Point‐of‐Dispensing Location and Capacity Optimization via a Decision Support System

Abstract

Dispensing of mass prophylaxis can be critical to public health during emergency situations and involves complex decisions that must be made in a short period of time. This study presents a model and solution approach for optimizing point‐of‐dispensing (POD) location and capacity decisions. This approach is part of a decision support system designed to help officials prepare for and respond to public health emergencies. The model selects PODs from a candidate set and suggests how to staff each POD so that average travel and waiting times are minimized. A genetic algorithm (GA) quickly solves the problem based on travel and queuing approximations (QAs) and it has the ability to relax soft constraints when the dispensing goals cannot be met. We show that the proposed approach returns solutions comparable with other systems and it is able to evaluate alternative courses of action when the resources are not sufficient to meet the performance targets.

Selecting pharmacies for COVID-19 testing to ensure access

“…Facility location for administering COVID-19 testing can be classified as selecting points of dispensing , which differs from traditional health care facility location problems because a key concern is to provide equitable and easy access to all [ 24 ]. Depending on application and focus, the points-of-dispensing problem and solution methods take different forms in the literature, including combining simulation and optimization [ 25 ], queueing approximations for drive-through dispensing [ 26 ], and addressing demand uncertainty through chance constraints [ 27 ]. Our objective is to estimate access to selected COVID-19 test sites, and corresponding travel distances, on national and state levels in the US.…”

The importance of widespread testing for COVID-19 pandemic: systems thinking for drive-through testing sites

“…Some of these studies suggest considering not only the size of the population to be served at each site, but also their socio-demographic characteristics. Differences in socio-demographic and health status across communities, e.g., age distribution and proportion of the population with underlying medical conditions, may affect the variability of testing time in sites and, consequently, boost waiting times (Ramirez-Nafarrate et al, 2015). Racial disparities have become increasingly evident as the sociodemographic profiles of hospitalisation cases and deaths are reported (NPR, 2020).…”

Repurposing COVID-19 Drive-Through Testing Centers for Mass Vaccination

“…Consolidating the lessons learned from and building upon the infrastructure of current drive-through testing centers into a decision support system can help improve efficiency upon roll-out of a mass vaccination campaign and provide the opportunity to preemptively address barriers to vaccine distribution. 11 Evidence from previous vaccination campaigns highlights the need to proactively address equity among racial, ethnic, and socioeconomic groups, as well as enforcing distribution priorities to high-risk groups, like the homeless, elderly and pregnant women. 10 Seemingly small shortcomings in access can lead to significant gaps in vaccine distribution consequently negatively impacting public health outcomes.…”