13,769 research outputs found
Targeting vaccination against novel infections : risk, age and spatial structure for pandemic influenza in Great Britain
The emergence of a novel strain of H1N1 influenza virus in Mexico in 2009, and its subsequent worldwide spread, has focused attention to the question of optimal deployment of mass vaccination campaigns. Here, we use three relatively simple models to address three issues of primary concern in the targeting of any vaccine. The advantages of such simple models are that the underlying assumptions and effects of individual parameters are relatively clear, and the impact of uncertainty in the parametrization can be readily assessed in the early stages of an outbreak. In particular, we examine whether targeting risk-groups, age-groups or spatial regions could be optimal in terms of reducing the predicted number of cases or severe effects; and how these targeted strategies vary as the epidemic progresses. We examine the conditions under which it is optimal to initially target vaccination towards those individuals within the population who are most at risk of severe effects of infection. Using age-structured mixing matrices, we show that targeting vaccination towards the more epidemiologically important age groups (5-14 year olds and then 15-24 year olds) leads to the greatest reduction in the epidemic growth and hence reduces the total number of cases. Finally, we consider how spatially targeting the vaccine towards regions of country worst affected could provide an advantage. We discuss how all three of these priorities change as both the speed at which vaccination can be deployed and the start of the vaccination programme is varied
A Comparative Analysis of Influenza Vaccination Programs
The threat of avian influenza and the 2004-2005 influenza vaccine supply
shortage in the United States has sparked a debate about optimal vaccination
strategies to reduce the burden of morbidity and mortality caused by the
influenza virus. We present a comparative analysis of two classes of suggested
vaccination strategies: mortality-based strategies that target high risk
populations and morbidity-based that target high prevalence populations.
Applying the methods of contact network epidemiology to a model of disease
transmission in a large urban population, we evaluate the efficacy of these
strategies across a wide range of viral transmission rates and for two
different age-specific mortality distributions. We find that the optimal
strategy depends critically on the viral transmission level (reproductive rate)
of the virus: morbidity-based strategies outperform mortality-based strategies
for moderately transmissible strains, while the reverse is true for highly
transmissible strains. These results hold for a range of mortality rates
reported for prior influenza epidemics and pandemics. Furthermore, we show that
vaccination delays and multiple introductions of disease into the community
have a more detrimental impact on morbidity-based strategies than
mortality-based strategies. If public health officials have reasonable
estimates of the viral transmission rate and the frequency of new introductions
into the community prior to an outbreak, then these methods can guide the
design of optimal vaccination priorities. When such information is unreliable
or not available, as is often the case, this study recommends mortality-based
vaccination priorities
Theoretical Framework and Empirical Modeling for Time Required to Vaccinate a Population in an Epidemic
The paper describes a method to understand time required to vaccinate against
viruses in total as well as subpopulations. As a demonstration, a model based
estimate for time required to vaccinate H1N1 in India, given its administrative
difficulties is provided. We have proved novel theorems for the time functions
defined in the paper. Such results are useful in planning for future epidemics.
The number of days required to vaccinate entire high risk population in three
subpopulations (villages, tehsils and towns) are noted to be 84, 89 and 88
respectively. There exists state wise disparities in the health infrastructure
and capacities to deliver vaccines and hence national estimates need to be
re-evaluated based on individual performances in the states.Comment: 14 pages, 1 Table, 5 Figures (A preliminary draft
Mathematical models for vaccination, waning immunity and immune system boosting: a general framework
When the body gets infected by a pathogen or receives a vaccine dose, the
immune system develops pathogen-specific immunity. Induced immunity decays in
time and years after recovery/vaccination the host might become susceptible
again. Exposure to the pathogen in the environment boosts the immune system
thus prolonging the duration of the protection. Such an interplay of within
host and population level dynamics poses significant challenges in rigorous
mathematical modeling of immuno-epidemiology. The aim of this paper is twofold.
First, we provide an overview of existing models for waning of
disease/vaccine-induced immunity and immune system boosting. Then a new
modeling approach is proposed for SIRVS dynamics, monitoring the immune status
of individuals and including both waning immunity and immune system boosting.
We show that some previous models can be considered as special cases or
approximations of our framework.Comment: 18 pages, 1 figure keywords: Immuno-epidemiology, Waning immunity,
Immune status, Boosting, Physiological structure, Reinfection, Delay
equations, Vaccination. arXiv admin note: substantial text overlap with
arXiv:1411.319
Age-structured models and optimal control in mathematical equidemiology: a survey
In this chapter we shall discuss the use of both optimal control theory and age-structured epidemic models in mathematical epidemiology. We use a very broad definition of optimal control, for example mathematical models for control by vaccination, as well as applications of optimal control theory. This is a wide area and we have had to be selective. In terms of applications a lot of the models which we present are applicable to the spread of common childhood diseases as that is an area in which age-structured models have been shown to fit data well and are most commonly applied in practice. This is because vaccination programs are often age-dependent targeting children of a given age and so they need age-structured models. The first section of this chapter discusses age-structured epidemic models including the question of optimal vaccination in them. 2 Then we move on to the optimal control in “stage-structured” (rather than age-structured) epidemic models, in which the individuals are grouped into susceptible, infected, and so on, depending on their relation to the epidemic. This gives a survey of how the ideas of optimal control theory, in particular the Maximum Principle and dynamic programming have been applied in the past to determine optimal control strategies for an epidemic, for example by immunization or removal of infected individuals. We finish this section with a few papers which apply optimal control theory to drug epidemics. We next survey some articles which give applications of optimal control to age-structured epidemic models. Much of this work concerns the existence and structure of optimal age-dependent vaccination strategies for common childhood diseases but we cover some other applications too. This is followed by a short section on spatial models used to determine optimal epidemic control policies. A brief summary and discussion conclude
Evaluation of Targeted Influenza Vaccination Strategies via Population Modeling
Background
Because they can generate comparable predictions, mathematical models are ideal tools for evaluating alternative drug or vaccine allocation strategies. To remain credible, however, results must be consistent. Authors of a recent assessment of possible influenza vaccination strategies conclude that older children, adolescents, and young adults are the optimal targets, no matter the objective, and argue for vaccinating them. Authors of two earlier studies concluded, respectively, that optimal targets depend on objectives and cautioned against changing policy. Which should we believe? Methods and Findings
In matrices whose elements are contacts between persons by age, the main diagonal always predominates, reflecting contacts between contemporaries. Indirect effects (e.g., impacts of vaccinating one group on morbidity or mortality in others) result from off-diagonal elements. Mixing matrices based on periods in proximity with others have greater sub- and super-diagonals, reflecting contacts between parents and children, and other off-diagonal elements (reflecting, e.g., age-independent contacts among co-workers), than those based on face-to-face conversations. To assess the impact of targeted vaccination, we used a time-usage study\u27s mixing matrix and allowed vaccine efficacy to vary with age. And we derived mortality rates either by dividing observed deaths attributed to pneumonia and influenza by average annual cases from a demographically-realistic SEIRS model or by multiplying those rates by ratios of (versus adding to them differences between) pandemic and pre-pandemic mortalities. Conclusions
In our simulations, vaccinating older children, adolescents, and young adults averts the most cases, but vaccinating either younger children and older adults or young adults averts the most deaths, depending on the age distribution of mortality. These results are consistent with those of the earlier studies
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