The influenza pandemic preparedness planning tool InfluSim

Abstract

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.

Global asymptotic properties of an SEIRS model with multiple infectious stages

“…The average incubation period of influenza is 1.9 days, so that 1 σ E = 1.9 days [5,6,14,[28][29][30]. Similarly, the average infectious period is 5 days [29,30].…”

Virtual epidemic in a virtual city: simulating the spread of influenza in a US metropolitan area

“…Compartment models treat the population as homogenous blocks of people who share the same characteristics and decision making. 3,[47][48][49][50] The output from compartment models tends to be gross population disease incidence and prevalence, without details on geospatial location of diseased individuals, disease behavior in specific sociodemographic groups, and health care seeking behavior. Agent-based models consist of more autonomous virtual individuals that make choices and take action just as individuals would.…”

Towards efficient large scale epidemiological simulations in EpiGraph

“…Simulators based on the deterministic approach [5,6] use a set of differential equations to model the process by which individuals pass between the different stages of the infection. These models assume that the population is homogeneously mixed and the social contacts are highly structured-an unrealistic assumption.…”

“…InFlusim [16] implements an extension of the SEIR compartmental model which includes hospitalization and home confinement. InFlusim is a deterministic model that does not take into account individual characteristics or spatial distribution of the population.…”