Multi-generational SIR modeling: Determination of parameters, epidemiological forecasting and age-dependent vaccination policies
We use an age-dependent SIR system of equations to model the evolution of the COVID-19. Parameters that measure the amount of interaction in different locations (home, work, school, other) are approximated from in-sample data using a random optimization scheme, and indicate changes in social distanc...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
KeAi Communications Co., Ltd.
2021-01-01
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| Series: | Infectious Disease Modelling |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2468042721000373 |
| _version_ | 1797204410129973248 |
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| author | Eduardo Lima Campos Rubens Penha Cysne Alexandre L. Madureira Gélcio L.Q. Mendes |
| author_facet | Eduardo Lima Campos Rubens Penha Cysne Alexandre L. Madureira Gélcio L.Q. Mendes |
| author_sort | Eduardo Lima Campos |
| collection | DOAJ |
| description | We use an age-dependent SIR system of equations to model the evolution of the COVID-19. Parameters that measure the amount of interaction in different locations (home, work, school, other) are approximated from in-sample data using a random optimization scheme, and indicate changes in social distancing along the course of the pandemic. That allows the estimation of the time evolution of classical and age-dependent reproduction numbers. With those parameters we predict the disease dynamics, and compare our results with out-of-sample data from the City of Rio de Janeiro. Finally, we provide a numerical investigation regarding age-based vaccination policies, shedding some light on whether is preferable to vaccinate those at most risk (the elderly) or those who spread the disease the most (the youngest). There is no clear upshot, as the results depend on the age of those immunized, contagious parameters, vaccination schedules and efficiency. |
| first_indexed | 2024-04-24T08:34:47Z |
| format | Article |
| id | doaj.art-aa7b0f56cdf546159f6cf11006d44cb7 |
| institution | Directory Open Access Journal |
| issn | 2468-0427 |
| language | English |
| last_indexed | 2024-04-24T08:34:47Z |
| publishDate | 2021-01-01 |
| publisher | KeAi Communications Co., Ltd. |
| record_format | Article |
| series | Infectious Disease Modelling |
| spelling | doaj.art-aa7b0f56cdf546159f6cf11006d44cb72024-04-16T18:02:09ZengKeAi Communications Co., Ltd.Infectious Disease Modelling2468-04272021-01-016751765Multi-generational SIR modeling: Determination of parameters, epidemiological forecasting and age-dependent vaccination policiesEduardo Lima Campos0Rubens Penha Cysne1Alexandre L. Madureira2Gélcio L.Q. Mendes3EPGE Brazilian School of Economics and Finance (FGV EPGE), Rio de Janeiro, RJ, Brazil; ENCE - Escola Nacional de Ciências Estatísticas (ENCE/IBGE), Rio de Janeiro, RJ, BrazilEPGE Brazilian School of Economics and Finance (FGV EPGE), Rio de Janeiro, RJ, BrazilLaboratório Nacional de Computação Científica, Petrópolis, RJ, Brazil; EPGE Brazilian School of Economics and Finance (FGV EPGE), Rio de Janeiro, RJ, Brazil; Corresponding author. Laboratório Nacional de Computação Científica, Petrópolis, RJ, Brazil.INCA - Brazilian National Cancer Institute, Coordination of Assistance, Rio de Janeiro, RJ, BrazilWe use an age-dependent SIR system of equations to model the evolution of the COVID-19. Parameters that measure the amount of interaction in different locations (home, work, school, other) are approximated from in-sample data using a random optimization scheme, and indicate changes in social distancing along the course of the pandemic. That allows the estimation of the time evolution of classical and age-dependent reproduction numbers. With those parameters we predict the disease dynamics, and compare our results with out-of-sample data from the City of Rio de Janeiro. Finally, we provide a numerical investigation regarding age-based vaccination policies, shedding some light on whether is preferable to vaccinate those at most risk (the elderly) or those who spread the disease the most (the youngest). There is no clear upshot, as the results depend on the age of those immunized, contagious parameters, vaccination schedules and efficiency.http://www.sciencedirect.com/science/article/pii/S2468042721000373COVID-19EpidemiologyVaccinationCompartmental modelingSIR |
| spellingShingle | Eduardo Lima Campos Rubens Penha Cysne Alexandre L. Madureira Gélcio L.Q. Mendes Multi-generational SIR modeling: Determination of parameters, epidemiological forecasting and age-dependent vaccination policies Infectious Disease Modelling COVID-19 Epidemiology Vaccination Compartmental modeling SIR |
| title | Multi-generational SIR modeling: Determination of parameters, epidemiological forecasting and age-dependent vaccination policies |
| title_full | Multi-generational SIR modeling: Determination of parameters, epidemiological forecasting and age-dependent vaccination policies |
| title_fullStr | Multi-generational SIR modeling: Determination of parameters, epidemiological forecasting and age-dependent vaccination policies |
| title_full_unstemmed | Multi-generational SIR modeling: Determination of parameters, epidemiological forecasting and age-dependent vaccination policies |
| title_short | Multi-generational SIR modeling: Determination of parameters, epidemiological forecasting and age-dependent vaccination policies |
| title_sort | multi generational sir modeling determination of parameters epidemiological forecasting and age dependent vaccination policies |
| topic | COVID-19 Epidemiology Vaccination Compartmental modeling SIR |
| url | http://www.sciencedirect.com/science/article/pii/S2468042721000373 |
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