BackgroundPlanning public health responses against pandemic influenza relies on predictive models by which the impact of different intervention strategies can be evaluated. Research has to date rather focused on producing predictions for certain localities or under specific conditions, than on designing a publicly available planning tool which can be applied by public health administrations. Here, we provide such a tool which is reproducible by an explicitly formulated structure and designed to operate with an optimal combination of the competing requirements of precision, realism and generality.ResultsInfluSim is a deterministic compartment model based on a system of over 1,000 differential equations which extend the classic SEIR model by clinical and demographic parameters relevant for pandemic preparedness planning. It allows for producing time courses and cumulative numbers of influenza cases, outpatient visits, applied antiviral treatment doses, hospitalizations, deaths and work days lost due to sickness, all of which may be associated with economic aspects. The software is programmed in Java, operates platform independent and can be executed on regular desktop computers.ConclusionInfluSim is an online available software which efficiently assists public health planners in designing optimal interventions against pandemic influenza. It can reproduce the infection dynamics of pandemic influenza like complex computer simulations while offering at the same time reproducibility, higher computational performance and better operability.
BackgroundAfter vaccination, vaccinees acquire some protection against infection and/or disease. Vaccination, therefore, reduces the number of infections in the population. Due to this herd protection, not everybody needs to be vaccinated to prevent infections from spreading.MethodsWe quantify direct and indirect effects of influenza vaccination examining the standard Susceptible-Infected-Recovered (SIR) and Susceptible-Infected-Recovered-Susceptible (SIRS) model as well as simulation results of a sophisticated simulation tool which allows for seasonal transmission of four influenza strains in a population with realistic demography and age-dependent contact patterns.ResultsAs shown analytically for the simple SIR and SIRS transmission models, indirect vaccination effects are bigger than direct ones if the effective reproduction number of disease transmission is close to the critical value of 1. Simulation results for 20–60% vaccination with live influenza vaccine of 2–17 year old children in Germany, averaged over 10 years (2017–26), confirm this result: four to seven times as many influenza cases are prevented among non-vaccinated individuals as among vaccinees. For complications like death due to influenza which occur much more frequently in the unvaccinated elderly than in the vaccination target group of children, indirect benefits can surpass direct ones by a factor of 20 or even more than 30.ConclusionsThe true effect of vaccination can be much bigger than what would be expected by only looking at vaccination coverage and vaccine efficacy.Electronic supplementary materialThe online version of this article (doi:10.1186/s12879-017-2399-4) contains supplementary material, which is available to authorized users.
BackgroundInfluenza vaccines contain Influenza A and B antigens and are adjusted annually to match the characteristics of circulating viruses. In Germany, Influenza B viruses belonged to the B/Yamagata lineage, but since 2001, the antigenically distinct B/Victoria lineage has been co-circulating. Trivalent influenza vaccines (TIV) contain antigens of the two A subtypes A(H3N2) and A(H1N1), yet of only one B lineage, resulting in frequent vaccine mismatches. Since 2012, the WHO has been recommending vaccine strains from both B lineages, paving the way for quadrivalent influenza vaccines (QIV).MethodsUsing an individual-based simulation tool, we simulate the concomitant transmission of four influenza strains, and compare the effects of TIV and QIV on the infection incidence. Individuals are connected in a dynamically evolving age-dependent contact network based on the POLYMOD matrix; their age-distribution reproduces German demographic data and predictions. The model considers maternal protection, boosting of existing immunity, loss of immunity, and cross-immunizing events between the B lineages. Calibration to the observed annual infection incidence of 10.6% among young adults yielded a basic reproduction number of 1.575. Vaccinations are performed annually in October and November, whereby coverage depends on the vaccinees’ age, their risk status and previous vaccination status. New drift variants are introduced at random time points, leading to a sudden loss of protective immunity for part of the population and occasionally to reduced vaccine efficacy. Simulations run for 50 years, the first 30 of which are used for initialization. During the final 20 years, individuals receive TIV or QIV, using a mirrored simulation approach.ResultsUsing QIV, the mean annual infection incidence can be reduced from 8,943,000 to 8,548,000, i.e. by 395,000 infections, preventing 11.2% of all Influenza B infections which still occur with TIV (95% CI: 10.7-11.8%). Using a lower B lineage cross protection than the baseline 60%, the number of Influenza B infections increases and the number additionally prevented by QIV can be 5.5 times as high.ConclusionsVaccination with TIV substantially reduces the Influenza incidence compared to no vaccination. Depending on the assumed degree of B lineage cross protection, QIV further reduces Influenza B incidence by 11-33%.
BackgroundSeasonal influenza infection is primarily caused by circulation of two influenza A strain subtypes and strains from two B lineages that vary each year. Trivalent influenza vaccine (TIV) contains only one of the two B-lineage strains, resulting in mismatches between vaccine strains and the predominant circulating B lineage. Quadrivalent influenza vaccine (QIV) includes both B-lineage strains. The objective was to estimate the cost-utility of introducing QIV to replace TIV in Germany.MethodsAn individual-based dynamic transmission model (4Flu) using German data was used to provide realistic estimates of the impact of TIV and QIV on age-specific influenza infections. Cases were linked to health and economic outcomes to calculate the cost-utility of QIV versus TIV, from both a societal and payer perspective. Costs and effects were discounted at 3.0 and 1.5 % respectively, with 2014 as the base year. Univariate and probabilistic sensitivity analyses were conducted.ResultsUsing QIV instead of TIV resulted in additional quality-adjusted life-years (QALYs) and cost savings from the societal perspective (i.e. it represents the dominant strategy) and an incremental cost-utility ratio (ICUR) of €14,461 per QALY from a healthcare payer perspective. In all univariate analyses, QIV remained cost-effective (ICUR <€50,000). In probabilistic sensitivity analyses, QIV was cost-effective in >98 and >99 % of the simulations from the societal and payer perspective, respectively.ConclusionThis analysis suggests that QIV in Germany would provide additional health gains while being cost-saving to society or costing €14,461 per QALY gained from the healthcare payer perspective, compared with TIV.Electronic supplementary materialThe online version of this article (doi:10.1007/s40273-016-0443-7) contains supplementary material, which is available to authorized users.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.