Skip to main content
View expanded cover

International Conference on Geospatial Information Sciences

iGISc 2021: Advances in Geospatial Data Science pp 177–193Cite as

A Dynamic Social Vulnerability Index to COVID-19 in Mexico

Part of the Lecture Notes in Geoinformation and Cartography book series (LNGC)

Abstract

Several months have passed since the appearance of COVID-19, populations that were the most vulnerable at the beginning might not be anymore, and vice-versa. Government interventions, people behaviours and vaccination policies, change the social vulnerability. Our work proposes a complementary framework to the classic vulnerability indexes which aggregate structural variables into composite indexes. We define a Dynamic Vulnerability Index as an evolving relation between structural indicators and mortality ratio, we construct this index using a data-driven approach that updates the mortality ratio and uses Partial Least Squares to find a weighting of the structural variables at each municipality. Our index is able to distinguish at any given time between zones that are potentially vulnerable but do not exhibit a high exposure, and zones that are not as vulnerable in terms of their structural variables but present higher levels of exposure. The southwest part of the country, comprising the states of Chiapas, Guerrero and Oaxaca, exhibits low Dynamic Vulnerability for most of the study period despite being one of the poorest regions in the country. This happens because most of the region is relatively isolated and doesn’t have a great influx of people that could carry the virus. On the contrary, the Central Region where the capital (Mexico City) is located and has been the epicenter of the pandemic in Mexico, has remained with a high vulnerability for the whole period, even if it is not particularly poor. Our index represents a complement to the static view of vulnerability in the context of an evolving pandemic. While static vulnerability highlights regions that could experience a strong impact, the dynamic vulnerability highlights regions where there is a strong relationship between the fixed structural conditions and the evolving epidemic. This complementary picture allows decision makers to take more focused actions.

This is a preview of subscription content, access via your institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-030-98096-2_13
  • Chapter length: 17 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
eBook
USD   109.00
Price excludes VAT (USA)
  • ISBN: 978-3-030-98096-2
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Hardcover Book
USD   149.99
Price excludes VAT (USA)
Learn about institutional subscriptions
Fig. 1

Adapted from Cutter et al. (2003)

Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

References

  • Acharya R, Porwal A (2020) A vulnerability index for the management of and response to the COVID-19 epidemic in India: an ecological study. Lancet Global Health 0(0):1–10. ISSN 2214109X. 10.1016/S2214-109X(20)30300-4. https://linkinghub.elsevier.com/retrieve/pii/S2214109X20303004

  • Aksha SK, Juran L, Resler LM, Zhang Y (2019) An analysis of social vulnerability to natural hazards in Nepal using a modified social vulnerability index. Int J Disas Risk Sci 10(1):103–116. ISSN 2095-0055, 2192-6395. https://doi.org/10.1007/s13753-018-0192-7

  • Birkmann J, Dech S, Hirzinger G, Klein R, Klüpfel H, Lehmann F, Mott C, Nagel K, Schlurmann T, Setiadi NJ, Siegert F, Strunz G (2006) Measuring vulnerability to promote disaster resilient societies? Conceptual frameworks and definitions. In: Measuring vulnerability to natural hazards: towards disaster resilient societies. UNU-Press, Tokio

    Google Scholar 

  • Brandt N (2011) Informality in Mexico. Working Paper 896, OECD, Paris, October

    Google Scholar 

  • Cardona O-D, van Aalst MK, Birkmann J, Fordham M, McGregor G, Rosa P, Pulwarty RS, Schipper ELF, Sinh BT, Décamps H, Keim M, Davis I, Ebi KL, Lavell A, Mechler R, Murray V, Pelling M, Pohl Smith A-O, Thomalla F (2012) Determinants of risk: exposure and vulnerability. In: Field CB, Barros V, Stocker TF, Dahe Q (eds) Managing the risks of extreme events and disasters to advance climate change adaptation. Cambridge University Press, Cambridge, pp 65–108. 978-1-139-17724-5. https://doi.org/10.1017/CBO9781139177245.005

  • CONEVAL (2015) Pobreza municipal 2010–2015. https://www.coneval.org.mx/Medicion/Paginas/Pobreza-municipal.aspx. Accessed 01 Oct 2020

  • Cutter SL, Boruff BJ, Shirley WL (2003) Social vulnerability to environmental hazards *: social vulnerability to environmental hazards. Soc Sci Q 84(2):242–261. ISSN 00384941. 10.1111/1540-6237.8402002

    Google Scholar 

  • de Loyola Hummell BM, Cutter SL, Emrich CT (2016) Social vulnerability to natural hazards in Brazil. Int J Disaster Risk Sci 7(2):111–122. ISSN 2095-0055, 2192-6395. https://doi.org/10.1007/s13753-016-0090-9

