Effects of Internal Border Control on Spread of Pandemic Influenza

Wood, James G.; Zamani, Nasim; MacIntyre, C. Raina; Becker, Niels G.

doi:10.3201/eid1307.060740

Cited by 13 publications

(17 citation statements)

References 13 publications

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“…This included three that specifically evaluated the effect of air travel restrictions in mitigating a pandemic. They observed that unless air travel restrictions were imposed in approximately 100% of the affected countries, there would be no effect on influenza spread, although these measures delayed the peak of the influenza epidemic to varying degrees (Cooper et al., 2006; Hollingsworth et al., 2006; Wood et al., 2007). One article studied the effect of travel restriction between neighbouring communities during a pandemic with similar results (Nigmatulina and Larson, 2009).…”

Section: Resultsmentioning

confidence: 99%

A Review of Simulation Modelling Approaches Used for the Spread of Zoonotic Influenza Viruses in Animal and Human Populations

Dorjee

Poljak

Revie

et al. 2012

Zoonoses and Public Health

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Increasing incidences of emerging and re-emerging diseases that are mostly zoonotic (e.g. severe acute respiratory syndrome, avian influenza H5N1, pandemic influenza) has led to the need for a multidisciplinary approach to tackling these threats to public and animal health. Accordingly, a global movement of 'One-Health/One-Medicine' has been launched to foster collaborative efforts amongst animal and human health officials and researchers to address these problems. Historical evidence points to the fact that pandemics caused by influenza A viruses remain a major zoonotic threat to mankind. Recently, a range of mathematical and computer simulation modelling methods and tools have increasingly been applied to improve our understanding of disease transmission dynamics, contingency planning and to support policy decisions on disease outbreak management. This review provides an overview of methods, approaches and software used for modelling the spread of zoonotic influenza viruses in animals and humans, particularly those related to the animal-human interface. Modelling parameters used in these studies are summarized to provide references for future work. This review highlights the limited application of modelling research to influenza in animals and at the animal-human interface, in marked contrast to the large volume of its research in human populations. Although swine are widely recognized as a potential host for generating novel influenza viruses, and that some of these viruses, including pandemic influenza A/H1N1 2009, have been shown to be readily transmissible between humans and swine, only one study was found related to the modelling of influenza spread at the swine-human interface. Significant gaps in the knowledge of frequency of novel viral strains evolution in pigs, farm-level natural history of influenza infection, incidences of influenza transmission between farms and between swine and humans are clearly evident. Therefore, there is a need to direct additional research to the study of influenza transmission dynamics in animals and at the animal-human interface.

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Section: Resultsmentioning

confidence: 99%

A Review of Simulation Modelling Approaches Used for the Spread of Zoonotic Influenza Viruses in Animal and Human Populations

Dorjee

Poljak

Revie

et al. 2012

Zoonoses and Public Health

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“…During the 2009 H1N1 pandemic, countries including China, Japan, Hong Kong, Singapore, and Australia implemented various border control strategies to prevent arriving airline passengers from infecting the countries' citizens [32], [33], [34], [35]. Health authorities screened arriving passengers for flu-like symptoms using thermal scanners.…”

Section: Discussionmentioning

confidence: 99%

“…These screenings will miss asymptomatic individuals [35], and in particular infected individuals with no fever. However border control has been shown to delay the local community transmission [32], [33], [34], which may allow sufficient time for health interventions, such as vaccinations. On the other hand, border control may only delay transmission and could still result in a second wave, as seen in the simulations.…”

Section: Discussionmentioning

confidence: 99%

“…Using this along with the CDC compiled data, the influenza confirmed case data was adjusted to reflect underreporting by a scale factor of 700∶1. The authors are aware that a team of investigators estimated an underreporting scale factor of 79∶1 at the beginning (April through July) of the pandemic [32]. This is not a contradiction, since, very quickly, many physicians and hospitals stopped testing and the underreporting factor skyrocketed.…”

Section: Methodsmentioning

confidence: 98%

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A Perspective on Multiple Waves of Influenza Pandemics

