IVTSARS-CoV-2 : um índice multicritério e a dinâmica urbana em Minas Gerais, Brasil
DOI:
https://doi.org/10.11606/s15188787.2025059006877Palabras clave:
Demografia, Epidemiologia, Disparidades em Assistência à Saúde, Fatores SocioeconômicosResumen
OBJETIVO: Desenvolver um índice de vulnerabilidade municipal à transmissão do SARS-CoV-2 em Minas Gerais, integrando fatores socioeconômicos, infraestrutura urbana, mobilidade e indicadores compostos de vulnerabilidade social e lazer. Identificaram-se os fatores associados à distribuição espacial da covid-19 e classificaram-se os 853 municípios do estado por níveis de vulnerabilidade. MÉTODOS: Foram utilizados dados de casos de covid-19 de fevereiro de 2022, combinados a nove variáveis organizadas em três domínios: (i) fatores socioeconômicos (empregos nos setores de comércio, serviços, construção e transformação); (ii) infraestrutura urbana e mobilidade (área urbana, conectividade rodoviária e densidade de veículos); e (iii) indicadores compostos (índice de vulnerabilidade social e de acesso à cultura, esporte e lazer). Aplicou-se um modelo de análise multicritério, com pesos definidos pelas correlações de Pearson. O índice final foi validado por validação cruzada. RESULTADOS: O índice de vulnerabilidade variou de 1 a 8. Belo Horizonte registrou o maior valor (8), seguida de Uberlândia (6) e outros municípios de médio a grande porte com elevado dinamismo urbano. As variáveis mais influentes no modelo foram área urbana (22,92%), conectividade rodoviária (17,54%) e densidade de veículos (12,86%). A distribuição espacial indicou maior vulnerabilidade em regiões metropolitanas e polos regionais. CONCLUSÕES: O índice destacou o papel de características estruturais e ocupacionais na vulnerabilidade territorial à covid-19. Limitações como a defasagem temporal de algumas variáveis, a heterogeneidade entre fontes e a subnotificação de casos — especialmente em municípios com menor capacidade de testagem — podem ter influenciado os resultados. A incorporação de tecnologias emergentes e práticas sustentáveis pode mitigar riscos de futuras pandemias e promover melhor qualidade de vida. Ainda assim, o índice demonstrou ser uma ferramenta útil ao planejamento sanitário, com potencial de adaptação a outras doenças transmissíveis.
Referencias
1. Bambra C, Riordan R, Ford J, Matthews F. The COVID-19 pandemic and health inequalities. J Epidemiol Community Health. 2020 Nov;74(11):964-8. https://doi.org/10.1136/jech-2020-214401
2. Goldman N. Social Inequalities in Health. Ann N Y Acad Sci. 2001;954(1):118-39. https://doi.org/10.1111/j.1749-6632.2001.tb02750.x
3. Mackenbach JP, Stirbu I, Roskam AJR, Schaap MM, Menvielle G, Leinsalu M, et al. Socioeconomic Inequalities in Health in 22 European Countries. N Engl J Med. 2008 Jun;358(23):2468-81. https://doi.org/10.1056/NEJMsa0707519
4. Campos IS, Aratani VF, Cabral KB, Limongi JE, Oliveira SV. A Vulnerability Analysis for the Management of and Response to the COVID-19 Epidemic in the Second Most Populous State in Brazil. Front Public Health. 2021 Apr; 9. https://doi.org/10.3389/fpubh.2021.586670
5. Cifuentes MP, Rodriguez-Villamizar LA, Rojas-Botero ML, Alvarez-Moreno CA, Fernández-Niño JA. Socioeconomic inequalities associated with mortality for COVID-19 in Colombia: a cohort nationwide study. J Epidemiol Community Health. 2021 Jul;75(7):610-5. https://doi.org/10.1136/jech-2020-216275
6. Cortez MLJ, Azevedo UR, Campos Júnior EO, Nascimento DCM, Diogo AM, Coelho VBN, et al. What role do social variables and health play in the spread of COVID-19 in the state of Minas Gerais – Brazil? Cad Geogr. 2023 Apr;33(72):317. https://doi.org/10.5752/P.2318-2962.2023v33n72p317
