Dynamic redistribution of mitigation resources during influenza pandemics
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.
References (112)
- et al.
Optimal vaccination policis for stochastic epdidemics among a population of households
Mathematical Biosciences
(2002) - et al.
Living with influenza: impacts of government imposed and voluntarily selected interventions
European Journal of Operational Research
(2009) - et al.
Finding optimal vaccination strategies for pandemic influenza using genetic algorithms
Journal of Theoretical Biology
(2005) - et al.
Towards a quantitative understanding of the within-host dynamics of influenza A infections
Epidemics
(2009) - et al.
Optimal vaccination strategies for a community of households
Mathematical Biosciences
(1997) - et al.
Using influenza-like illness data to reconstruct an influenza outbreak
Mathematical and Computer Modelling
(2008) - et al.
Confidence in vaccination: a parent model
Vaccine
(2005) - et al.
Parental decision-making for the varicella vaccine
Journal of Pediatric Health Care
(2001) - et al.
Exploring hepatitis B vaccination acceptance among young men who have sex with men: facilitators and barriers
Preventive Medicine
(2002) Cumulative number of confirmed human cases of Avian Influenza A(H5N1) reported to WHO
Pandemic (H1N1) 2009-update 112
Swine flu can become drug-resistant quickly: study
Spanish flu
Probable person-to-person transmission of avian influenza A (H5N1)
New England Journal of Medicine
A potential influenza pandemic: possible macroeconomic effects and policy issues
Pandemic influenza and the global vaccine supply
Clinical Infectious Deseases
Vaccine production
Pandemic (h1n1) 2009 vaccine deployment update – 17 december 2009
Pandemic influenza preparedness and response. Technical report
Vaccines against avian influenza – a race against time
New England Journal of Medicine
Development of a triage protocol for critical care during an influenza pandemic
Canadian Medical Association Journal
Major issues and challenges of influenza pandemic preparedness in developing countries
Emerging Infectious Diseases
Preparing for pandemic influenza
HHS pandemic influenza plan
A Bayesian (MCMC) approach to study transmission of influenza: application to household longitudinal data
Statistics in Medicine
Household and community transmission parameters from final distributions of infections in households
Biometrics
The generalized discrete-time epidemic model with immunity: a synthesis
Mathematical Bioscience
Detecting human-to-human transmission of avian influenza A (H5N1)
Emerging Infectious Diseases
Factors that make an infectious disease outbreak controllable
PNAS
Repeated influenza vaccination of healthy children and adults:borrow now, pay later?
Epidemiology and Infection
Simple models of influenza progression within a heterogeneous population
Operations Research
Simple models for containment of a pandemic
Journal of Royal Society Interface
Stochastic modelling of the spatial spread of influenza in germany
Austrian Journal of Statistics
A population-dynamic model for evaluating the potential spread of drug-resistant influenza virus infections during community-based use of antivirals
Journal of Antimicrobial Chemotherapy
Spatial considerations for the allocation of pre-pandemic influenza vaccination in the United States
Proceedings of the Royal Societyof London Biological Sciences
Simulation suggests that rapid activation of social distancing can arrest epidemic development due to a novel strain of influenza
BMC Public Health
Analysis of the effectiveness of interventions used during the 2009 A/H1N1 influenza pandemic
BioMed Central
Measures against transmission of pandemic H1N1 influenza in Japan in 2009: simulation model
Euro surveillance: bulletin européen sur les maladies transmissibles = European Communicable Disease Bulletin
A small community model for the transmission of infectious diseases: comparison of school closure as an intervention in individual-based models of an influenza pandemic
PLoS One
Containing pandemic influenza with antiviral agents
American Journal of Epidemiology
Strategies for mitigating an influenza pandemic
Nature
A large scale simulation model for assessment of societal risk and development of dynamic mitigation strategies
IIE Transactions
Strategies for containing an emerging influenza pandemic in Southeast Asia
Nature
Mitigation strategies for pandemic influenza in the United States
PNAS
Developing dynamic predictive strategies for mitigation of cross-regional pandemic outbreaks
IIE Transactions (in review)
Reducing the impact of the next influenza pandemic using household-based public health interventions
PLoS Medicine
Targeted social distancing design for pandemic influenza
Emerging Infectious Diseases
Initial human transmission dynamics of the pandemic (H1N1) 2009 virus in North America
Influenza Other Respi Viruses
Quantifying the routes of transmission for pandemic influenza
Bulletin of Mathematical Biology
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2022, Sustainable Cities and SocietyCitation Excerpt :Several models have focused on combining SIR models and logistics models. For instance, Savachkin and Uribe (2012) proposed an SIR model to distribute the resources that minimizes the impact of pandemics on public healthcare sectors. Rachaniotis et al. (2012) investigated a scheduling model using an SIR model to allocate the limited medical resources during the pandemics.
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2021, Transportation Research Part E: Logistics and Transportation ReviewCitation Excerpt :Exceptions include Rachaniotis et al. (2012), who proposed a deterministic model for disaster management during an epidemic considering uncertain demand and distributed, limited resources, and Liu and Zhang (2016), who proposed a time series model to forecast errors for shipping and resource allocation. The vaccine distribution problem during a pandemic was covered by Savachkin and Uribe (2012), who considered a dynamic model to distribute medicine based on disease propagation, and Sun et al. (2014) and Enayati and Özaltın (2020), who offered advice for healthcare authorities regarding vaccine storage facility location selection, distribution plan efficiency, and resource allocation. Models have also been proposed for the allocation phase of the vaccine.
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2021, Transportation Research Part E: Logistics and Transportation ReviewCitation Excerpt :In the first area, articles have highlighted resource shortages as a major obstacle during an epidemic (Enayati and Özaltın, 2020; Liu et al., 2020; Parvin et al., 2018; Rachaniotis et al., 2012; Savachkin and Uribe, 2012; Sun et al., 2014). Consequently, these studies offered various strategies for allocating minimal or further resources, such as controlling transportation costs and equitable policies (Savachkin and Uribe, 2012); undertaking threshold policy for inventory balancing; optimal area-based trans-shipment policy and planning horizon (Parvin et al., 2018); increasing capacity to manage disruptions (Hessel, 2009; Sun et al., 2014); implementing cost-sharing contracts (Mamani et al., 2013) or coordinating contracts (Chick et al., 2008); and appropriate capacity setting and the minimum budget (Liu et al., 2020). These studies mostly looked at the influenza epidemic, while a few were focused on outbreaks of ebola and malaria (Büyüktahtakın et al., 2018).