Operational research for the safe and effective design of COVID-19 mass vaccination centres

Use of linked patient data to assess the effect of Long-COVID on system-wide healthcare utilisation

Background: Within the relatively early stages of the COVID-19 pandemic, there had been an awareness of the potential longer-term effects of infection (so called Long-COVID) but little was known of the ongoing demands such patients may place on healthcare services. Objective: To investigate whether COVID-19 illness is associated with increased post-acute healthcare utilisation. Method: Using linked data from primary care, secondary care, mental health and community services, activity volumes were compared across the 3 months preceding and proceeding COVID-19 diagnoses for 7,791 individuals, with a distinction made between whether or not patients were hospitalised for treatment. Differences were assessed against those of a control group containing individuals who had not received a COVID-19 diagnosis. All data were sourced from the authors' healthcare system in South West England. Results: For hospitalised COVID-19 cases, a statistically significant increase in non-elective admissions was identified for males and females <65 years. For non-hospitalised cases, statistically significant increases were identified in GP Doctor and Nurse attendances and GP prescriptions (males and females, all ages); Emergency Department attendances (females <65 years); Mental Health contacts (males and females ≥65 years); and Outpatient consultations (males ≥65 years). Conclusion: There is evidence of an association between positive COVID-19 diagnosis and increased post-acute activity within particular healthcare settings. Linked patient-level data provides information that can be useful to understand ongoing healthcare needs resulting from Long-COVID, and support the configuration of Long-COVID pathways of care.

Simulation modeling assessment and improvement of a COVID-19 mass vaccination center operations

The development of safe and effective vaccines against COVID-19 has been a turning point in the international effort to control this disease. However, vaccine development is only the first phase of the COVID-19 vaccination process. Correct planning of mass vaccination is important for any policy to immunize the population. For this purpose, it is necessary to set up and properly manage mass vaccination centers. This paper presents a discrete event simulation model of a real COVID-19 mass vaccination center located in Sfax, Tunisia. This model was used to evaluate the management of this center through different performance measures. Three person's arrival scenarios were considered and simulated to verify the response of this real vaccination center to arrival variability. A second model was proposed and simulated to improve the performances of the vaccination center. Like the first model, this one underwent the same evaluation process through the three arrivals scenarios. The simulation results show that both models respond well to the arrival's variability. Indeed, most of the arriving persons are vaccinated on time for all the studied scenarios. In addition, both models present moderate average vaccination and waiting times. However, the average utilization rates of operators are modest and need to be improved. Furthermore, both simulation models show a high average number of persons present in the vaccination center, which goes against the respect of the social distancing condition. Comparison between the two simulation models shows that the proposed model is more efficient than the actual one.

A queueing Network approach for capacity planning and patient Scheduling: A case study for the COVID-19 vaccination process in Colombia

This paper considers the problem of patient scheduling and capacity planning for the vaccination process during the COVID-19 pandemic. The proposed solution is based on a non-linear mathematical modeling approach representing the dynamics of an open Jackson Network and a Generalized Network. To test these models, we proposed three objective functions and analyzed different configurations of the process corresponding to various levels of the models' parameters as well as the conditions present in the case study. To assess the computational performance of the models, we also experimented with larger instances in terms of number of steps or stations used and number of patients scheduled. The computational results show how parameters such as the minimum percentage of patients served, the maximum occupation allowed per station and the objective functions used have an impact on the configuration of the process. The proposed approach can support the decision-making process in vaccination centers to efficiently assign human and material resources to maximize the number of patients vaccinated while ensuring reasonable waiting times, number of patients in queue and servers' utilization rates, which in turn are key to avoid overcrowding and other negative conditions in the system that could increase the risk of infections.