7. Quan D, Luna Wong L, Shallal A, Madan R, Hamdan A, Ahdi H, et al. Impact of Race and Socioeconomic Status on Outcomes in Patients Hospitalized with COVID-19. J Gen Intern Med. 2021 May;36(5):1302-9. https://doi.org/10.1007/s11606-020-06527-1
8. Siqueira CAS, Freitas YNL, Cancela MC, Carvalho M, Silva LP, Dantas NCD, et al. COVID-19 no Brasil: tendências, desafios e perspectivas após 18 meses de pandemia. Rev Panam Salud Pública. 2023 Apr;46:e74. https://doi.org/10.26633/rpsp.2022.74
9. Connolly C, Keil R, Ali SH. Extended urbanisation and the spatialities of infectious disease: Demographic change, infrastructure and governance. Urban Stud. 2021 Feb;58(2):245-63. https://doi.org/10.1177/0042098020910873
10. Koehl A. Urban transport and COVID-19: challenges and prospects in low- and middle-income countries. Cities Health. 2021 Jul;5(sup1):S185-90. https://doi.org/10.1080/23748834.2020.1791410
11. Teller J. Urban density and Covid-19: towards an adaptive approach. Build Cities. 2021;2(1):150-65. https://doi.org/10.5334/bc.89
12. Alves A, Costa NM, Morgado P, Costa EM. Uncovering COVID-19 infection determinants in Portugal: towards an evidence-based spatial susceptibility index to support epidemiological containment policies. Int J Health Geogr. 2023 Apr;22(1):8. https://doi.org/10.1186/s12942-023-00329-4
13. Li X, Liu T, Lin L, Song T, Du X, Lin H, et al. Application of the analytic hierarchy approach to the risk assessment of Zika virus disease transmission in Guangdong Province, China. BMC Infect Dis. 2017 Jan;17(1):65. https://doi.org/10.1186/s12879-016-2170-2
14. Mishra SV, Gayen A, Haque SM. COVID-19 and urban vulnerability in India. Habitat Int. 2020 Sep;103:102230. https://doi.org/10.1016/j.habitatint.2020.102230
15. Sangiorgio V, Parisi F. A multicriteria approach for risk assessment of Covid-19 in urban district lockdown. Saf Sci. 2020 Oct;130:104862. https://doi.org/10.1016/j.ssci.2020.104862
16. Dom NC, Ahmad AH, Latif ZA, Ismail R. Application of geographical information system-based analytical hierarchy process as a tool for dengue risk assessment. Asian Pac J Trop Dis. 2016 Dec 1;6(12):928-35. https://doi.org/10.1016/S2222-1808(16)61158-1
17. Zhao X, Thanapongtharm W, Lawawirojwong S, Wei C, Tang Y, Zhou Y, et al. Malaria Risk Map Using Spatial Multi-Criteria Decision Analysis along Yunnan Border During the Pre-elimination Period. Am J Trop Med Hyg. 2020 Aug;103(2):793-809. https://doi.org/10.4269/ajtmh.19-0854
18. Almeida AT, Alencar MH, Garcez TV, Ferreira RJP. A systematic literature review of multicriteria and multi-objective models applied in risk management. IMA J Manag Math. 2017 Apr;28(2):153-84. https://doi.org/10.1093/imaman/dpw021
19. Hawkins RB, Charles EJ, Mehaffey JH. Socio-economic status and COVID-19 –related cases and fatalities. Public Health. 2020 Dec;189:129-34. https://doi.org/10.1016/j.puhe.2020.09.016
20. Henning A, McLaughlin C, Armen S, Allen S. Socio-spatial influences on the prevalence of COVID-19 in central Pennsylvania. Spat Spatio-Temporal Epidemiol. 2021 Jun;37:100411. https://doi.org/10.1016/j.sste.2021.100411
21. Khalatbari-Soltani S, Cumming RC, Delpierre C, Kelly-Irving M. Importance of collecting data on socioeconomic determinants from the early stage of the COVID-19 outbreak onwards. J Epidemiol Community Health. 2020 Aug;74(8):620-3. https://doi.org/10.1136/jech-2020-214297
22. Santos HLPC, Maciel FBM, Santos GM, Martins PC, Prado NMBL. Gastos públicos com internações hospitalares para tratamento da covid-19 no Brasil em 2020. Rev Saúde Pública. 2021 Aug;55. https://doi.org/10.11606/s1518-8787.2021055003666
23. Nações Unidas Brasil. Objetivo de Desenvolvimento Sustentável 3: Saúde e Bem-Estar [Internet]. 2022 [citado 2025 jan 3]. Disponível em: https://brasil.un.org/pt-br/sdgs/3
24. Instituto Brasileiro de Geografia e Estatística. Minas Gerais | Cidades e Estados [Internet]. 2025 [citado 2025 jan 3]. Disponível em: https://www.ibge.gov.br/cidades-e-estados/mg/
