Quiliche, R.; Rentería-Ramos, R.; de Brito Junior, I.; Luna, A.; Chong, M. Using Spatial Patterns of COVID-19 to Build a Framework for Economic Reactivation. Sustainability 2021, 13, 10092, doi:10.3390/su131810092.
Quiliche, R.; Rentería-Ramos, R.; de Brito Junior, I.; Luna, A.; Chong, M. Using Spatial Patterns of COVID-19 to Build a Framework for Economic Reactivation. Sustainability 2021, 13, 10092, doi:10.3390/su131810092.
Quiliche, R.; Rentería-Ramos, R.; de Brito Junior, I.; Luna, A.; Chong, M. Using Spatial Patterns of COVID-19 to Build a Framework for Economic Reactivation. Sustainability 2021, 13, 10092, doi:10.3390/su131810092.
Quiliche, R.; Rentería-Ramos, R.; de Brito Junior, I.; Luna, A.; Chong, M. Using Spatial Patterns of COVID-19 to Build a Framework for Economic Reactivation. Sustainability 2021, 13, 10092, doi:10.3390/su131810092.
Abstract
In this article we propose an application of humanitarian logistics theory to build a supportive framework for economic reactivation and pandemic management based on province vulnerability against COVID-19. The main research question is: which factors are related to COVID-19 mortality between Peruvian provinces? We conduct a spatial regression analysis to explore which factors determines the differences in COVID-19 cumulative mortality rates for 189 Peruvian provinces up to December 2020. The most vulnerable provinces are characterized by having low outcomes of long-run poverty and high population density. Low poverty means a high economic activity that leads to more deaths of COVID-19. There is a lack of supply of a set of relief goods defined as Pandemic Response and Recovery Supportive Goods and Services (PRRSGS). These goods must be delivered in order to mitigate the risk associated to COVID-19. A supportive framework for economic reactivation can be built based on regression results and a delivery strategy can be discussed according to the spatial patterns that we found for mortality rates.
Business, Economics and Management, Accounting and Taxation
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.