Optimal treatment allocations in space and time for on-line control of an emerging infectious disease

A key component in controlling the spread of an epidemic is deciding where, whenand to whom to apply an intervention.We develop a framework for using data to informthese decisionsin realtime.We formalize a treatment allocation strategy as a sequence of functions, oneper treatment period, that map up-to-date information on the spread of an infectious diseaseto a subset of locations where treatment should be allocated. An optimal allocation strategyoptimizes some cumulative outcome, e.g. the number of uninfected locations, the geographicfootprint of the disease or the cost of the epidemic. Estimation of an optimal allocation strategyfor an emerging infectious disease is challenging because spatial proximity induces interferencebetween locations, the number of possible allocations is exponential in the number oflocations, and because disease dynamics and intervention effectiveness are unknown at outbreak.We derive a Bayesian on-line estimator of the optimal allocation strategy that combinessimulation–optimization with Thompson sampling.The estimator proposed performs favourablyin simulation experiments. This work is motivated by and illustrated using data on the spread ofwhite nose syndrome, which is a highly fatal infectious disease devastating bat populations inNorth America

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

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

Estimating the impact and economic trade-offs of infectious disease control strategies using metapopulation models

Infectious diseases remain the main cause of death in low-income countries. Because of this, efforts to control the circulation of infectious agents are a priority for public policy makers. This control is challenged by a combination of complex disease dynamics, funding constraints or lack of political and societal commitment. These challenges are generally heterogeneous between geographical settings making the impact of control strategies hard to assess. In view of this, the purpose of this research is to integrate economic and epidemiological tools in order to improve support for disease control planning and implementation. To do this, I develop a metapopulation model framework to analyse the impact of control strategies when there are neighbouring populations with different epidemiological conditions. The results from this framework can be incorporated into further economic analysis and optimisations. The first section of this project aims to understand interventions’ effects when transmission intensity varies between populations. As a first approach, I implement the framework to analyse indirect effects of interventions for a transmission-stratified population, using generic models. Then, to contextualise the findings from the generic model, I analyse optimal intervention allocation for malaria control. Results from this section evidenced the importance of aligning local and global control strategies. The second section of this project focuses on understanding the consequences of disease control when intervention uptake varies between populations. For this, the metapopulation framework is applied to estimate the burden populations undergo due to the presence of an anti-vaccination movement. First, I analyse the burden of an outbreak of a vaccine preventable disease in a population where there are opposing vaccine acceptance views, implementing a measles transmission. Finally, I use the same approach to estimate the likely impact of vaccine hesitancy on the control of the COVID-19 pandemic. Results of this section highlight the importance of addressing vaccine hesitancy as a public health priorityOpen Acces

Ready or Not? Protecting the Public's Health in the Age of Bioterrorism, 2004

Examines ten key indicators to evaluate state preparedness to respond to bioterrorist attacks and other public health emergencies. Evaluates the federal government's role and performance, and offers recommendations for improving readiness

Dynamic optimization model for allocating medical resources in epidemic controlling

Purpose: The model proposed in this paper addresses a dynamic optimization model for allocating medical resources in epidemic controlling. Design/methodology/approach: In this work, a three-level and dynamic linear programming model for allocating medical resources based on epidemic diffusion model is proposed. The epidemic diffusion model is used to construct the forecasting mechanism for dynamic demand of medical resources. Heuristic algorithm coupled with MTLAB mathematical programming solver is adopted to solve the model. A numerical example is presented for testing the model’s practical applicability. Findings: The main contribution of the present study is that a discrete time-space network model to study the medical resources allocation problem when an epidemic outbreak is formulated. It takes consideration of the time evolution and dynamic nature of the demand, which is different from most existing researches on medical resources allocation. Practical implications: In our model, the medicine logistics operation problem has been decomposed into several mutually correlated sub-problems, and then be solved systematically in the same decision scheme. Thus, the result will be much more suitable for real operations. Originality/value: In our model, the rationale that the medical resources allocated in early periods will take effect in subduing the spread of the epidemic spread and thus impact the demand in later periods has been for the first time incorporated. A win-win emergency rescue effect is achieved by the integrated and dynamic optimization model. The total rescue cost is controlled effectively, and meanwhile, inventory level in each urban health departments is restored and raised gradually.Peer Reviewe