25. SES-MG. Boletim Epidemiológico Coronavírus [Internet]. 2022 [citado 2023 fev 16]. Disponível em: https://coronavirus.saude.mg.gov.br/boletim
26. SES-MG. Dados Abertos Coronavírus [Internet]. 2025 [citado 2025 jan 3]. Disponível em: https://coronavirus.saude.mg.gov.br/dadosabertos
27. Instituto Butantan. Portal do Butantan. Seis fatos sobre a ômicron, a variante mais transmissível da covid-19 [Internet]. 2022 [citado 2023 ago 5]. Disponível em: https://butantan.gov.br/ noticias/seis-fatos-sobre-a-omicron-a-variante-mais-transmissivel-da-covid-19
28. Benita F, Gasca-Sanchez F. The main factors influencing COVID-19 spread and deaths in Mexico: A comparison between phases I and II. Appl Geogr. 2021 Sep;134:102523. https://doi.org/10.1016/j.apgeog.2021.102523
29. Rocha R, Atun R, Massuda A, Rache B, Spinola P, Nunes L, et al. Effect of socioeconomic inequalities and vulnerabilities on health-system preparedness and response to COVID-19 in Brazil: a comprehensive analysis. Lancet Glob Health. 2021 Jun;9(6):e782-92. https://doi.org/10.1016/S2214-109X(21)00081-4
30. Fundação João Pinheiro. Home [Internet]. 2019 [citado 2025 jan 3]. Disponível em: https://imrs.fjp.mg.gov.br/Home/Dimensoes
31. MapBiomas. Home [Internet]. 2025 [citado 2025 jan 3]. Disponível em: https://brasil.mapbiomas.org
32. Yu D, Li X, Yu J, Shi X, Liu P, Tian P. Whether Urbanization Has Intensified the Spread of Infectious Diseases — Renewed Question by the COVID-19 Pandemic. Front Public Health. 2021 Nov;9. https://doi.org/10.3389/fpubh.2021.699710
33. OpenStreetMap. Home. 2025 [citado 2025 jan 3]. Disponível em: https://www.openstreetmap.org/
34. Fundação João Pinheiro. Home [Internet]. 2019 [citado 2023 fev 6]. Disponível em: https://imrs.fjp.mg.gov.br/Home/IMRS
35. Soares-Filho BS, Cerqueira GC, Pennachin CL. Dinamica — a stochastic cellular automata model designed to simulate the landscape dynamics in an Amazonian colonization frontier. Ecol Model. 2002 Sep;154(3):217-35. https://doi.org/10.1016/S0304-3800(02)00059-5
36. Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, et al. scikit-learn: machine learning in Python. J Mach Learn Res. 2011; 12:2825-30.
37. Arlot S, Celisse A. A survey of cross-validation procedures for model selection. Stat Surv. 2010 Jan;4:40-79. https://doi.org/10.1214/09-SS054
38. Bishop CM, Nasrabadi NM. Pattern Recognition and Machine Learning [Internet]. Vol. 4. New York: Springer; 2006 [citado 2024 ago 8]. Disponível em: https://link.springer.com/ book/9780387310732
39. Browne MW. Cross-Validation Methods. J Math Psychol. 2000 Mar;44(1):108-32. https://doi.org/10.1006/jmps.1999.1279
40. SES-MG. Superintendências Regionais de Saúde (SRS) e Gerências Regionais de Saúde (GRS) [Internet]. 2025 [citado 2025 jan 3]. Disponível em: https://www.saude.mg.gov.br/sobre/ institucional/superintendencias-regionais-de-saude-e-gerencias-regionais-de-saude
41. Marshall JD. Urban Land Area and Population Growth: A New Scaling Relationship for Metropolitan Expansion. Urban Stud. 2007 Sep;44(10):1889-904. https://doi.org/10.1080/00420980701471943
42. Chen L, Xing Y, Zhang Y, Xie J, Su B, Jiang J, et al. Long-term variations of urban–Rural disparities in infectious disease burden of over 8.44 million children, adolescents, and youth in China from 2013 to 2021: An observational study. PLOS Med. 2024 Apr;21(4):e1004374. https://doi.org/10.1371/journal.pmed.1004374
43. David Q, Peeters D, Van Hamme G, Vandermotten C. Is bigger better? Economic performances of European cities, 1960–2009. Cities. 2013 Dec;35:237-54. https://doi.org/10.1016/j.cities.2013.07.011
44. Marconato M, Parré JL, Coelho MH. Dinâmica financeira dos municípios brasileiros. Rev Adm Pública. 2021 May; 55:378-94. https://doi.org/10.1590/0034-761220200041
