Using spatial patterns of COVID-19 to build a framework for economic reactivation

Renato Quiliche, Rafael Rentería-Ramos, Irineu de Brito Junior, Ana Luna, Mario Chong

Research output: Contribution to journalArticle in a journalpeer-review

4 Scopus citations


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 determine 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 high economic activity, which leads to more deaths due to COVID-19. There is a lack of supply in the 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 with 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.
Original languageEnglish
Article number10092
Issue number18
StatePublished - 9 Sep 2021

Bibliographical note

Funding Information:
This research was funded by Vicerrectorado de Investigaci?n (VRI) y Centro de Investigaci?n de la Universidad del Pac?fico (CIUP), Universidad Nacional Abierta y a Distancia and the Coordination for the Improvement of Higher Education Personnel-Brazil (CAPES), Procad Defesa 88887.387760/2019-00.

Publisher Copyright:
© 2021 by the authors. Licensee MDPI, Basel, Switzerland.


  • Humanitarian logistics
  • Pandemic
  • Economic reactivation
  • Spatial modelling
  • COVID-19
  • economic reactivation
  • humanitarian logistics
  • pandemic
  • spatial modelling


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