Geospatial modelling to estimate the territory at risk of establishment of influenza type A in Mexico - An ecological study

Submitted: 11 November 2020
Accepted: 16 March 2021
Published: 14 May 2021
Abstract Views: 1613
PDF: 440
HTML: 25
Publisher's note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

Authors

The aim of this study was to estimate the territory at risk of establishment of influenza type A (EOITA) in Mexico, using geospatial models. A spatial database of 1973 outbreaks of influenza worldwide was used to develop risk models accounting for natural (natural threat), anthropic (man-made) and environmental (combination of the above) transmission. Then, a virus establishment risk model; an introduction model of influenza A developed in another study; and the three models mentioned were utilized using multi-criteria spatial evaluation supported by geographically weighted regression (GWR), receiver operating characteristic analysis and Moran's I. The results show that environmental risk was concentrated along the Gulf and Pacific coasts, the Yucatan Peninsula and southern Baja California. The identified risk for EOITA in Mexico were: 15.6% and 4.8%, by natural and anthropic risk, respectively, while 18.5% presented simultaneous environmental, natural and anthropic risk. Overall, 28.1% of localities in Mexico presented a High/High risk for the establishment of influenza type A (area under the curve=0.923, P<0.001; GWR, r2=0.840, P<0.001; Moran's I =0.79, P<0.001). Hence, these geospatial models were able to robustly estimate those areas susceptible to EOITA, where the results obtained show the relation between the geographical area and the different effects on health. The information obtained should help devising and directing strategies leading to efficient prevention and sound administration of both human and financial resources.

Dimensions

Altmetric

PlumX Metrics

Downloads

Download data is not yet available.