45. Knowles RD. Transit Oriented Development in Copenhagen, Denmark: from the Finger Plan to Ørestad. J Transp Geogr. 2012 May; 22:251-61. https://doi.org/10.1016/j.jtrangeo.2012.01.009
46. Li JZ, Yang DC. Guangdong: China’s Economic Powerhouse — The Past, the Present and the Future. In: Bui TX, Yang DC, Jones WD, Li JZ, editors. China’s Economic Powerhouse: Reform in Guangdong Province [Internet]. London: Palgrave Macmillan UK; 2003 [citado 2023 set 3]. p. 208-30. Disponível em: https://doi.org/10.1057/9780230508668_11
47. Munster VJ, Bausch DG, Wit E, Fischer R, Kobinger G, Muñoz-Fontela C, et al. Outbreaks in a Rapidly Changing Central Africa — Lessons from Ebola. N Engl J Med. 2018 Sep; 379(13):1198-201. https://doi.org/10.1057/9780230508668_11
48. Batella W, Miyazaki VK. Relações entre rede urbana e covid-19 em Minas Gerais. Hygeia. 2020 Jun;102-10. https://doi.org/10.14393/Hygeia0054622
49. Souza MVM, Ferreira Júnior DB. Rede urbana, interações espaciais e a geografia da saúde: análise da trajetória da Covid-19 no estado do Pará. Rev Bras Geogr Econômica. 2020 Apr;(18). https://doi.org/10.4000/espacoeconomia.13146
50. Alidadi M, Sharifi A. Effects of the built environment and human factors on the spread of COVID-19: A systematic literature review. Sci Total Environ. 2022 Dec; 850:158056. https://doi.org/10.1016/j.scitotenv.2022.158056
51. Kephart JL, Delclòs-Alió X, Rodríguez DA, Sarmiento OL, Barrientos-Gutiérrez T, Ramirez-Zea M, et al. The effect of population mobility on COVID-19 incidence in 314 Latin American cities: a longitudinal ecological study with mobile phone location data. Lancet Digit Health. 2021 Nov;3(11):e716–22. https://doi.org/10.1016/S2589-7500(21)00174-6
52. Wang B, Liu J, Li Y, Fu S, Xu X, Li L, et al. Airborne particulate matter, population mobility and COVID-19: a multi-city study in China. BMC Public Health. 2020 Oct;20(1):1585. https://doi.org/10.1186/s12889-020-09669-3
53. Chang S, Pierson E, Koh PW, Gerardin J, Redbird B, Grusky D, et al. Mobility network models of COVID-19 explain inequities and inform reopening. Nature. 2021 Jan;589(7840):82-7. https://doi.org/10.1038/s41586-020-2923-3
54. Li S, Ma S, Zhang J. Association of built environment attributes with the spread of COVID-19 at its initial stage in China. Sustain Cities Soc. 2021 Apr;67:102752. https://doi.org/10.1016/j.scs.2021.102752
55. Wells K, Lurgi M, Collins B, Lucini B, Kao RR, Lloyd AL, et al. Disease control across urban–rural gradients. J R Soc Interface. 2020 Dec 9; 17(173):20200775. https://doi.org/10.1098/rsif.2020.0775
56. Boterman W. Population density and SARS-CoV-2 pandemic: Comparing the geography of different waves in the Netherlands. Urban Stud. 2023 Jun;60(8):1377-402. https://doi.org/10.1177/00420980221087165
57. Patil R, Dave R, Patel H, Shah VM, Chakrabarti D, Bhatia U. Assessing the interplay between travel patterns and SARS-CoV-2 outbreak in realistic urban setting. Appl Netw Sci. 2021 Dec;6(1):1-19. https://doi.org/10.1007/s41109-020-00346-3
58. Artı́ñano B, Salvador P, Alonso DG, Querol X, Alastuey A. Influence of traffic on the PM10 and PM2.5 urban aerosol fractions in Madrid (Spain). Sci Total Environ. 2004 Dec;334-335:111-23. https://doi.org/10.1016/j.scitotenv.2004.04.032
59. Beddows DCS, Harrison RM. PM10 and PM2.5 emission factors for non-exhaust particles from road vehicles: Dependence upon vehicle mass and implications for battery electric vehicles. Atmos Environ. 2021 Jan;244:117886. https://doi.org/10.1016/j.atmosenv.2020.117886
60. Renard JB, Surcin J, Annesi-Maesano I, Poincelet E. Temporal Evolution of PM2.5 Levels and COVID-19 Mortality in Europe for the 2020–2022 Period. Atmosphere. 2023 Aug;14(8):1222. https://doi.org/10.3390/atmos14081222
