS) were used. ArcGIS 10.2.2 and Maxent 3.3.3k packages were used. Results. Model for evaluation of potential risk of HFRS in Nizhny Novgorod was developed and validated. Conclusion. The data obtained do not contradict the observed spatial localization of the cases of HFRS infection (prediction accuracy over 75%), detected connection between spatial localization of HFRS cases and combination of environment factors and allow to predict changes in borders of potentially dangerous segments after environmental changes.

About the authors

L. A. Solntsev

Blokhina Research Institute of Epidemiology and Microbiology

Author for correspondence.
Email: noemail@neicon.ru
Россия

V. M. Dubyansky

Stavropol Institute of Plague Control

Email: noemail@neicon.ru
Россия

References

  1. Джиллер П. Структура сообществ и экологическая ниша. М., Мир, 1988.
  2. Санитарно-эпидемиологические правила СП 3.1.7.2614-10 «Профилактика геморрагической лихорадки с почечным синдромом», утвержд. Постановлением Главного санитарного врача от 26 апреля 2010 г., № 38.
  3. Corsi F., de Leeuw J., Skidmore A. Modeling species distribution with GIS. In: Boitani L., Fuller T. (Eds.). Research techniques in animal ecology. New York, Columbia University Press, 2000, p. 389-434.
  4. Crippen R. E. Calculating the Vegetation Index Faster. Remote Sensing of Environment. 1990. 34: 71-73.
  5. Elith J., Leathwick J.R. Species distribution models: ecological explanation and prediction across space and time. Annual Review of Ecology, Evolution and Systematics. 2009, 40: 677-697.
  6. Elith J., Phillips S. J., Hastie T. et al. A statistical explanation of MaxEnt for ecologists. Diversity and Distributions. 2011,17: 43-57.
  7. Franklin J. Mapping species dist ributions: spatial inference and prediction. Cambridge University Press, 2009.
  8. Gaston A., Garcia-VihasJ.i. Modelling species distributions with penalized logistic regressions: A comparison with maximum entropy models. Ecol. Model. 2011, 222 (13): 2037-2041.
  9. Guisan A., Zimmerman N.E. Predictive habitat distribution models in ecology. Ecol. Model. 2000, 135: 147-186.
  10. Liu H.-N., Gao- L.-D., Chowell G. et al. Time-specific ecologic niche models forecast the risk of haemorrhage fever with renal syndrome in Dongting Lake District, China, 2005-2010. PLOS ONE. 2014, 9 (9): el06839.
  11. L Merow C., Smith M.J., Silander J.A. A practical guide to Maxent for modeling species1 distributions: what it does, and whv inputs and settings matter. Ecography. 2013, 36 (Ю): 1058-1069.
  12. Phillips S.J., Anderson R.P., Schapire R.E. Maximum entropy modeling of species geographic distributions. Ecol. Mod. 2006, 190: 231-259.
  13. Phillips S.J., Dudik M. Modeling of species distributions with Maxent: new extensions and a comprehensive evaluation. Ecography. 2008, 31: 161-175.
  14. Wei L., Qian Q., Wang Z.Q. et al. Using geographic information system-based ecologic niche models to forecast the risk of hantavirus infection in Shandong Province, China. Am. J. Trop. Med. Hyg. Mar. 2011, 84 (3): 497-503.
  15. Zeimes C.B., Olsson G.E., Ahlm C. Modelling zoonotic diseases in humans: comparison of methods for hantavirus in Sweden. Int. J. Health Geogr. 2012, 11: 39.

Supplementary files

Supplementary Files
Action
1. JATS XML

Copyright (c) 2017 Solntsev L.A., Dubyansky V.M.

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

СМИ зарегистрировано Федеральной службой по надзору в сфере связи, информационных технологий и массовых коммуникаций (Роскомнадзор).
Регистрационный номер и дата принятия решения о регистрации СМИ: ПИ № ФС77-75442 от 01.04.2019 г.


This website uses cookies

You consent to our cookies if you continue to use our website.

About Cookies