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

Evidence to recommendations frameworks for use of JYNNEOS\uae

03-OPX-Rao-508.pdf20211042

Shrinking the Malaria Map: A Prospectus on Malaria Elimination

\ud Thirty-nine countries across the world are making progress toward malaria elimination. Some are committed to nationwide elimination, while others are pursuing spatially progressive elimination within their borders. Influential donor and multilateral organizations are supporting their goals of achieving malaria-free status. With elimination back on the global agenda, countries face a myriad of questions. Should they change their programs to eliminate rather than control malaria? What tools are available? What policies need to be put into place? How will they benefit from elimination? Unfortunately, answers to these questions, and resources for agencies and country program managers considering or pursuing elimination, are scarce. The 39 eliminating countries are all positioned along the endemic margins of the disease, yet they naturally experience a variety of country characteristics and epidemiologies that make their malaria situations different from one another. The Malaria Elimination Group (MEG) and this Prospectus recognize\ud that there is no single solution, strategy, or time line that will be appropriate for every country, and each is encouraged to initiate a comprehensive evaluation of its readiness and strategy for elimination. The Prospectus is designed to guide countries in conducting these assessments. The Prospectus provides detailed and informed discussion on the practical means of achieving and sustaining zero transmission. It is designed as a road map, providing direction and options from which to choose an appropriate path. As on all maps, the destination is clearly marked, but the possible routes to reach it are numerous. The Prospectus is divided into two sections: Section 1 Eliminating Malaria comprises four chapters covering the strategic components important to the periods before, during, and after an elimination program. Section 2 Tools for the Job, comprises six chapters that outline basic information about how interventions in an elimination program will be different from those in a control setting. Chapter 1, Making the Decision, evaluates the issues that a country should consider when deciding whether or not to eliminate malaria. The chapter begins with a discussion about the quantitative and qualitative benefits that a country could expect from eliminating malaria and then recommends a thorough feasibility assessment. The feasibility assessment is based on three major components: operational, technical, and financial feasibility. Cross-border and regional collaboration is a key subject in this chapter. Chapter 2, Getting to Zero, describes changes that programs must consider when moving from sustained control to an elimination goal. The key strategic issues that must be addressed are considered, including supply chains, surveillance systems, intersectoral collaboration, political will, and legislative framework. Cross-border collaboration is again a key component in Getting to Zero. Chapter 3, Holding the Line, provides recommendations on how to conduct an assessment of two key factors that will affect preventing the reemergence of malaria once transmission is interrupted: outbreak risk and importation risk. The chapter emphasizes the need for a strong surveillance system in order to prevent and, if necessary, respond to imported cases. Chapter 4, Financing Elimination, reviews the cost-effectiveness of elimination as compared with sustained control and then presents the costs of selected elimination programs as examples. It evaluates four innovative financing mechanisms that must support elimination, emphasizing the need for predictable and stable financing. Case studies from Swaziland and two provinces in China are provided. Chapter 5, Understanding Malaria, considers malaria from the point of view of elimination and provides a concise overview of the current burden of the disease, malaria transmission, and the available interventions that can be used in an elimination program. Chapter 6, Learning from History, extracts important lessons from the Global Malaria Eradication Program and analyzes some elimination efforts that were successful and some that were unsuccessful. The chapter also reviews how the malaria map has been shrinking since 1900. xiv A Prosp ectus on Mala ria Elimi natio n\ud Chapter 7, Measuring Malaria for Elimination, provides a precise language for discussing malaria and gives the elimination discussion a quantitative structure. The chapter also describes the role of epidemiological theory and mathematical modeling in defining and updating an elimination agenda for malaria. Chapter 8, Killing the Parasite, outlines the importance of case detection and management in an elimination setting. Options for diagnosis, the hidden challenge of Plasmodium vivax in an elimination setting, and the impact of immunity are all discussed. Chapter 9, Suppressing the Vector, explores vector control, a necessary element of any malaria program. It considers optimal methods available to interrupt transmission and discusses potential changes, such as insecticide resistance, that may affect elimination efforts. Chapter 10, Identifying the Gaps — What We Need to Know, reviews the gaps in our understanding of what is required for elimination. The chapter outlines a short-term research agenda with a focus on the operational needs that countries are facing today. The Prospectus reviews the operational, technical, and financial feasibility for those working on the front lines and considers whether, when, and how to eliminate malaria. A companion document, A Guide on Malaria Elimination for Policy Makers, is provided for those countries or agencies whose responsibility is primarily to make the policy decisions on whether to pursue or support a malaria elimination strategy. The Guide is available at www.malaria eliminationgroup.org