Simulation and Optimization Modeling for Drive-Through Mass Vaccination – A Generalized Approach

Proper planning and execution of mass vaccination at the onset of a pandemic outbreak is important for local health departments. Mass vaccination clinics are required to be setup and run for naturally occurring pandemic outbreaks or even in response to terrorist attacks, e.g., anthrax attack. Walk-in clinics have often been used to administer vaccines. When a large percentage of a population must be vaccinated to mitigate the ill-effects of an attack or pandemic, drive-through clinics appear to be more effective because a much higher throughput can be achieved when compared to walk-in clinics. There are other benefits as well. For example, the spread of the disease can be minimized because infected patients are not exposed to uninfected patients. This research extends the simulation modeling work that was done for a mass vaccination drive-through clinic in the city of Louisville in November 2009. This clinic is the largest clinic set up in Louisville with more than 19,000 patients served, over two-thirds via ten drive-through lanes. The intent of the model in this paper is to illustrate a general tool that can be customized for a community of any size. The simulation-optimization tool will allow decision makers to investigate several interacting control variables in a simultaneous fashion; any of several criterion models in which various performance measures are either optimized or constrained, can be investigated. The model helps the decision maker determine the required number of Points of Dispense (POD) lanes, number and length of the lanes for consent hand outs and fill in, staff needed at the consent handout stations and PODs, and average user waiting time in the system

Drive-through vaccinations prove successful in immunizing mountain communities against tick-borne encephalitis during the Covid-19 pandemic

In March 2020 the covid-19 pandemic led to the abruption of most of the routine outpatient activities in the Italian hospitals and Prevention Departments, including those vaccinations which were not urgent and/or scheduled for the age 0-6. As soon as a milder phase of the pandemic made it possible, since June 2020, in the mountain territory of the province of Belluno (Veneto, North-East of Italy), in the Alps, 12.152 doses of vaccine against tick-borne encephalitis have been administered by means of the innovative “drive-through” modality. No significant adverse events occurred and the demand by the population has kept growing since the previous year, proving the “drive-through” to be safe, efficient and successful

Literature Review - the vaccine supply chain

Vaccination is one of the most effective ways to prevent the outbreak of an infectious disease. This medical intervention also brings about many logistical quest

Literature review: The vaccine supply chain

Vaccination is one of the most effective ways to prevent and/or control the outbreak of infectious diseases. This medical intervention also brings about many logistical questions. In the past years, the Operations Research/Operations Management community has shown a growing interest in the logistical aspects of vaccination. However, publications on vaccine logistics often focus on one specific logistical aspect. A broader framework is needed so that open research questions can be identified more easily and contributions are not overlooked.In this literature review, we combine the priorities of the World Health Organization for creating a flexible and robust vaccine supply chain with an Operations Research/Operations Management supply chain perspective. We propose a classification for the literature on vaccine logistics to structure this relatively new field, and identify promising research directions. We classify the literature into the following four components: (1) product, (2) production, (3) allocation, and (4) distribution. Within the supply chain classification, we analyze the decision problems for existing outbreaks versus sudden outbreaks and developing countries versus developed countries. We identify unique characteristics of the vaccine supply chain: high uncertainty in both supply and demand; misalignment of objectives and decentralized decision making between supplier, public health organization and end customer; complex political decisions concerning allocation and the crucial

Numerical computation of rare events via large deviation theory

An overview of rare events algorithms based on large deviation theory (LDT) is presented. It covers a range of numerical schemes to compute the large deviation minimizer in various setups, and discusses best practices, common pitfalls, and implementation trade-offs. Generalizations, extensions, and improvements of the minimum action methods are proposed. These algorithms are tested on example problems which illustrate several common difficulties which arise e.g. when the forcing is degenerate or multiplicative, or the systems are infinite-dimensional. Generalizations to processes driven by non-Gaussian noises or random initial data and parameters are also discussed, along with the connection between the LDT-based approach reviewed here and other methods, such as stochastic field theory and optimal control. Finally, the integration of this approach in importance sampling methods using e.g. genealogical algorithms is explored

Learning about a Moving Target in Resource Management: Optimal Bayesian Disease Control

Resource managers must often make difficult choices in the face of imperfectly observed and dynamically changing systems (e.g., livestock, fisheries, water, and invasive species). A rich set of techniques exists for identifying optimal choices when that uncertainty is assumed to be understood and irreducible. Standard optimization approaches, however, cannot address situations in which reducible uncertainty applies to either system behavior or environmental states. The adaptive management literature overcomes this limitation with tools for optimal learning, but has been limited to highly simplified models with state and action spaces that are discrete and small. We overcome this problem by using a recently developed extension of the Partially Observable Markov Decision Process (POMDP) framework to allow for learning about a continuous state. We illustrate this methodology by exploring optimal control of bovine tuberculosis in New Zealand cattle. Disease testing—the control variable—serves to identify herds for treatment and provides information on prevalence, which is both imperfectly observed and subject to change due to controllable and uncontrollable factors. We find substantial efficiency losses from both ignoring learning (standard stochastic optimization) and from simplifying system dynamics (to facilitate a typical, simple learning model), though the latter effect dominates in our setting. We also find that under an adaptive management approach, simplifying dynamics can lead to a belief trap in which information gathering ceases, beliefs become increasingly inaccurate, and losses abound

A Mathematical Model for Co-Evolution of Pandemic and Infodemic with Vaccine

Vaccine hesitancy, resulting from bad information, threatens the possibility of ending the COVID-19 pandemic through mass vaccination. The COVID-19 pandemic coincides with an overabundance of controversial information regarding disease transmission and public health mitigation approaches. We investigate a phenomenological co-evolution of pandemic and infodemic in the context of COVID-19 with an emphasis on evolutionary game theory. Using bifurcation analysis, we determine the limit cycle boundaries and the separation of attraction between stable foci of infection and periodic outbreaks of infection. Our results suggest that low risk perception of vaccination relative to infection is not sufficient to eradicate the disease; promotion of quarantine methods or targeted mitigation of the spread of corona-misinformation is necessary to drive the system to disease free equilibrium