Citations

Aguirre CA, Valdez JR, Sánchez G, Aragón L, Aguirre AI, 2015. Modelling site selection for tree plantation establishment under different decision scenarios. J Tropical J Trop For Sci 27:298-313.
Alkhamis M, Hijmans R, Al-Enezia A, Martínez B, Perea A, 2016. The use of spatial and spatiotem-poral modeling for surveillance of H5N1 highly pathogenic avian influenza in poultry in the Middle East. Avian Dis 60:146-55. DOI: https://doi.org/10.1637/11106-042115-Reg
Artois J, Jiang H, Wang X, Qin Y, Pearcy M, Lai S, Shi Y, Zhang J, Peng Z, Zheng J, He Y, Dhingra MS, von Dobschuetz S, Guo F, Martin V, Kalpravidh W, Claes F, Robinson T, Hay SI, Xiao X, Feng L, Gilbert M, Yu H, 2018. Changing geographic patterns and risk factors for avian influenza A (H7N9) infections in humans, China. Emerg Infect Dis 24:87-94. DOI: https://doi.org/10.3201/eid2401.171393
Belkhiria J, Alkhamis MA, Martínez B, 2016. Application of Species Distribution Modeling for Avi-an Influenza surveillance in the United States considering the North America Migratory Fly-ways. Sci Rep 6:33161. DOI: https://doi.org/10.1038/srep33161
Belkhiria J, Hijmans RJ, Boyce W, Crossley MM, Martinez B, 2018. Identification of high risk areas for avian influenza outbreaks in California using disease distribution models. PLoS One 13:e019082. DOI: https://doi.org/10.1371/journal.pone.0190824
Bi Y, Zhang Z, Liu W, Yin Y, Hong J, Li X, Wang H, Wong G, Chen J, Li Y, Ru W, Gao R, Liu D, Liu Y, Zhou B, Gao GF, Shi W, Lei F, 2015. Highly pathogenic avian influenza A (H5N1) vi-rus struck migratory birds in China in 2015. Sci Rep 5:12986. DOI: https://doi.org/10.1038/srep12986
Bouwstra R, Gonzales JL, de Wit S, Stahl J, Fouchier RA, Elbers AR, 2017. Risk for low patho-genicity avian influenza virus on poultry farms, the Netherlands, 2007-2013. Emerg Infect Dis 23:1510-6. DOI: https://doi.org/10.3201/eid2309.170276
Brundson C, Fotheringham AS, Charlton ME, 1996. Geographically weighted regression: a method for exploring spatial non-stationarity. Geograph Analysis 28:281-98. DOI: https://doi.org/10.1111/j.1538-4632.1996.tb00936.x
Bui CM, Gardner L, MacIntyre CR, Sarkar S, 2017. Correction: influenza A H5N1 and H7N9 in China: a spatial risk analysis. PLoS One 12:e0176903. DOI: https://doi.org/10.1371/journal.pone.0176903
Chen Y, 2020. New framework of Getis-Ord’s indexes associating spatial autocorrelation with inter-action. PLoS One 15:e0236765. DOI: https://doi.org/10.1371/journal.pone.0236765
Cordova-Villalobos JA, Macias AE, Hernandez-Avila M, Dominguez-Cherit G, Lopez-Gatell H, Alpuche-Aranda C, Ponce de León-Rosales S, 2017. The 2009 pandemic in Mexico: Experi-ence and lessons regarding national preparedness policies for seasonal and epidemic influenza. Gac Med Mex 153:102-110.
Egli A, Saalfrank C, Goldman N, Brunner M, Hollensyein Y, Vogel T, Agustin N, WüthricH D, Seth-Smith MBH, Roth E, Syedbasha M, Mueller FN, Vogt D, Bauer J, Amar-Silva N, Meinel MD, Dubis O, Naegele M, Buser A, Nickel HC, Zeller A, Ritz N, Battegay M, Stadler T, Schneider-Silwa R, 2019. Identification of influenza urban transmission patterns by geograph-ical, epidemiological and whole genome sequencing data: protocol for an observational study. BMJ Open 9:e030913. DOI: https://doi.org/10.1136/bmjopen-2019-030913
Elith J, Graham C, Anderson R, Dudík M, Ferrier, Guisan A, Hijmans R, Huettmann F, Leathwick JR, Lehmann A, Li J, Lohmann L, Loiselle B, Manion G, Moritz C, Nakamura M, Nakazawa Y, Overton JM, Peterson AT, Phillips SJ, Richardson K, Scachetti R, Schapire R, Soberón J, Williams S, Wisz M, Zimmermann N, 2006. Novel methods improve prediction of species dis-tributions from occurrence data. Ecography 29:129-51. DOI: https://doi.org/10.1111/j.2006.0906-7590.04596.x
Fang L, Li X, Liu K, Li Y, Yao H, Liang S, Yang Y, Feng Z, Gary G, Cao WC, 2013. Mapping spread and risk of avian influenza A (H7N9) in China. Sci Rep 3:1-8. DOI: https://doi.org/10.1038/srep02722
Fernandes-Matano L, Monroy-Muñoz IE, de León MB, Leal-Herrera YA, Palomec-Nava ID, Ruíz-Pacheco JA, Escobedo-Guajardo BL, Marín-Budip C, Santacruz-Tinoco CE, González-Ibarra J, González-Bonilla CR, Muñoz-Medina JE, 2019. Analysis of influenza data generated by four epidemiological surveillance laboratories in Mexico, 2010-2016. Epidemiol Infect 147:e183. DOI: https://doi.org/10.1017/S0950268819000694