  • Farin Fatemi, Ali Ardalan, Benigno Aguirre, Nabiollah Mansouri, and Iraj Mohammadfam. Social vulnerability indicators in disasters: Findings from a systematic review, 6 2017. ISSN 22124209

    Google Scholar 

  • Fernández-Rojas MA, Esparza MAL-R, Campos-Romero A, Calva-Espinosa DY, Moreno-Camacho JL, Langle-Martínez AP, García-Gil A, Solís-González CJ, Canizalez-Román A, León-Sicairos N, Alcántar-Fernández J (2021) Epidemiology of COVID-19 in Mexico: symptomatic profiles and presymptomatic people. Int J Infect Dis 104:572–579. ISSN 1201-9712. 10.1016/j.ijid.2020.12.086

    Google Scholar 

  • Flanagan BE, Gregory EW, Hallisey EJ, Heitgerd JL, Lewis B (2011) A social vulnerability index for disaster management a social vulnerability index for disaster management. J Homel Secur Emerg Manag. https://doi.org/10.2202/1547-7355.1792

    CrossRef  Google Scholar 

  • Fortaleza CMCB, Guimarães RB, De Almeida GB, Pronunciate M, Ferreira CP (2020) Taking the inner route: spatial and demographic factors affecting vulnerability to COVID-19 among 604 cities from inner São Paulo State, Brazil. Epidemiol Infect 148. ISSN 14694409. 10.1017/S095026882000134X. https://doi.org/10.1017/S095026882000134X

  • Frigerio I, Carnelli F, Cabinio M, De Amicis M (2018) Spatiotemporal pattern of social vulnerability in Italy. Int J Disaster Risk Sci 9(2):249–262. ISSN 2095-0055. 2192-6395. https://doi.org/10.1007/s13753-018-0168-7

  • Hale T, Angrist N, Goldszmidt R, Kira B, Petherick A, Phillips T, Webster S, Cameron-Blake E, Hallas L, Majumdar S, Tatlow H (2021) A global panel database of pandemic policies (Oxford COVID-19 Government Response Tracker). Nat Hum Behav 1–10. ISSN 2397-3374. 10.1038/s41562-021-01079-8

    Google Scholar 

  • Hernández-Garduño E (2020) Obesity is the comorbidity more strongly associated for Covid-19 in Mexico. A case-control study. Obes Res Clin Pract 14(4):375–379. ISSN 1871-403X. 10.1016/j.orcp.2020.06.001

    Google Scholar 

  • Höskuldsson A (1988) PLS regression methods. J Chemom 2(3), 211–228. ISSN 0886-9383, 1099-128X. https://doi.org/10.1002/cem.1180020306

  • Ienca M, Vayena E (2020) On the responsible use of digital data to tackle the COVID-19 pandemic. Nat Med 26(4):463–464. ISSN 1546-170X. 10.1038/s41591-020-0832-5

    Google Scholar 

  • INEGI (2010) Censo nacional de población y vivienda. https://inegi.org.mx/programas/ccpv/2010/. Accessed 01 Oct 2020

  • INEGI (2018) Prevalencia de obesidad, hipertensión y diabetes para los municipios de méxico. https://www.inegi.org.mx/investigacion/pohd/2018/. Accessed 01 Feb 2021

  • Khazanchi R, Beiter ER, Gondi S, Beckman AL, Bilinski A, Ganguli I (2020) County-level association of social vulnerability with COVID-19 cases and deaths in the USA, vol 6. ISSN 15251497. https://link.springer.com/article/10.1007/s11606-020-05882-3

  • Lara-Garcia OE, Retamales VA, Suarez OM, Parajuli P, Hingle S, Robinson R (2020) Application of social vulnerability index to identify high- risk population of contracting COVID-19 infection: a state-level study. https://doi.org/10.1101/2020.08.03.20166983

  • Mario Graff-Guerrero, Sánchez-Siordia Oscar, Daniela Moctezuma, Eric Tellez, Miranda Sabino (2020) Medición de movilidad usando facebook, google y twitter. Technical report, CONACyT

    Google Scholar 

  • Meza R, Barrientos-Gutierrez T, Rojas-Martinez R, Reynoso-Noverón N, Palacio-Mejia LS, Lazcano-Ponce E, Hernández-Ávila M (2015) Burden of type 2 diabetes in Mexico: past, current and future prevalence and incidence rates. Prev Med 81:445–450. ISSN 0091-7435. 10.1016/j.ypmed.2015.10.015

    Google Scholar 

  • Naik P, Tsai C-L (2000) Partial least squares estimator for single-index models. J R Stat Soc: Ser B (Stat Methodol) 62(4):763–771. ISSN 1369-7412, 1467-9868. https://doi.org/10.1111/1467-9868.00262

