Using Spatial Patterns of COVID-19 to Build a Framework for Economic Reactivation

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.

Automated summary

This study utilizes spatial regression analysis to explore factors contributing to differences in COVID-19 mortality rates among Peruvian provinces. Findings indicate that provinces with lower poverty rates and higher population densities are more vulnerable to the virus. The study also highlights the inadequate supply of relief goods for COVID-19 and suggests establishing a supportive framework for economic recovery based on research results.