61. Zoran MA, Savastru RS, Savastru DM, Tautan MN. Assessing the relationship between surface levels of PM2.5 and PM10 particulate matter impact on COVID-19 in Milan, Italy. Sci Total Environ. 2020 Oct;738:139825. https://doi.org/10.1016/j.scitotenv.2020.139825
62. Brągoszewska E, Mainka A. Impact of different air pollutants (PM10, PM2.5, NO2, and bacterial aerosols) on COVID-19 cases in Gliwice, Southern Poland. Int J Environ Res Public Health. 2022 Jan;19(21):14181. https://doi.org/10.3390/ijerph192114181
63. Jiang Y, Wu XJ, Guan YJ. Effect of ambient air pollutants and meteorological variables on COVID-19 incidence. Infect Control Hosp Epidemiol. 2020 Sep;41(9):1011-5. https://doi.org/10.1017/ice.2020.222
64. Khaniabadi YO, Sicard P, Dehghan B, Mousavi H, Saeidimehr S, Farsani MH, et al. COVID-19 Outbreak Related to PM10, PM2.5, Air Temperature and Relative Humidity in Ahvaz, Iran. Dr Sulaiman Al Habib Med J. 2022 Dec;4(4):182-95. https://doi.org/10.1007/s44229-022-00020-z
65. Meo SA, Al-Khlaiwi T, Ullah CH. Effect of ambient air pollutants PM2.5 and PM10 on COVID-19 incidence and mortality: observational study. Eur Rev Med Pharmacol Sci. 2021 Dec;25(23):7553-64. https://doi.org/10.26355/eurrev_202112_27455
66. Gonçalves K dos S, Cirino GG, Costa MO, Couto L de O do, Tortelote GG, Hacon S de S. The potential impact of PM on the covid-19 crisis in the Brazilian Amazon region. Rev Saúde Pública. 2023 Sep;57. https://doi.org/10.11606/s1518-8787.2023057005134
67. Andersen LM, Harden SR, Sugg MM, Runkle JD, Lundquist TE. Analyzing the spatial determinants of local Covid-19 transmission in the United States. Sci Total Environ. 2021 Feb;754:142396. https://doi.org/10.1016/j.scitotenv.2020.142396
68. De Angelis E, Renzetti S, Volta M, Donato F, Calza S, Placidi D, et al. COVID-19 incidence and mortality in Lombardy, Italy: An ecological study on the role of air pollution, meteorological factors, demographic and socioeconomic variables. Environ Res. 2021 Apr;195:110777. https://doi.org/10.1016/j.envres.2021.110777
69. Garnier R, Benetka JR, Kraemer J, Bansal S. Socioeconomic Disparities in Social Distancing During the COVID-19 Pandemic in the United States: Observational Study. J Med Internet Res. 2021 Jan 22;23(1):e24591. https://doi.org/10.2196/24591
70. Yoshikawa Y, Kawachi I. Association of Socioeconomic Characteristics With Disparities in COVID-19 Outcomes in Japan. JAMA Netw Open. 2021 Jul 14;4(7):e2117060. https://doi.org/10.1001/jamanetworkopen.2021.17060
71. Instituto Butantan. Retrospectiva 2021: segundo ano da pandemia é marcado pelo avanço da vacinação contra Covid-19 no Brasil [Internet]. 2021 [citado 2023 set 5]. Disponível em: https://butantan.gov.br/noticias/retrospectiva-2021-segundo-ano-da-pandemia-e-marcado-peloavanco-da-vacinacao-contra-covid-19-no-brasil
72. Ministério da Saúde (BR). Portaria no 913, de 22 de abril de 2022 [Internet]. 2022 [citado 2025 jan 3]. Disponível em: https://www.planalto.gov.br/ccivil_03/portaria/prt/ portaria-913-22-ms.htm
73. Ribeiro L, Bernardes A. Atualização da estimativa de subnotificação em casos de hospitalização por síndrome respiratória aguda e confirmados por infecção por Covid-19 no Brasil e estimativa para Minas Gerais [Internet]. Belo Horizonte: Centro de Desenvolvimento e Planejamento Regional (Cedeplar/UFMG); 2020 [citado 2024 nov 15]. Disponível em: https://ufmg.br/ comunicacao/noticias/estudo-da-face
Publicado
Número
Sección
Licencia
Derechos de autor 2025 Matheus Luiz Jorge Cortez, Úrsula Ruchkys de Azevedo, Sónia Maria Carvalho Ribeiro, Anacleto Marito Diogo, Danilo Cirino Muniz do Nascimento
Esta obra está bajo una licencia internacional Creative Commons Atribución 4.0.
Datos de los fondos
-
Coordenação de Aperfeiçoamento de Pessoal de Nível Superior
Números de la subvención 88887.504714/2020-00
