Optimization models for patient allocation during a pandemic influenza outbreak.

Pandemic influenza has been an important public health concern. During the 20th century, three major pandemics of influenza occurred in 1918, 1957, and 1968. The pandemic of 1918 caused 40 to 50 million deaths worldwide and more than 500,000 deaths in the United States. The 1957 pandemic, during a time with much less globalization than now, spread to the U.S. within 4 to 5 months of its origination in China, causing more than 70,000 deaths in the U.S., and the 1968 pandemic spread to the U.S. from Hong Kong within 2 to 3 months, causing 34,000 deaths. Pandemic influenza is considered to be a relatively high probability event, even inevitable by many experts. During a pandemic influenza outbreak, some key preparedness tasks cannot be accomplished by hospitals individually; regional resource allocation, patient redistribution, and use of alternative care sites all require collaboration among hospitals both in planning and in response. The research presented in this dissertation develops optimization models to be used by decision makers (e.g. hospital associations, emergency management agency, etc.) to determine how best to manage medical resources as well as suggest patient allocation among hospitals and alternative healthcare facilities. Both single-objective and multi-objective optimization models are developed to determine the patient allocation and resource allocation among healthcare facilities. The single-objective optimization models are developed to optimize the patient allocation in terms of minimizing the travel distance between patients and healthcare facilities while considering medical resource capacity constraints. During the pandemic, the surge demand most likely would exhaust all the medical resources, at which time the models can help predict the potential resource shortage so an appropriate contingency plan can be developed. If additional resource quantities become available, the models help to determine the best allocation of these resources among healthcare facilities. Various methods are proposed to conduct the sensitivity analysis to help decision makers determine the impact of different level of each type resource on the patient service. The multi-objective optimization model not only considers the objective of minimization of the total travel distance by patients to healthcare facilities, but also considers the minimization of maximum patient travel distance. A case study from Metro Louisville, Kentucky is presented to demonstrate how the models would aid in patient allocation and resource allocation during a pandemic influenza outbreak. A web-based application based on the optimization models developed in this dissertation is presented as an initial tool for decision makers

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

Simulation-based optimization of mitigation strategies for pandemic influenza.

Millions of people have been infected and died as results of influenza pandemics in human history. In order to prepare for these disasters, it is important to know how the disease spreads. Further, intervention strategies should be implemented during the pandemics to mitigate their ill effects. Knowledge of how these interventions will affect the pandemic course is paramount for decision makers. This study develops a simulation-based optimization model which aims at finding a combination of strategies that result in the best value for an objective function of defined metrics under a set of constraints. Also, a procedure is presented to solve the optimization model. In particular, a simulation model for the spread of the influenza virus in case of a pandemic is presented that is based on the socio-demographic characteristics of the Jefferson County, KY. Then, School closure and home confinement are considered as the two intervention strategies that are investigated in this study and the simulation model is enhanced to incorporate the changes of the pandemic course (e.g. the number of ill individuals during the pandemic period) as results of the establishment of different scenarios for the intervention strategies. Finally, an optimization model is developed that its feasible region includes the feasible scenarios for establishment of intervention strategies (i.e. home confinement and school closure). The optimization model aims at finding an optimal combination of those two strategies to minimize the economic cost of the pandemic under a set of constraints on the control variables. Control variables include time, length of closure for schools, and the rate of home confinement of the individuals for home confinement strategy. This optimization model is connected to the pre-mentioned simulation model and is solved using a simulation-based optimization procedure called NSGS. Where the results of the analysis show both home confinement and school closure strategies are effective in terms of the outputs of the model (e.g. number of illness cases during the pandemic), they show home confinement is a more cost effective one

Graph Matching Based Decision Support Tools For Mitigating Spread Of Infectious Diseases Like H1N1

Diseases like H1N1 can be prevented from becoming a wide spread epidemic through timely detection and containment measures. Similarity of H1N1 symptoms to any common flu and its alarming rate of spread through animals and humans complicate the deployment of such strategies. We use dynamic implementation of graph matching methods to overcome these challenges. Specifically, we formulate a mixed integer programming model (MIP) that analyzes patient symptom data available at hospitals to generate patient graph match scores. Successful matches are then used to update counters that generate alerts to the Public Health Department when the counters surpass the threshold values. Since multiple factors like age, health status, etc., influence vulnerability of exposed population and severity of those already infected, a heuristic that dynamically updates patient graph match scores based on the values of these factors is developed. To better understand the gravity of the situation at hand and achieve timely containment, the rate of infection and size of infected population in a specific region needs to be estimated. To this effect, we propose an algorithm that clusters the hospitals in a region based on the population they serve. Hospitals grouped together affect counters that are local to the population they serve. Analysis of graph match scores and counter values specific to the cluster helps identify the region that needs containment attention and determine the size and severity of infection in that region. We demonstrate the application of our models via a case study on emergency department patients arriving at hospitals in Buffalo, NY

