COVID-19 durante a primeira onda pandêmica no Valle de México e na Cidade do México: Uma abordagem de análise espacial em pequenas áreas
DOI:
https://doi.org/10.59072/rper.vi63.55Palavras-chave:
COVID-19, Análise econométrica espacial, Valle de MéxicoResumo
Os primeiros casos da doença COVID-19 no México vieram do exterior em fevereiro de 2020. A propagação comunitária acelerou a infecção nas grandes áreas metropolitanas do México, como a Zona Metropolitana do Vale do México (VMMZ), onde se localizava a maior concentração de pessoas na região. país. Neste estudo, avaliamos a distribuição espacial dos casos positivos e óbitos em VMMZ em nível municipal por meio de um modelo econométrico espacial que inclui variáveis sociodemográficas e econômicas, além de explorarmos os casos ativos na Cidade do México em nível de bairro. Encontramos efeitos espaciais significativos, principalmente nos casos positivos, que poderiam ajudar a explicar o estágio da doença, em ambos os níveis município e bairro. O modelo lançou luz ao observar como o COVID19 atinge mais fortemente os municípios mais densamente povoados e onde o processo de urbanização foi mais profundo, em comparação com os periféricos, no entanto, as piores condições de vida também apresentam uma relação positiva, tanto nos casos positivos como nos óbitos.
Referências
Anselin, L. (2005). Exploring Spatial Data with GeoDa: A Workbook. Center for Spatially Integrated Social Science. < https://www.geos.ed.ac.uk/~gisteac/fspat/geodaworkbook.pdf>
Anselin, L. (2020). Local Spatial Autocorrelation (1) LISA and Local Moran. GeoDa. An Introduction to Spatial Data Analysis. Documentation.< https://geodacenter.github.io/workbook/6a_ local_auto/lab6a.html>
Badr, H. S., Du, H., Marshall, M., Dong, E., Squire, M. M., and Gardner, L. M. (2020). Association between mobility patterns and COVID-19 transmission in the USA: a mathematical modelling study. The Lancet Infectious Diseases, 20 (11). https://doi.org/10.1016/S1473-3099(20)305533
Baltagi, B. H. (2021). Econometric analysis of panel data. Springer Nature.
Bivand, Roger S. and Wong, David W. S. (2018). Comparing implementations of global and local indicators of spatial association TEST, 27(3), 716-748. URL https://doi.org/10.1007/s11749018-0599-x.
Bivand, R., G. Millo, and G. Piras (2021). A Review of Software for Spatial Econometrics in R. Mathematics 9 (11):1276. https://doi.org/10.3390/math9111276.
Cliff, A. D., Ord, J. K., Haggett, P., & Versey, G. R. (1981). Spatial diffusion: an historical geography of epidemics in an island community. Cambridge University Press, Cambridge.
Coccia, M. (2020). “Factors determining the diffusion of COVID-19 and suggested strategy to prevent future accelerated viral infectivity similar to COVID”. Science of the Total Environment, 729, 138474. < 10.1016/j.scitotenv.2020.138474>
Cordes, J. and Castro, M. C. (2020). “Spatial analysis of COVID-19 clusters and contextual factors in New York City”. Spatial and Spatio-temporal Epidemiology, 34, 100355. < https://doi.org/10.1016/j.sste.2020.100355>
Dong E., Du H., Gardner L. An interactive web-based dashboard to track COVID-19 in real time. Lancet Inf Dis. 20(5):533-534. doi: 10.1016/S1473-3099(20)30120-1. Accessed June 22th 2021.
Franch-Pardo, I., Napoletano, B. M., Rosete-Verges, F. and Billa, L. (2020). Spatial analysis and GIS in the study of COVID-19. A review. Science of The Total Environment, 739, 140033< https://doi.org/10.1016/j.scitotenv.2020.140033>.
Galindo, Jorge (2020). “La pandemia sigue el rastro de la desigualdad en México”, El País, accessed July 05, 2021 <https://elpais.com/sociedad/2020-05-19/la-pandemia-sigue-el-rastro-de-ladesigualdad-en-mexico.html>.
Ghosh, Pritam and Alfredo Cartone, (2020), A Spatio‐temporal analysis of COVID‐19 outbreak in Italy, Regional Science Policy & Practice, 12(6), pp. 1047-1062 < https://doi.org/10.1111/rsp3.12376>.
Gobierno de México (2021). Casos históricos asociados a COVID-19. Subsecretaría de Prevención y Promoción de la Salud Dirección General de Epidemiología. Accessed June 26th 2021 <https://www.gob.mx/salud/documentos/datos-abiertos-152127>.
Guillén, Pedro (2021). “El COVID-19 sí entiende de clases sociales”, The Conversation, accessed July 05, 2021 <https://theconversation.com/la-COVID-19-si-entiende-de-clases-sociales-
>
Harris T.M. (2006) Scale as Artifact: GIS, Ecological Fallacy, and Archaeological Analysis. In: Lock G., Molyneaux B.L. (eds) Confronting Scale in Archaeology. Springer, Boston, MA <https://doi.org/10.1007/0-387-32773-8_4>
Hernández, Héctor (2020). “Mortalidad por COVID-19 en México. Notas preliminares para un perfil sociodemográfico”, Notas de Coyuntura del CRIM, N° 36, accessed June 29, 2021 <https://web.crim.unam.mx/sites/default/files/2020-06/crim_036_hector-hernandez_mortalidadpor-COVID-19_0.pdf>
Huitrón-Mendoza, J. A. and Prudencio-Vázquez, J.A. (2020) Gestión Geoespacial de la Pandemia SARS-CoV-2 en México. Boletín Ciudades y Regiones. <https://www.boletinciu dadesyregiones.org/2020/11/gestion-geoespacial-de-la-pandemia-sars.html>.