Mummert

Weiss

et al. 2013

PLoS ONE

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BackgroundA striking characteristic of the past four influenza pandemic outbreaks in the United States has been the multiple waves of infections. However, the mechanisms responsible for the multiple waves of influenza or other acute infectious diseases are uncertain. Understanding these mechanisms could provide knowledge for health authorities to develop and implement prevention and control strategies.Materials and MethodsWe exhibit five distinct mechanisms, each of which can generate two waves of infections for an acute infectious disease. The first two mechanisms capture changes in virus transmissibility and behavioral changes. The third mechanism involves population heterogeneity (e.g., demography, geography), where each wave spreads through one sub-population. The fourth mechanism is virus mutation which causes delayed susceptibility of individuals. The fifth mechanism is waning immunity. Each mechanism is incorporated into separate mathematical models, and outbreaks are then simulated. We use the models to examine the effects of the initial number of infected individuals (e.g., border control at the beginning of the outbreak) and the timing of and amount of available vaccinations.ResultsFour models, individually or in any combination, reproduce the two waves of the 2009 H1N1 pandemic in the United States, both qualitatively and quantitatively. One model reproduces the two waves only qualitatively. All models indicate that significantly reducing or delaying the initial numbers of infected individuals would have little impact on the attack rate. Instead, this reduction or delay results in a single wave as opposed to two waves. Furthermore, four of these models also indicate that a vaccination program started earlier than October 2009 (when the H1N1 vaccine was initially distributed) could have eliminated the second wave of infection, while more vaccine available starting in October would not have eliminated the second wave.

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“…However, the full extent to which border control measures actually contribute to realworld epidemic preventions is still rather unknown. While some reports [33][34][35][36] provided rather pessimistic estimations on the effectiveness of border control, recent evaluations of the entry screening policies adopted by different nations during the 2009 H1N1 pandemic [37] demonstrated that long-range travel control significantly delayed disease transmission, relative to countries which did not employ a strategy for screening of incoming passengers. Due to such conflicting reports, more studies on the relevance of border control measures are needed.…”

Section: Entry Screening Of Long-range Travelersmentioning

confidence: 99%

Epidemic spreading on hierarchical geographical networks with mobile agents

Xiao

Zhao

Hadzibeganovic

et al. 2014

Communications in Nonlinear Science and Numerical Simulation

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a b s t r a c tHierarchical geographical traffic networks are critical for our understanding of scaling laws in human trajectories. Here, we investigate the susceptible-infected epidemic process evolving on hierarchical networks in which agents randomly walk along the edges and establish contacts in network nodes. We employ a metapopulation modeling framework that allows us to explore the contagion spread patterns in relation to multi-scale mobility behaviors. A series of computer simulations revealed that a shifted power-law-like negative relationship between the peak timing of epidemics s 0 and population density, and a logarithmic positive relationship between s 0 and the network size, can both be explained by the gradual enlargement of fluctuations in the spreading process. We employ a semianalytical method to better understand the nature of these relationships and the role of pertinent demographic factors. Additionally, we provide a quantitative discussion of the efficiency of a border screening procedure in delaying epidemic outbreaks on hierarchical networks, yielding a rather limited feasibility of this mitigation strategy but also its nontrivial dependence on population density, infector detectability, and the diversity of the susceptible region. Our results suggest that the interplay between the human spatial dynamics, network topology, and demographic factors can have important consequences for the global spreading and control of infectious diseases. These findings provide novel insights into the combined effects of human mobility and the organization of geographical networks on spreading processes, with important implications for both epidemiological research and health policy. human motion, including ultraslow diffusion [3,4] and strikingly high predictability [6]. It is these abnormal properties that actually shape the difference between the natural human movements and the so-called Lévy flights [7,16].Several studies have been conducted so far to address the underlying mechanisms of the discovered abnormal properties of human spatial behavior. For the urban and intercity travels, Song et al. investigated the impact of exploration and preferential return on mobility patterns [8]. For the long-range travels, Han et al. [9] demonstrated that hierarchical geographical organization of traffic systems plays a crucial role in the emergence of scaling in human trajectories.An important, but less studied issue in this context, is the impact of the abnormal features of human motion on the dynamical evolution of spreading processes. For example, based on the mobile phone user data published in Ref.[3], Wang et al. analyzed the spreading of a mobile-phone-virus outbreak [10]. Ni and Weng [11] investigated the effects of heterogeneous spatial properties in metapopulation networks, and more recently, Belik et al. [12,13] reported on the dynamics of a spreading process under bidirectional mobility, that was further investigated by Balcan and Vespignani [14]. All of these studies converge on the finding that spat...

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Effects of Internal Border Control on Spread of Pandemic Influenza

Abstract: Population size, travel rates, and residence of travelers can aid in determining travel restrictions as a control policy.

Cited by 13 publications

References 13 publications

A Review of Simulation Modelling Approaches Used for the Spread of Zoonotic Influenza Viruses in Animal and Human Populations

A Review of Simulation Modelling Approaches Used for the Spread of Zoonotic Influenza Viruses in Animal and Human Populations

A Perspective on Multiple Waves of Influenza Pandemics

Epidemic spreading on hierarchical geographical networks with mobile agents

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