Stochastic programming and agent-based simulation approaches for epidemics control and logistics planning

This dissertation addresses the resource allocation challenges of fighting against infectious disease outbreaks. The goal of this dissertation is to formulate multi-stage stochastic programming and agent-based models to address the limitations of former literature in optimizing resource allocation for preventing and controlling epidemics and pandemics. In the first study, a multi-stage stochastic programming compartmental model is presented to integrate the uncertain disease progression and the logistics of resource allocation to control a highly contagious infectious disease. The proposed multi-stage stochastic program, which involves various disease growth scenarios, optimizes the distribution of treatment centers and resources while minimizing the total expected number of new infections and funerals due to an epidemic. Two new equity metrics are defined and formulated, namely infection and capacity equity, to explicitly consider equity for allocating treatment funds and facilities for fair resource allocation in epidemics control. The multi-stage value of the stochastic solution (VSS), demonstrating the superiority of the proposed stochastic programming model over its deterministic counterpart, is studied. The first model is applied to the Ebola Virus Disease (EVD) case in West Africa, including Guinea, Sierra Leone, and Liberia. In the following study, the previous model is extended to a mean-risk multi-stage vaccine allocation model to capture the influence of the outbreak scenarios with low probability but high impact. The Conditional Value at Risk (CVaR) measure used in the model enables a trade-off between the weighted expected loss due to the outbreak and expected risks associated with experiencing disastrous epidemic scenarios. A method is developed to estimate the migration rate between each infected region when limited migration data is available. The second study is applied to the case of EVD in the Democratic Republic of the Congo. In the third study, a new risk-averse multi-stage stochastic epidemics-ventilator-logistics compartmental stochastic programming model is developed to address the resource allocation challenges of mitigating COVID-19. This epidemiological logistics model involves the uncertainty of untested asymptomatic infections and incorporates short-term human migration. Disease transmission is also forecasted through deriving a new formulation of transmission rates that evolve over space and time with respect to various non-pharmaceutical interventions, such as wearing masks, social distancing, and lockdown. In the fourth study, a simulation-optimization approach is introduced to address the vaccination facility location and allocation challenges of the COVID-19 vaccines. A detailed agent-based simulation model of the COVID-19 is extended and integrated with a new vaccination center and vaccine-allocation optimization model. The proposed agent-based simulation-optimization framework simulates the disease transmission first and then minimizes the total number of infections over all the considered regions by choosing the optimal vaccine center locations and vaccine allocation to those centers. Specifically, the simulation provides the number of susceptible and infected individuals in each geographical region for the current time period as an input into the optimization model. The optimization model then minimizes the total number of estimated infections and provides the new vaccine center locations and vaccine allocation decisions for the following time period. Decisions are made on where to open vaccination centers and how many people should be vaccinated at each future stage in each region of the considered geographical location. Then these optimal decision values are imported back into the simulation model to simulate the number of susceptible and infected individuals for the subsequent periods. The agent-based simulation-optimization framework is applied to controlling COVID-19 in the states of New Jersey. The results provide insights into the optimal vaccine center location and vaccine allocation problem under varying budgets and vaccine types while foreseeing potential epidemic growth scenarios over time and spatial locations

A bi-objective optimization model to plan vaccination campaigns

Vaccination campaigns have saved thousands of lives, reaching the farthest places in the world. These campaigns have required substantial investments and accurate coordination between several actors within the vaccine supply chain. Despite these successful strategies, the outbreak of COVID-19 has altered the objectives and rules of undertaking vaccine campaigns. Then, it is essential to consider two new facts in planning vaccination campaigns. First, some groups of infected people by the virus are more vulnerable to severe illness. Second, the virus is exceptionally contagious; sometimes, no symptoms are apparent. Accordingly, we propose a bi-objective optimization model that allows healthcare decision-makers to design effective vaccination campaigns by considering these COVID-19 characteristics and controlling the associated costs. Careful utilization of temporary and traditional vaccination centers is crucial to creating a more robust strategy. Located in suitable places, temporary centers help increase the probability of reaching groups difficult to be vaccinated while simultaneously avoiding crowd congestion and reducing the risk of spreading infections in dispensing vaccination centers. Experiments were conducted using data from an area in Santiago, Chile. The results show the model allows us to manage the resources by providing a variety of vaccination plans according to the prioritization level of each objective