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.
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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
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