As recently pointed out by the Institute of Medicine, the existing
October 1, 2017
As recently pointed out by the Institute of Medicine, the existing pandemic mitigation models lack the dynamic decision support capability. assess the impact of variability of some critical factors on policy performance. The model is intended to support public health policy making for effective distribution of limited Indaconitin supplier mitigation resources. 1. Introduction As of July Rabbit Polyclonal to GPR132 2010, WHO has reported 501 confirmed human cases of avian influenza A/(H5N1) which resulted in 287 deaths worldwide . At the same time, the statistics for the H1N1 2009 outbreak has so far included 214 countries Indaconitin supplier with a total reported number of infections and deaths of 419,289 and 18,239, respectively . Today, an ominous expectation exists that the next pandemic will be triggered by a highly pathogenic virus, to which there is little or no pre-existing immunity in humans . The nation’s ability to mitigate a pandemic influenza depends on Indaconitin supplier 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, reaching an adequate vaccine supply can currently take up to nine months [4, 5]. Even if the existing vaccines prove to be potent, their availability will be limited by high production and inventory costs [6, 7] and also will be constrained by the supply of antiviral drugs, healthcare providers, hospital beds, medical supplies, and logistics. Hence, pandemic mitigation will have to be done amidst limited availability of resources and supporting infrastructure. This challenge has been acknowledged by WHO  and echoed by the HHS and CDC [8, 9]. The existing models on pandemic influenza (PI) containment and mitigation aims to address various complex aspects of the pandemic evolution process including: (i) the mechanism of disease progression, from the initial contact and infection transmission to the asymptomatic phase, manifestation of symptoms, and the final health outcome [10C12], (ii) the population dynamics, including individual susceptibility [13, 14] and transmissibility [10, 15C17], and behavioral factors affecting infection generation and effectiveness of interventions [18C20], (iii) the impact of pharmaceutical and nonpharmaceutical measures, including vaccination [21C23], antiviral therapy [24C26], social distancing [27C31] and travel Indaconitin supplier restrictions, and the use of low-cost measures, such as face masks and hand washing [26, 32C34]. Recently, the modeling efforts have focused on combining pharmaceutical and nonpharmaceutical interventions in search for synergistic strategies, aimed at better resource utilization. Most of such approaches attempt implementing a form of social distancing followed by application of pharmaceutical measures. For significant contributions in this area see [33, 35C41]. One of the most notable among these efforts is a 2006-07 initiative by MIDAS , which cross-examined independent simulation models of PI spread Indaconitin supplier in rural areas of Asia [43, 44], USA and UK [45, 46], and the city of Chicago , respectively. MIDAS cross-validated the models by simulating the city of Chicago, with 8.6M inhabitants and implementing a targeted layered containment [48, 49]. The research findings of MIDAS and some other groups [12, 33] were used in a recent Modeling Community Containment for Pandemic Influenza report by IOM, to formulate a set of recommendations for PI mitigation . These findings were also used in a pandemic preparedness guidance developed by CDC . At the same time, The IOM report  points out several of the MIDAS models, observing that because of the significant constraints placed on the models the scope of models should be expanded. The IOM recommends to adapt or develop that can provide and include the of intervention strategies. Our literature review yields a similar observation that most existing approaches focus on assessment of defined strategies, and virtually none of the models are capable of that is, adapting to changes in the pandemic progress, or even predicting them, to generate strategies. Such a strategy has the advantage of being developed dynamically, as the pandemic spreads, by selecting a mix of available mitigation options at each decision epoch, based on both the present state of the pandemic and its predicted evolution. In an attempt to address the IOM recommendations, we present a simulation optimization model for developing predictive resource distribution over a network of regional outbreaks. The underlying simulation model mimics the disease and population dynamics of each of the affected regions (Sections 2.1 and 2.2). As the pandemic spreads from region to region, the optimization model distributes mitigation resources, including stockpiles of vaccines and antiviral and administration capacities (Section 2.3). The model seeks to minimize the impact of ongoing outbreaks and the expected impact of outbreaks, using measures of morbidity, mortality, and.