A spatial queuing model for the location decision of emergency medical vehicles for pandemic outbreaks: the case of Za'atari refugee camp

Enhancing Mass Vaccination Programs with Queueing Theory and Spatial Optimization

Background

Mass vaccination is a cornerstone of public health emergency preparedness and response. However, injudicious placement of vaccination sites can lead to the formation of long waiting lines or queues, which discourages individuals from waiting to be vaccinated and may thus jeopardize the achievement of public health targets. Queueing theory offers a framework for modeling queue formation at vaccination sites and its effect on vaccine uptake.

Methods

We developed an algorithm that integrates queueing theory within a spatial optimization framework to optimize the placement of mass vaccination sites. The algorithm was built and tested using data from a mass canine rabies vaccination campaign in Arequipa, Peru. We compared expected vaccination coverage and losses from queueing (i.e., attrition) for sites optimized with our queue-conscious algorithm to those obtained from a queue-naive version of the same algorithm.

Results

Sites placed by the queue-conscious algorithm resulted in 9-19% less attrition and 1-2% higher vaccination coverage compared to sites placed by the queue-naïve algorithm. Compared to the queue-naïve algorithm, the queue-conscious algorithm favored placing more sites in densely populated areas to offset high arrival volumes, thereby reducing losses due to excessive queueing. These results were not sensitive to misspecification of queueing parameters or relaxation of the constant arrival rate assumption.

Conclusion

One should consider losses from queueing to optimally place mass vaccination sites, even when empirically derived queueing parameters are not available. Due to the negative impacts of excessive wait times on participant satisfaction, reducing queueing attrition is also expected to yield downstream benefits and improve vaccination coverage in subsequent mass vaccination campaigns.

Optimization in the Context of COVID-19 Prediction and Control: A Literature Review

This paper presents an overview of some key results from a body of optimization studies that are specifically related to COVID-19, as reported in the literature during 2020-2021. As shown in this paper, optimization studies in the context of COVID-19 have been used for many aspects of the pandemic. From these studies, it is observed that since COVID-19 is a multifaceted problem, it cannot be studied from a single perspective or framework, and neither can the related optimization models. Four new and different frameworks are proposed that capture the essence of analyzing COVID-19 (or any pandemic for that matter) and the relevant optimization models. These are: (i) microscale vs. macroscale perspective; (ii) early stages vs. later stages perspective; (iii) aspects with direct vs. indirect relationship to COVID-19; and (iv) compartmentalized perspective. To limit the scope of the review, only optimization studies related to the prediction and control of COVID-19 are considered (public health focused), and which utilize formal optimization techniques or machine learning approaches. In this context and to the best of our knowledge, this survey paper is the first in the literature with a focus on the prediction and control related optimization studies. These studies include optimization of screening testing strategies, prediction, prevention and control, resource management, vaccination prioritization, and decision support tools. Upon reviewing the literature, this paper identifies current gaps and major challenges that hinder the closure of these gaps and provides some insights into future research directions.

Emergency supplies purchase patterns during COVID-19 outbreak in the developing economy: frequency and stockpiling drivers’ assessment

Purpose

The purpose of the paper is to assess the roots of stockpiling behaviors and to give a quantitative assessment of shopping frequency changes for emergency supplies during the coronavirus disease 2019 (COVID-19) pandemic. In addition, the authors aim to determine the sources that influenced emergency supply purchases during the COVID-19 outbreak.

Design/methodology/approach

The study used a polling or survey process implementation to collect the data on shopping patterns and to determine the drivers of stockpiling behaviors for the assessment. The polling was conducted using a snowball technique, and descriptive and regression analyses were used to define the roots of the stockpiling behaviors and the shopping frequency changes.

Findings

It was determined that 88.0% of end-consumers increased their shopping volumes for emergency supplies. An almost twofold increase in the average duration of usage for stockpiled goods (from 11 to 21 days) was also determined. Also revealed was a reduction in shopping frequency from an average of seven (pre-COVID-19 period) to five (first wave of COVID-19 pandemic) days. Such disproportional increases in purchase volumes along with a slight reduction in shopping frequency indicate the strong stockpile patterns that occurred during the pandemic.

Originality/value

The research is based on data from Ukraine, where the number of COVID-19 cases was low. Despite the comparatively low spread of COVID-19 in large cities in Ukraine in relation to other cities globally, people still revealed panic and stockpiling behaviors. The study's quantitative assessment of shopping behaviors reveals the social and economic determinants of the shopping frequency.