Differences in Demographics of Vaccinees, Access to, and Satisfaction with SARS-CoV-2 Vaccination Procedures between German General Practices and Mass Vaccination Centers

In the European Union, SARS-CoV-2 vaccines became available in December 2020. The vaccination campaign in Germany was initially implemented through mass vaccination centers and later joined by general practitioners (GPs) in spring 2021. This study compared population characteristics, perceived access barriers, and satisfaction with the vaccination procedure between vaccination centers and GP practices. A paper-based survey was distributed (07/2021-10/2021) among newly vaccinated individuals in ten GP practices (n = 364) and two vaccine centers (n = 474). Participants in vaccine centers were younger compared to participants in GP practices. GP preference was higher in older participants and those with pre-existing illnesses. Wait time at vaccination site was longer in GP practices, whereas travel distance to site was longer for participants in vaccine centers. However, satisfaction with patient education and recommendation of site were more likely with increasing comprehensibility of the vaccination procedure and physicians' information as well as perceived sufficiency of patient education duration, factors that can be easily modified by all vaccination sites. Our results demonstrate that both types of vaccination sites complement each other in terms of accessibility and target population and that satisfaction with the vaccination procedure can be promoted at all sites by an easy-to-understand process.

Mass vaccinations against COVID-19 through the use of technologies for the management of appointment scheduling and data of large volumes of vaccinated

Mass vaccination against COVID-19 using technologies to manage appointment scheduling and data in large volumes of vaccinated people Abstract Mass vaccination poses a challenge for health authorities due to the high volume of people who need to be vaccinated in a short period of time. Manual processes in vaccination centres to record and control vaccinations where the data is entered on paper result in delays in the timely input of information rendering the vaccination process inefficient. The proposed prototype, as a strategy for mass COVID-19 vaccination, to generate appointments, record, and control entry to vaccination centres, uses mobile technology, QR codes, and cloud computing to automate these data-driven processes. Technology-based processes help people by giving them the flexibility to choose the most convenient vaccination centre and provide health authorities with data-driven tools for management, control, and real-time decision-making.

Modelling vaccination capacity at mass vaccination hubs and general practice clinics: a simulation study

Background

COVID-19 mass vaccination programs place an additional burden on healthcare services. We aim to model the queueing process at vaccination sites to inform service delivery.

Methods

We use stochastic queue network models to simulate queue dynamics in larger mass vaccination hubs and smaller general practice (GP) clinics. We estimate waiting times and daily capacity based on a range of assumptions about appointment schedules, service times and staffing and stress-test these models to assess the impact of increased demand and staff shortages. We also provide an interactive applet, allowing users to explore vaccine administration under their own assumptions.

Results

Based on our assumed service times, the daily throughput for an eight-hour clinic at a mass vaccination hub ranged from 500 doses for a small hub to 1400 doses for a large hub. For GP clinics, the estimated daily throughput ranged from about 100 doses for a small practice to almost 300 doses for a large practice. What-if scenario analysis showed that sites with higher staff numbers were more robust to system pressures and mass vaccination sites were more robust than GP clinics.

Conclusions

With the requirement for ongoing COVID-19 booster shots, mass vaccination is likely to be a continuing feature of healthcare delivery. Different vaccine sites are useful for reaching different populations and maximising coverage. Stochastic queue networks offer a flexible and computationally efficient approach to simulate vaccination queues and estimate waiting times and daily throughput to inform service delivery.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12913-022-08447-8.

Vacunaciones masivas contra el COVID-19 mediante el uso de las tecnologías para la gestión de programación de citas y de datos de grandes volúmenes de vacunados

Las vacunaciones masivas son un desafío al que se enfrentan las autoridades sanitarias, debido al alto volumen de ciudadanos que deben de ser vacunados en un corto tiempo. Los procesos manuales en los centros de vacunación para el registro y control de las vacunas donde se emplea el papel como elemento repositorio de los datos, generan retrasos en la entrega oportuna de la información procesada y el proceso de vacunación se vuelve ineficiente. El prototipo propuesto como estrategia de vacunación masiva contra el COVID-19 para la generación de citas, registro y control del ingreso a los centros de vacunación utiliza las tecnologías móviles, código QR y Cloud Computing, para la automatización de estos procesos basados en datos. Los procesos apoyados en la tecnología ayudan al ciudadano por la flexibilidad de elegir el centro de vacunación más conveniente a su realidad y permiten a las autoridades sanitarias disponer de herramientas basadas en datos para la gestión, el control y la toma decisiones en tiempo real.