INEGI (2020). Encuesta Nacional de Ocupación y Empleo, Nueva Edición, ENOEN.
Jaramillo, Máximo (2021). “La pandemia contra los pobres: la Ciudad de México y COVID-19”, Blogs LSE, accessed July 15, 2021 <https://blogs.lse.ac.uk/latamcaribbean/2021/03/02/la-pandemia-contra-los-pobres-la-ciudad-de-mexico-y-COVID-19/>
Kang, D., Choi, H., Kim, J. H. and Choi, J. (2020). Spatial epidemic dynamics of the COVID-19 outbreak in China. International Journal of Infectious Diseases, 94, 96-102. < https://doi.org/10.1016/j.ijid.2020.03.076>
Maroko, A., Nash, D. y Pavilonis, B. (2020). "COVID-19 and Inequity: a Comparative Spatial Analysis of New York City and Chicago Hot Spots", Journal of Urban Health, Vol. 97, 461-470 < https://link.springer.com/article/10.1007/s11524-020-00468-0>.
Mollalo, A., Vahedi, B. and Rivera, K. M. (2020). GIS-based spatial modeling of COVID-19 incidence rate in the continental United States. Science of the total environment, 728, 138884 < https://doi.org/10.1016/j.scitotenv.2020.138884>
Mongey, Simon and Alex Weinberg, (2020), Characteristics of workers in low work-from-home and high personal-proximity occupations, Becker Friedman Institute for Economic, White Paper < https://bfi.uchicago.edu/working-paper/characteristics-of-workers-in-low-work-from-home-andhigh-personal-proximity-occupations/>.
Musa, G. et al. (2013). “Use of gis Mapping as a Public Health Tool-From Cholera to Cancer”. Health Services Insights, (6), 111-116 < https://www.ncbi.nlm.nih.gov/pmc/articles/ PMC4089751/>.
O’Sullivan, D. and Unwin, D. (2010). Geographic Information Analysis, Second Edition, John Wiley & Sons, Inc., New Jersey
Piantadosi, S., Byar, D. P. and Green, S. B. (1988). “The ecological fallacy”, American journal of epidemiology, 127(5), 893-904 < https://doi.org/10.1093/oxfordjournals.aje.a114892>.
Programa de Naciones Unidas para el Desarrollo, PNUD (2019). Informe de Desarrollo Humano Municipal 2010–2015. Transformando México desde lo local. Accessed June 22th <https://www.mx.undp.org/content/mexico/es/home/library/poverty/informe-de-desarrollo-hu- mano-municipal-2010-2015--transformando-.html>.
Qiu, Y., Chen, X., & Shi, W. (2020). “Impacts of social and economic factors on the transmission of coronavirus disease 2019 (COVID-19) in China”. Journal of Population Economics, 33(4), 11271172 < https://link.springer.com/article/10.1007/s00148-020-00778-2>.
R Core Team (2013). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL http://www.R-project.org/.
Rezaeian, M. et al. (2007). “Geographical Epidemiology, Spatial Analysis and Geographical Information Systems: A Multidisciplinary Glossary”. Journal of Epidemiology Community Health, (61), 98-102. doi: <10.1136/jech.2005.043117>.
Rivera, Alejandra (2020). “El COVID-19 y las desigualdades sociales”, CLACSO, Observatorio Social del Coronavirus, accessed July 05, 2021 <https://www.clacso.org/wp-con- tent/uploads/2020/04/Alejandra-Rivera-Alvarado.pdf>.
Rivero, Paulina (2020). “El coronavirus tiene clase social”, Revista Nexos, accessed June 30, 2021 <https://www.nexos.com.mx/?p=47908>
Sannigrahi, S., Pilla, F., Basu, B., Basu, A. S., and Molter, A. (2020). “Examining the association between socio-demographic composition and COVID-19 fatalities in the European region using spatial regression approach”. Sustainable cities and society, 62, 102418 <https://doi.org/10.1016/j.scs.2020.102418>.
Sannigrahi, Srikanta, Francesco Pilla, Bidroha Basu, Arunima Sarkar Basu, and Anna Molter, (2020), Examining the association between socio-demographic composition and COVID-19 fatalities in the European region using spatial regression approach, Sustainable cities and society, 62, 102418 < https://doi.org/10.1016/j.scs.2020.102418>.
Schwartz, S. (1994). The fallacy of the ecological fallacy: the potential misuse of a concept and the consequences. American journal of public health, 84(5), 819-824 < https://pubmed.ncbi.nlm.nih.gov/8179055/>.
Smallman-Raynor, M. & Cliff, A. (2004). War epidemics: an historical geography of infectious diseases in military conflict and civil strife, 1850-2000. Oxford University Press, Oxford < https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2289967/>.
Sun, F., Matthews, S. A., Yang, T. C. and Hu, M. H. (2020). “A spatial analysis of the COVID19 period prevalence in US counties through June 28, 2020: where geography matters?”. Annals of epidemiology. 52, 54-59 < https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7386391/>
Downloads
Publicado
Como Citar
Edição
Secção
Licença
Direitos de Autor (c) 2023 RPER

Este trabalho encontra-se publicado com a Creative Commons Atribuição-NãoComercial 4.0.