Location-Allocation Model to Improve the Distribution of COVID-19 Vaccine Centers in Jeddah City, Saudi Arabia.

The correct distribution of service facilities can help keep fixed and overhead costs low while increasing accessibility. When an appropriate location is chosen, public-sector facilities, such as COVID-19 centers, can save lives faster and provide high-quality service to the community at a low cost. The purpose of the research is to highlight the issues related to the location of COVID-19 vaccine centers in the city of Jeddah, Saudi Arabia. In particular, this paper aims to analyze the accessibility of COVID-19 vaccine centers in Jeddah city using maximal coverage location problems with and without constraint on the number and capacity of facilities. A maximal coverage model is first used to analyze the COVID-19 vaccination coverage of Jeddah districts with no restriction on the facility capacity. Then, a maximize capacitated coverage method is utilized to assess the centers' distribution and demand coverage with capacity constraints. Finally, the minimize facilities model is used to identify the most optimal location required to satisfy all demand points with the least number of facilities. The optimization approaches consider the objective function of minimizing the overall transportation time and travel distance to reduce wastage on the service rate provided to the patients. The optimization model is applied to a real-world case study in the context of the COVID-19 vaccination center in Jeddah. The results of this study provide valuable information that can help decision-makers locate and relocate COVID-19 centers more effectively under different constraints conditions.

County-Level Planning for Efficient Distribution of Emergency Medical Countermeasures with RealOpt Software.

Emergency preparedness systems plan for antibiotic distribution and vaccine administration to respond to public health threats. The arrival of a COVID-19 vaccine underscores the importance of organized logistics for rapid administration to populations. The US Centers for Disease Control and Prevention Cities Readiness Initiative encourages frontline responders from 72 US cities and metropolitan statistical areas to use planning software, such as RealOpt-POD-v8.0.2, to design dispensing operations and predict staffing needs. However, planning can be difficult for local jurisdictions given uncertainty about how long it may take to complete various processes during a dispensing operation, including assessment of countermeasure needs for each person (eg, based on age or pregnancy status) and the careful dispensing of countermeasures and accompanying education. The Union County Health Department in Ohio gathered data on the timing of typical processes for an anthrax medical countermeasures distribution site through a small-scale drill and used these data to parameterize a RealOpt model capable of serving the rural county's population of just over 50,000 people within 24 hours. Results help fill a gap in parameterizing RealOpt-based planning models by highlighting the use of a small-scale drill to inform time estimates, which can be applied to RealOpt as part of county-level planning in advance of larger-scale drills to evaluate dispensing capabilities and effectiveness. The findings provide a methodological basis of future resource typing for adaptable and scalable dispensing, particularly for rural areas. Both the approach and resulting antibiotics dispensing schematic presented here could be tailored to support planning for population-based countermeasure administration to combat emerging pandemics.

A Drive-through Simulation Tool for Mass Vaccination during COVID-19 Pandemic.

Several research and development teams around the world are working towards COVID-19 vaccines. As vaccines are expected to be developed and produced, preparedness and planning for mass vaccination and immunization will become an important aspect of the pandemic management. Mass vaccination has been used by public health agencies in the past and is being proposed as a viable option for COVID-19 immunization. To be able to rapidly and safely immunize a large number of people against SARS-CoV-2, different mass vaccination options are available. Drive-through facilities have been successfully used in the past for immunization against other diseases and for testing during COVID-19. In this paper we introduce a drive-through vaccination simulation tool that can be used to enhance the planning, design, operation, and feasibility and effectiveness assessment of such facilities. The simulation tool is a hybrid model that integrates discrete event and agent-based modeling techniques. The simulation outputs visually and numerically show the average processing and waiting times and the number of cars and people that can be served (throughput values) under different numbers of staff, service lanes, screening, registration, immunization, and recovery times.