Fick SE, Hijmans RJ, 2017. Worldclim 2: New 1-km spatial resolution climate surfaces for global land areas. Int J Clim 37:4302-15. DOI: https://doi.org/10.1002/joc.5086
Global Consortium for H5N8 and Related Influenza Viruses, 2016. Role for migratory wild birds in the global spread of avian influenza H5N8. Science 354:213-7. DOI: https://doi.org/10.1126/science.aaf8852
Gulyaeva M, Huettmann F, Shestopalov A, Okamatsu M, Matsuno K, Chu D-H, Sakoda Y, Glush-chenko A, Milton E, Bortz E, 2020. Data mining and model-predicting a global disease reser-voir for low-pathogenic Avian Influenza (AI) in the wider pacific rim using big data sets. Sci Rep 10:16817. DOI: https://doi.org/10.1038/s41598-020-73664-2
Harding N, Spinney RE, Prokopenko M, 2020. Phase transitions in spatial connectivity during influ-enza pandemics. Entropy 22:133. DOI: https://doi.org/10.3390/e22020133
Herrick K, Huettmann F, Lindgren M, 2013. A global model of avian influenza prediction in wild birds: the importance of northern regions. Vet Res 44:42. DOI: https://doi.org/10.1186/1297-9716-44-42
Ibarra-Zapata E, Gaytán-Hernández D, Mora Aguilera G, González Castañeda ME, 2019. Escenario de riesgo de introducción de la influenza tipo A en México estimado mediante geointeligencia. Rev Panam Salud Publica 43:e32. DOI: https://doi.org/10.26633/RPSP.2019.32
Li Y-T, Linster M, Mendenhall IH, Su YCF, Smit GJD, 2019. Avian influenza viruses in humans: lessons from past outbreaks. Br Med Bull 00:1-15. DOI: https://doi.org/10.1093/bmb/ldz036
Marsh DK, Sculpher M, Caro JJ, Tervonen T, 2018. The use of MCDA in HTA: great potential, but more effort needed. Value Health 21:394-7. DOI: https://doi.org/10.1016/j.jval.2017.10.001
Mondal B, Das DN, Dolui G, 2015. Modeling spatial variation of explanatory factors of urban ex-pansion of Kolkata: a geographically weighted regression approach. Model Earth Syst Environ 1:29. DOI: https://doi.org/10.1007/s40808-015-0026-1
Moriguchi S, Onuma M, Goka K, 2013. Potential risk map for avian influenza A virus invading Ja-pan. Divers Distrib 19:78-85. DOI: https://doi.org/10.1111/ddi.12006
Olsen B, Munster VJ, Wallensten A, Osterhaus A, Fouchier RA, 2006. Global patterns of influenza A virus in wild birds. Science 312:384-8. DOI: https://doi.org/10.1126/science.1122438
Ord JK, Getis A, 1995. Local spatial autocorrelation statistics: distributional issues and an application. Geograph Analys 27:286-306. DOI: https://doi.org/10.1111/j.1538-4632.1995.tb00912.x
PAHO/WHO (Pan American Health Organization and World Health Organization), 2018. Avian flu. Available from: https://www.paho.org/hq/index.php?option=com_topics&view=article&id=344&Itemid=40932&lang=es Accessed: 23 June 2020.
Parque J, Jinhwa J, Insung A, 2017. Epidemic simulation of H1N1 influenza virus using GIS in South Korea. pp. 58-60 in International Conference on Information and Communication Tech-nology Convergence (ICTC), Jeju, Korea (South). DOI: https://doi.org/10.1109/ICTC.2017.8190942
Peiris M, Cowling BJ, Wu JT, Feng L, Guan Y, Yu H, Leung GM, 2016. Interventions to reduce zoonotic and pandemic risks from avian influenza in Asia. Infect Dis 16:252-8. DOI: https://doi.org/10.1016/S1473-3099(15)00502-2
Pergolizzi JV, LeQuang JA, Taylor R, Wollmuth C, Nalamachu M, Varrassi G, Christo P, Breve F, Magnusson P, 2020. Four pandemics: lessons learned, lessons lost. Signa Vitae 1-5.
Phillips S, Anderson R, Sphapire R, 2006. Maximum entropy modeling of species geographic distri-butions. Ecol Model 190:3-4. DOI: https://doi.org/10.1016/j.ecolmodel.2005.03.026
Prosser DJ, Hungerford LL, Erwin RM, Ottinger MA, Takekawa JY, Newman S, Xiao X, Ellis E, 2016. Spatial modeling of wild bird risk factors for highly pathogenic A(H5N1) avian influenza virus transmission. Avian Dis 60:329-36. DOI: https://doi.org/10.1637/11125-050615-Reg
