Difference between revisions of "SEIVAgent IBM Epidemic Simulator"
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===Using SEIVAgent=== | ===Using SEIVAgent=== | ||
SEIVAgent | SEIVAgent is a stochastic model simulating an epidemic spreading over a population of individuals over some time period. Individuals states include '''S''' (susceptible), '''E''' (exposed), '''I''' (infectious), '''V''' (vaccinated, recovered) and '''D''' (dead). A set of 18 parameters govern the epidemic over a course of time units (days, weeks, etc.) | ||
The program models both the initial infection and emerging variants. Each individual is currently in some state with respect to each of the variants. Variants are numbered 0 to 2<sup><var>n</var></sup> - 1 for some fixed <var>n</var>. | The program models both the initial infection and emerging variants. Each individual is currently in some state with respect to each of the variants. Variants are numbered 0 to 2<sup><var>n</var></sup> - 1 for some fixed <var>n</var>. | ||
SEIVAgent is initialed with a given population size and a single individual in state '''I''' with respect to the initial strain (variant 0). All other individuals are in state '''S'''. | SEIVAgent is initialed with a given population size and a single individual in state '''I''' with respect to the initial strain (variant 0). All other individuals are in state '''S'''. | ||
The program executes as a series of time steps. At each time step new susceptible and recovered individuals may be exposed to some strain. Each individual can only be exposed and infected with respect to one single strain at a time. Exposed individuals become infectious and infectious individuals either recover or die. | |||
Governing the progress of individuals through the epidemic are probabilities that are computed using the parameter set. Full details of the algorithm are provided in the paper. Along with these parameters, the user may set up a vaccination protocol, which is governed by parameters contained on the vaccination page (see below). | |||
The program implements some novel features designed to provide expressiveness and promote experimentations. These include an API available for both on-board and remote access. The second feature is a set of runtime alterable modules (RAMs) that manage changes to the basic algorithm in a safe environment. | |||
==The Dashboard== | ==The Dashboard== | ||
[[File:dashboard.png|800px]] | [[File:dashboard.png|800px]] | ||
Revision as of 18:19, 17 June 2021
Overview
The SEIVAgent Simulator is software to accompany Getz, et. al.,Adaptive vaccination may be needed to eliminate COVID-19: Results from a runtime-alterable strain-drift and waning-immunity model, https://doi.org/10.1101/2021.06.07.21258504. Complete documentation for the application is contained in the latter. This User Guide provides general instructions for using the program, together with some example runs from the paper.
Using SEIVAgent
SEIVAgent is a stochastic model simulating an epidemic spreading over a population of individuals over some time period. Individuals states include S (susceptible), E (exposed), I (infectious), V (vaccinated, recovered) and D (dead). A set of 18 parameters govern the epidemic over a course of time units (days, weeks, etc.)
The program models both the initial infection and emerging variants. Each individual is currently in some state with respect to each of the variants. Variants are numbered 0 to 2n - 1 for some fixed n.
SEIVAgent is initialed with a given population size and a single individual in state I with respect to the initial strain (variant 0). All other individuals are in state S.
The program executes as a series of time steps. At each time step new susceptible and recovered individuals may be exposed to some strain. Each individual can only be exposed and infected with respect to one single strain at a time. Exposed individuals become infectious and infectious individuals either recover or die.
Governing the progress of individuals through the epidemic are probabilities that are computed using the parameter set. Full details of the algorithm are provided in the paper. Along with these parameters, the user may set up a vaccination protocol, which is governed by parameters contained on the vaccination page (see below).
The program implements some novel features designed to provide expressiveness and promote experimentations. These include an API available for both on-board and remote access. The second feature is a set of runtime alterable modules (RAMs) that manage changes to the basic algorithm in a safe environment.
The Dashboard
