Estimated epidemiologic parameters and morbidity associated with pandemic H1N1 influenza

Tuite, Ashleigh R.; Greer, Amy L.; Whelan, Michael; Winter, Anne-Luise; Lee, Brenda; Yan, Ping; Wu, Jianhong; Moghadas, Seyed M.; Buckeridge, David L.; Pourbohloul, Babak; Fisman, David N.

doi:10.1503/cmaj.091807

Cited by 220 publications

(269 citation statements)

References 24 publications

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“…[1][2][3]6 Influenza can be safely prevented with vaccination. 5 When there is a good match between circulating and vaccine strains, influenza immunization is the most effective preventive measure for reducing influenza-related morbidity and mortality.…”

Section: Discussionmentioning

confidence: 99%

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Pandemic influenza vaccination coverage in children with type 1 diabetes: Analysis from seven Italian centers

et al. 2011

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Section: Discussionmentioning

confidence: 99%

“…2 The case-fatality rate has been estimated at around 0.3%. 3 Vaccination against influenza is considered the most effective intervention against this infection.…”

Section: Introductionmentioning

confidence: 99%

Pandemic influenza vaccination coverage in children with type 1 diabetes: Analysis from seven Italian centers

et al. 2011

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“…A fraction of diagnosed cases will be hospitalized and therefore effectively removed from the simulation scenarios without contributing to infection spread. We assumed that a fraction of infectious individuals who are not hospitalized will self-isolate for an average infectious period T I , sampled from the log-normal distribution 19 . We also assumed that recovery upon infection will confer immunity against re-infection in the same epidemic episode.…”

Section: Methodsmentioning

confidence: 99%

The Impact of Demographic Variables on Disease Spread: Influenza in Remote Communities

Laskowski

Mostaço-Guidolin

Greer

et al. 2011

Sci Rep

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The role of demographic variables in disease spread in remote and isolated communities is poorly understood. We developed an agent-based model of a small indigenous community to qualitatively study the impact of pre-existing immunity in both young and elderly populations. We observed that in crowded living conditions, the age distribution of the population is a critical factor influencing epidemic spread. As the average age of the population increases, the effect of the pre-existing immunity in older individuals becomes more pronounced in decreasing disease incidence, even when pre-existing immunity levels in young individuals are low. However, in a non-crowded setting with relatively low average persons-per-household, the pre-existing immunity levels of young individuals remains a determining factor, regardless of the age distribution of the population. We suggest that for optimizing public health policies, social and demographic complexities of the remote and vulnerable communities should be carefully evaluated in modeling intervention strategies. In the event of an emerging disease, public health officials are tasked with identifying the geographic spread and time course of the outbreak, and identifying the most effective utilization of available health interventions and resources to mitigate disease outcomes. In addition to the natural history and biology of the disease, the demographic characteristics of the population at risk play an important role in determining the pattern of epidemic spread and identifying the type and intensity of public health intervention measures required for disease control 1,2,3 . Furthermore, the differential prevalence of predisposing health conditions and other types of health disparities increases uncertainty about how a novel disease would affect different populations with distinctly different mobility patterns, social interactions, and health characteristics 4 . The importance of these factors in influencing disease burden was highlighted during outbreaks of the 2009 H1N1 pandemic in several Canadian population settings, including First Nation reserves in northern Manitoba, remote and isolated communities in Nunavut, and Aboriginal communities on Vancouver Island 5,6,7 . Understanding the interplay between demographic, health, infection and control parameters requires the development of a modeling framework that can identify individuals with their assigned information, and describe disease spread in the population in silico (i.e., via computer simulations). Agent-based models, which specifically encapsulate individual-level characteristics, behaviors and population profiles, provide such a framework that is capable of reproducing observed scenarios in epidemics and exploring plausible contingency plans and control measures for curtailing an emerging disease 8,9 . Agent based modeling typically uses a bottom-up approach in which complex phenomena emerge from interactions between autonomous entities (i.e., agents) that perceive, make decisions, and act within an environment.He...

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“…Many complex dynamical processes are supported by networks of interconnections between a large number of individual elements (e.g., epidemics [1][2][3][4], cancer spread [5], electrical power distribution [6,7], etc.). Interventions that seek to degrade [8][9][10][11][12][13] or protect [13][14][15][16] network connectivity are thus of great interest.…”

mentioning

confidence: 99%

Impact of imperfect information on network attack

et al. 2015

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This paper explores the effectiveness of network attack when the attacker has imperfect information about the network. For Erdős-Rényi networks, we observe that dynamical importance and betweenness centrality-based attacks are surprisingly robust to the presence of a moderate amount of imperfect information and are more effective compared with simpler degree-based attacks even at moderate levels of network information error. In contrast, for scale-free networks the effectiveness of attack is much less degraded by a moderate level of information error. Furthermore, in the Erdős-Rényi case the effectiveness of network attack is much more degraded by missing links as compared with the same number of false links. [6,7], etc.). Interventions that seek to degrade [8][9][10][11][12][13] or protect [13][14][15][16] network connectivity are thus of great interest. In particular, strategies for network attack by node or link removal have been intensively studied. Key issues have been the dependence of the attack effectiveness upon network topology and the strategy for selecting nodes or links for removal. We note, however, that, while such previous studies have predominantly presumed the attacker to have perfect knowledge of the network to be attacked, this is very often not the case. Specifically, networks inferred from measurements typically have false links and miss true links. One might suppose that these errors could very much lower the effectiveness of attack strategies. The purpose of this paper is to address this important issue for the case of node removal attacks of undirected networks (directed networks are treated in the online supplementary material [17]).One example of a network attack problem is an attempt to stop the spread of a disease with a limited number of vaccinations: the people who receive the vaccinations are chosen on the basis of their position in the social network [8][9][10][11][12][13]. Another example is that of deriving gene therapies for cancer. Here the goal is to select those genes whose disabling would most inhibit cancer cell survival and proliferation [18][5]. Yet another example is the study of the resilience of the Internet to intentional attack [8,12]. The typical attack strategy is to calculate some centrality measure of each node, and to then attack (disable, vaccinate, or remove) those nodes with the highest values of this measure. However, an attacker with imperfect network information will determine values of these centrality measures with some error, and using these would be expected to degrade the effectiveness of his attack. Imperfect network information is ubiquitous in applications and can arise in various ways. Examples of link errors can be found in online social networks, where a friendship may be indicated despite the two subjects having never personally met, or inversely, if no online friendship exists between two faceto-face friends. In the previously cited example of cancer gene therapy, genes are selected for disabling based upon an estimated gene interaction net...

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Estimated epidemiologic parameters and morbidity associated with pandemic H1N1 influenza

Cited by 220 publications

References 24 publications

Pandemic influenza vaccination coverage in children with type 1 diabetes: Analysis from seven Italian centers

Pandemic influenza vaccination coverage in children with type 1 diabetes: Analysis from seven Italian centers

The Impact of Demographic Variables on Disease Spread: Influenza in Remote Communities

Impact of imperfect information on network attack

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