Extension of the SIR Model Using a Stratified Population and a Hospital Compartment
Background and Aim. The classical Susceptible-Infected-Removed (SIR) model is a very simple model for the spread of an infection. The aim of this work was to extend this model to include additional compartments for hospitalized and dead patients and to apply the model to a stratified population. Mat...
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Published in | Applied medical informatics Vol. 43; no. Suppl. S1; p. 25 |
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Main Author | |
Format | Journal Article |
Language | English |
Published |
Cluj-Napoca
SRIMA Publishing House
01.09.2021
Iuliu Hatieganu University of Medicine and Pharmacy, Cluj-Napoca |
Subjects | |
Online Access | Get full text |
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Abstract | Background and Aim. The classical Susceptible-Infected-Removed (SIR) model is a very simple model for the spread of an infection. The aim of this work was to extend this model to include additional compartments for hospitalized and dead patients and to apply the model to a stratified population. Materials and Methods. The various mathematical models were implemented in R using the package deSolve and visualized using ggplot2 and shiny. Various model parameters were chosen artificially to explore the effects on the spread of an epidemic, but were not calibrated on real data. Output variables included daily hospitalization rates and death rates, as well as total patients in the hospital during each day. Results. The SIR model can be easily extended to include separate compartments for Recovered and Deceased patients. The population was stratified into young adults and old people; a proportion of 20% old persons was used for most analysis. Old people were modeled using higher rates of hospitalization and death. The daily rates of hospitalization and death were explored interactively using various model parameters. Conclusion: The extended SIR model was easy to use and allowed interactive exploration of various parameters. Such a model could be used to monitor a real epidemic and to forecast health-care resources needed during the epidemic. |
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AbstractList | Background and Aim. The classical Susceptible-Infected-Removed (SIR) model is a very simple model for the spread of an infection. The aim of this work was to extend this model to include additional compartments for hospitalized and dead patients and to apply the model to a stratified population. Materials and Methods. The various mathematical models were implemented in R using the package deSolve and visualized using ggplot2 and shiny. Various model parameters were chosen artificially to explore the effects on the spread of an epidemic, but were not calibrated on real data. Output variables included daily hospitalization rates and death rates, as well as total patients in the hospital during each day. Results. The SIR model can be easily extended to include separate compartments for Recovered and Deceased patients. The population was stratified into young adults and old people; a proportion of 20% old persons was used for most analysis. Old people were modeled using higher rates of hospitalization and death. The daily rates of hospitalization and death were explored interactively using various model parameters. Conclusion: The extended SIR model was easy to use and allowed interactive exploration of various parameters. Such a model could be used to monitor a real epidemic and to forecast health-care resources needed during the epidemic. |
Author | Mada, Leonard |
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ContentType | Journal Article |
Copyright | 2021. This work is published under https://creativecommons.org/licenses/by-nc/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
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Snippet | Background and Aim. The classical Susceptible-Infected-Removed (SIR) model is a very simple model for the spread of an infection. The aim of this work was to... Background and Aim: The classical Susceptible-Infected-Removed (SIR) model is a very simple model for the spread of an infection. The aim of this work was to... |
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SubjectTerms | age compartmental models Data analysis Death Disease transmission Epidemics epidemiology Health informatics Health risks Hospitalization infection rate Mathematical models Older people Parameters Real variables Young adults |
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Title | Extension of the SIR Model Using a Stratified Population and a Hospital Compartment |
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