Dynamic redistribution of mitigation resources during influenza pandemics

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Abstract

The Institute of Medicine (IOM) has pointed out that the existing pandemic mitigation models lack the dynamic decision support capability. In this paper, we present a simulation optimization model to generate dynamic strategies for distribution of limited mitigation resources, such as vaccines and antivirals, over a network of regional outbreaks. The model has the capability to redistribute the resources remaining from previous allocations in response to changes in the pandemic progress. The model strives to minimize the impact of ongoing outbreaks and the expected impact of potential outbreaks, considering measures of morbidity, mortality, and social distancing, translated into the societal and economic costs of lost productivity and medical services. The model is implemented on a simulated H5N1 outbreak involving four counties in the state of Florida, U.S. with over four million inhabitants. The performance of our strategy is compared to that of a myopic distribution strategy. Sensitivity analysis is performed to assess the impact of variability of some critical factors on policy performance. The methodology is intended to support public health policy on effective distribution of limited mitigation resources.

Introduction

As of October 2010, the World Health Organization (WHO) has reported 505 confirmed human cases of avian influenza A/(H5N1), which resulted in 300 deaths [1]. At the same time, worldwide more than 214 countries and overseas territories have reported laboratory confirmed cases of pandemic influenza (PI) H1N1 2009, including over 18,449 deaths. [2]. Because of a continuous reassortment and mutation of the influenza virus, an ominous expectation exists today that the next pandemic will be triggered by a highly pathogenic strain to which there is little or no pre-existing immunity in humans [3], [4], [5]. A study by the Congressional Budget Office estimates that the consequences of a severe pandemic could, in the U.S. alone, include 200 million people infected, 90 million clinically ill, and 2 million dead [6]. The study estimates that 30 percent of all workers would become ill and 2.5 percent would die, with 30 percent of workers missing a mean of three weeks of work. Furthermore, 18 million to 45 million people would require outpatient care, and economic costs would total approximately $675 billion.

The nation’s ability to mitigate a pandemic influenza depends on the available emergency response resources and infrastructure, and at present, challenges abound. Predicting the exact virus subtype remains a difficult task, and even when identified, a surge production of an adequate vaccine supply can currently take up to nine months [7], [8]. Even if the existing vaccines prove to be potent, their availability will be limited by high production and inventory costs [9], [10]. Also will be constrained the supply of antiviral agents, healthcare providers, hospital beds, medical supplies, personal protective equipment, and logistics [11], [12], [13]. Hence, pandemic mitigation will have to be done amidst limited availability of resources and supporting infrastructure. The pressing need for strategies aimed at efficient distribution of limited resources has been acknowledged by WHO [10] and echoed by the U.S. Department of Health and Human Services (HHS) and the Centers for Disease Control and Prevention (CDC) [14], [15].

Section snippets

Current literature

The existing models on PI containment and mitigation can broadly be classified into: (i) statistical, (ii) mathematical, (iii) simulation-based, and (iv) combinations of thereof. In what follows, we present a summary survey of these approaches mostly focusing on simulation-based models.

The statistical models driven mainly by likelihood and regression-based approaches have primarily been used for assessment of epidemiological parameters and estimation of pandemic impact [16], [17], [18]. These

Methodology

Our methodology generates dynamic progressive strategies for distribution of limited mitigation resources over a network of regional outbreaks. It incorporates: (i) a cross-regional simulation model, (ii) a set of single-region simulation models, and (iii) an optimization model.

We consider a network of regions each of which classified as either unaffected, ongoing, or contained outbreak (Fig. 1). The cross-regional simulation model connects the regions by air and land travel. The single-region

Testbed illustration

To illustrate the use of our DPO methodology, we implemented a sample H5N1 outbreak scenario including four counties in Florida: Miami Dade, Hillsborough, Duval, and Leon. The counties contain four major and densely populated metropolitan areas and transportation hubs of the state: Miami, Tampa, Jacksonville, and Tallahassee, respectively (see Fig. 6). The respective populations of these counties are 2.2, 1.0, 0.8, and 0.25 million people. A basic unit of time for population and disease

Conclusions

A recent report by the Institute of Medicine points out that the existing models for PI mitigation fall short of providing dynamic decision support. The report recommends “that policymakers consider a broader set of models to inform strategies and policies regarding pandemic influenza” [66]. In this paper, we present a large-scale simulation optimization model which attempts to fill this gap.

The model supports dynamic predictive resource distribution over a network of regions exposed to the

Acknowledgments

We would like to acknowledge with thanks the many helpful suggestions made by Prof. Yiliang Zhu, Department of Epidemiology and Biostatistics at the University of South Florida, Tampa, FL, USA.

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