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Журнал микробиологии, эпидемиологии и иммунобиологии. 2017; : 39-45

ОПЫТ ИСПОЛЬЗОВАНИЯ МЕТОДА МАКСИМАЛЬНОЙ ЭНТРОПИИ (MAXENT) ДЛЯ ЗОНИРОВАНИЯ ТЕРРИТОРИИ ПО РИСКУ ЗАРАЖЕНИЯ ГЛПС НА ПРИМЕРЕ НИЖЕГОРОДСКОЙ ОБЛАСТИ

Солнцев Л. А., Лубянский В. М.

https://doi.org/10.36233/0372-9311-2017-5-39-45

Аннотация

Цель. Зонирование территории Нижегородской области по риску заражения ГЛПС с использованием метода Maxent. Материалы и методы. Материалами являлись данные Центра гигиены и эпидемиологии по Нижегородской области по каждому случаю заражения ГЛПС за 2010 - 2016 гг.; данные по условиям окружающей среды (Bioclim); данные по вегетационной активности (MODIS). Обработка проводилась в пакетах ArcGIS 10.2.2 и Maxent 3.3.Зк. Результаты. Получена и валидирована модель для оценки потенциального риска заражения ГЛПС на территории Нижегородской области. Заключение. Полученные результаты не противоречат фактически наблюдаемой пространственной локализации случаев заражения ГЛПС (точность предсказания составляет более 75%), выявляют связь между пространственной локализацией случаев заражения ГЛПС и сочетания факторов среды и позволяют формировать прогнозы изменения границ потенциально опасных участков при изменении факторов среды.
Список литературы

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.

Journal of microbiology, epidemiology and immunobiology. 2017; : 39-45

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

Solntsev L. A., Dubyansky V. M.

https://doi.org/10.36233/0372-9311-2017-5-39-45

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.
References

1. Dzhiller P. Struktura soobshchestv i ekologicheskaya nisha. M., Mir, 1988.

2. Sanitarno-epidemiologicheskie pravila SP 3.1.7.2614-10 «Profilaktika gemorragicheskoi likhoradki s pochechnym sindromom», utverzhd. Postanovleniem Glavnogo sanitarnogo vracha ot 26 aprelya 2010 g., № 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 (Yu): 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.