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
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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.

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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

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