Secretaría de Agricultura y Desarrollo Rural, 2020. Manual de procedimientos para la prevención, control y erradicación de la influenza aviar de alta patogenicidad (IAAP). Available from: http://www.zoonosis.unam.mx/contenido/m_academico/archivos/Manual_Emergencia_control_erradicacion_Influenza_Aviar_Alta_Patogenicidad.pdf Accessed: February 2020.
Secretaría de Salud México, 2020. Informe semanal de la temporada de influenza estacional 2019-2020 (semana 40 a la 20)/semana 10-2020. Available from: https://www.gob.mx/cms/uploads/attachment/file/539259/INFLUENZA_SE10_2020.pdf Ac-cessed: February 2020.
Silva JJ, Aguirre CA, Miranda L, Sánchez G, Valdéz JR, Pedroza JW, Flores JA, 2017. Locating potential zones for cultivating Stevia rebaudiana in Mexico: weighted linear combination ap-proach. Sugar Tech 2:206-18. DOI: https://doi.org/10.1007/s12355-016-0446-x
Stallknecht DE, Brown JD, 2008. Ecology of avian influenza in wild birds. Chapter 3. In: Swayne DJ (Ed.), Avian influenza. John Wiley & Sons, Inc., New York, NY, USA, pp. 43-58. DOI: https://doi.org/10.1002/9780813818634.ch3
Stenkamp-Strahm C, Patyk K, McCool-Eye MJ, Fox A, Humphreys J, James A, South D, Magza-men S, 2020. Using geospatial methods to measure the risk of environmental persistence of avi-an influenza virus in South Carolina. Spat Spatiotemporal Epidemiol 34:100342. DOI: https://doi.org/10.1016/j.sste.2020.100342
Stevens K, Gilbert M, Pfeiffer D, 2013. Modeling habitat suitability for occurrence of highly patho-genic avian influenza virus H5N1 in domestic poultry in Asia: A spatial multi-criteria decision analysis approach. Spat Spatiotemporal Epidemiol 4:1-14. DOI: https://doi.org/10.1016/j.sste.2012.11.002
Sullivan-Wiley KA, Gianotti AGS, 2017. Risk perception in a multi-hazard environment. World Dev 97:138-52. DOI: https://doi.org/10.1016/j.worlddev.2017.04.002
Tjonâ€Konâ€Fat R, Meerhoff T, Nikisins S, Pires J, Pereyaslov D, Gross D, Brown, WHO European Region Influenza Network, 2016. The potential risks and impact of the start of the 2015-2016 influenza season in the WHO European Region: a rapid risk assessment. Influenza Other Respir Viruses 10:236-46.
Torres TM, Núñez-Sandoval YC, de la Cruz AJ, 2017. Social representations that adolescents from Guadalajara, México have about human influenza. Actualid Psicol 31:17-30. DOI: https://doi.org/10.15517/ap.v31i122.24578
Vega R, 2020. El rol de la vacunación contra influenza y su impacto en Cardiología. Rev Colomb Cardiol 27:582-8. DOI: https://doi.org/10.1016/j.rccar.2020.06.001
Wang J, Xiong J, Yang K, Peng S, Xu Q, 2010. Use of GIS and agent-based modeling to simulate the spread of influenza. pp. 1-6 in 18th International Conference on Geoinformatics, IEEE. DOI: https://doi.org/10.1109/GEOINFORMATICS.2010.5567658
Xian Q, Yan Q, Chang B, Xi G, Lun C, Fen T, Hong J, Yong H, Pei C, Bing L, Ke X, Chao S, Feng Z, Ming Z, Hua W, 2013. Probable person to person transmission of novel avian influenza A(H7N9) virus in Eastern China. Epidemiological Investigation. BMJ 347:f4752. DOI: https://doi.org/10.1136/bmj.f4752
Younsi ZF, Hamdadou D, Chakhar S, 2019. A multicriteria spatiotemporal system for influenza epi-demic surveillance. In: Nilanjan D. (Ed.), Technological innovations in knowledge management and decision support. IGI Global Publisher, Hershey, Pennsylvania, USA, pp. 27. DOI: https://doi.org/10.4018/978-1-5225-6164-4.ch008
Zhang Y, Shen Ma C, Jiang C, Feng C, Shankar N, Yang P, Sun W, Wang Q, 2015. Cluster of hu-man infections with avian influenza A (H7N9) cases: a temporal and spatial analysis. Int J Envi-ron Res Public Health 12:816-28. DOI: https://doi.org/10.3390/ijerph120100816
Zhang Z, Chen D, Chen Y, Davies T, Jean-Pierre Vaillancourt, JP, Liu W, 2012. Risk signals of an influenza pandemic caused by highly pathogenic avian influenza subtype H5N1: spatio-temporal perspectives. Vet J 192:417-21. DOI: https://doi.org/10.1016/j.tvjl.2011.08.012

How to Cite

Ibarra-Zapata, E. ., Gaytán-Hernández, D., Gallegos-García, V. ., González-Acevedo, C. E. ., Meza-Menchaca, T. ., Rios-Lugo, M. J. ., & Hernández-Mendoza, H. (2021). Geospatial modelling to estimate the territory at risk of establishment of influenza type A in Mexico - An ecological study. Geospatial Health, 16(1). https://doi.org/10.4081/gh.2021.956