  • Parra-Bracamonte GM, Lopez-Villalobos N, Parra-Bracamonte FE (2020) Clinical characteristics and risk factors for mortality of patients with COVID-19 in a large data set from Mexico. Annals Epidemiol 52:93–98.e2. ISSN 1047-2797. 10.1016/j.annepidem.2020.08.005

    Google Scholar 

  • Salamanca JDG, Vargas G (2020) Quarantine and informality: reflections on the colombian case. Space Cult 23(3):307–314. ISSN 1206-3312, 1552-8308. https://doi.org/10.1177/1206331220938626

  • Secretaria de Salud. Covid-19, 2021. data retrieved from Secretaria de Salud. https://www.gob.mx/salud/documentos/datos-abiertos-152127

  • Sun L, Ji S, Yu S, Ye J (2009) On the equivalence between canonical correlation analysis and orthonormalized partial least squares. In: Proceedings of the 21st international jont conference on artifical intelligence, IJCAI’09, pp 1230–1235, San Francisco, CA, USA, July 2009. Morgan Kaufmann Publishers Inc

    Google Scholar 

  • The Lancet. Redefining vulnerability in the era of COVID-19. Lancet 395(10230):1089. ISSN 01406736. 10.1016/S0140-6736(20)30757-1. https://www.uneca.org/sites/. https://linkinghub.elsevier.com/retrieve/pii/S0140673620307571

  • Tiwari A, Dadhania AV, Ragunathrao VA, Oliveira ER (2021) Using machine learning to develop a novel COVID-19 vulnerability index (C19VI). Sci Total Environ 773:145650. ISSN 0048-9697. 10.1016/j.scitotenv.2021.145650

    Google Scholar 

  • Trinchera L, Russolillo G (2010) On the use of structural equation models and pls path modeling to build composite indicators. University of Macerata, Italy

    Google Scholar 

  • Uddin MN, Islam AS, Bala SK, Islam GT, Adhikary S, Saha D, Haque S, Fahad MG, Akter R (2019) Mapping of climate vulnerability of the coastal region of Bangladesh using principal component analysis. Appl Geogr 102:47–57. ISSN 01436228. 10.1016/j.apgeog.2018.12.011

    Google Scholar 

  • United Nations Office for Disaster Risk Reduction (2015) Sendai framework for disaster risk reduction 2015–2030. In: UN world conference on disaster risk reduction, p 37, Sendai, Japan, 2015. United Nations Office for Disaster Risk Reduction

    Google Scholar 

  • Yoon J, Klasen S (2018) An application of partial least squares to the construction of the Social Institutions and Gender Index (SIGI) and the Corruption Perception Index (CPI). Soc Indic Res 138(1):61–88. ISSN 0303-8300, 1573-0921. https://doi.org/10.1007/s11205-017-1655-8

  • Yoon J, Klasen S, Dreher A, Krivobokova T (2015) Composite indices based on partial least squares. Discussion Papers 171, Georg-August-Universität Göttingen, Courant Research Centre - Poverty, Equity and Growth (CRC-PEG), Göttingen

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. CONABIO, Ciudad de México, México

    Raúl Sierra-Alcocer

  2. CentroGeo, Ciudad de México, México

    Pablo López-Ramírez

  3. CIMAT, Guanajuato, México

    Graciela González-Farías

Authors
  1. Raúl Sierra-Alcocer
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Pablo López-Ramírez
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Graciela González-Farías
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Raúl Sierra-Alcocer .

Editor information

Editors and Affiliations

  1. Centro de Investigación en Ciencias de Información Geoespacial - CDMX, Tlalpan, Mexico City, Mexico

    Ph.D. Rodrigo Tapia-McClung

  2. Centro de Investigación en Ciencias de Información Geoespacial - Yucatán, Parque Científico Tecnológico Yucatán (PCTY), Mérida, Yucatán, Mexico

    Prof. Oscar Sánchez-Siordia

  3. Centro de Investigación en Ciencias de Información Geoespacial - CDMX, Tlalpan, Mexico City, Mexico

    Mag. Karime González-Zuccolotto

  4. Centro de Investigación en Ciencias de Información Geoespacial - Yucatán, Parque Científico Tecnológico Yucatán (PCTY), Mérida, Yucatán, Mexico

    Dr. Hugo Carlos-Martínez

Rights and permissions

Reprints and Permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Verify currency and authenticity via CrossMark

Cite this paper

Sierra-Alcocer, R., López-Ramírez, P., González-Farías, G. (2022). A Dynamic Social Vulnerability Index to COVID-19 in Mexico. In: Tapia-McClung, R., Sánchez-Siordia, O., González-Zuccolotto, K., Carlos-Martínez, H. (eds) Advances in Geospatial Data Science. iGISc 2021. Lecture Notes in Geoinformation and Cartography. Springer, Cham. https://doi.org/10.1007/978-3-030-98096-2_13

Download citation