Estimating the effective reproduction number for heterogeneous models using incidence data
The effective reproduction number, [Formula: see text], plays a key role in the study of infectious diseases, indicating the current average number of new infections caused by an infected individual in an epidemic process. Estimation methods for the time evolution of [Formula: see text], using incid...
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| Format: | Article |
| Language: | English |
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The Royal Society
2022-09-01
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| Series: | Royal Society Open Science |
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| Online Access: | https://royalsocietypublishing.org/doi/10.1098/rsos.220005 |
| _version_ | 1797840284597354496 |
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| author | D. C. P. Jorge J. F. Oliveira J. G. V. Miranda R. F. S. Andrade S. T. R. Pinho |
| author_facet | D. C. P. Jorge J. F. Oliveira J. G. V. Miranda R. F. S. Andrade S. T. R. Pinho |
| author_sort | D. C. P. Jorge |
| collection | DOAJ |
| description | The effective reproduction number, [Formula: see text], plays a key role in the study of infectious diseases, indicating the current average number of new infections caused by an infected individual in an epidemic process. Estimation methods for the time evolution of [Formula: see text], using incidence data, rely on the generation interval distribution, g(τ), which is usually obtained from empirical data or theoretical studies using simple epidemic models. However, for systems that present heterogeneity, either on the host population or in the expression of the disease, there is a lack of data and of a suitable general methodology to obtain g(τ). In this work, we use mathematical models to bridge this gap. We present a general methodology for obtaining explicit expressions of the reproduction numbers and the generation interval distributions, within and between model sub-compartments provided by an arbitrary compartmental model. Additionally, we present the appropriate expressions to evaluate those reproduction numbers using incidence data. To highlight the relevance of such methodology, we apply it to the spread of COVID-19 in municipalities of the state of Rio de Janeiro, Brazil. Using two meta-population models, we estimate the reproduction numbers and the contributions of each municipality in the generation of cases in all others. |
| first_indexed | 2024-04-09T16:12:28Z |
| format | Article |
| id | doaj.art-869cb3e75a3347f89c53863cf7addfcf |
| institution | Directory Open Access Journal |
| issn | 2054-5703 |
| language | English |
| last_indexed | 2024-04-09T16:12:28Z |
| publishDate | 2022-09-01 |
| publisher | The Royal Society |
| record_format | Article |
| series | Royal Society Open Science |
| spelling | doaj.art-869cb3e75a3347f89c53863cf7addfcf2023-04-24T09:15:18ZengThe Royal SocietyRoyal Society Open Science2054-57032022-09-019910.1098/rsos.220005Estimating the effective reproduction number for heterogeneous models using incidence dataD. C. P. Jorge0J. F. Oliveira1J. G. V. Miranda2R. F. S. Andrade3S. T. R. Pinho4Instituto de Física Teórica, Universidade Estadual Paulista—UNESP, R. Dr. Teobaldo Ferraz 271, São Paulo 01140-070, BrazilCenter of Data and Knowledge Integration for Health (CIDACS), Instituto Gonçalo Moniz, Fundação Oswaldo Cruz, Salvador, Bahia, BrazilInstituto de Física, Universidade Federal da Bahia, Salvador, Bahia, BrazilCenter of Data and Knowledge Integration for Health (CIDACS), Instituto Gonçalo Moniz, Fundação Oswaldo Cruz, Salvador, Bahia, BrazilInstituto de Física, Universidade Federal da Bahia, Salvador, Bahia, BrazilThe effective reproduction number, [Formula: see text], plays a key role in the study of infectious diseases, indicating the current average number of new infections caused by an infected individual in an epidemic process. Estimation methods for the time evolution of [Formula: see text], using incidence data, rely on the generation interval distribution, g(τ), which is usually obtained from empirical data or theoretical studies using simple epidemic models. However, for systems that present heterogeneity, either on the host population or in the expression of the disease, there is a lack of data and of a suitable general methodology to obtain g(τ). In this work, we use mathematical models to bridge this gap. We present a general methodology for obtaining explicit expressions of the reproduction numbers and the generation interval distributions, within and between model sub-compartments provided by an arbitrary compartmental model. Additionally, we present the appropriate expressions to evaluate those reproduction numbers using incidence data. To highlight the relevance of such methodology, we apply it to the spread of COVID-19 in municipalities of the state of Rio de Janeiro, Brazil. Using two meta-population models, we estimate the reproduction numbers and the contributions of each municipality in the generation of cases in all others.https://royalsocietypublishing.org/doi/10.1098/rsos.220005effective reproduction numbermathematical modelsmeta-population modelsCOVID-19 |
| spellingShingle | D. C. P. Jorge J. F. Oliveira J. G. V. Miranda R. F. S. Andrade S. T. R. Pinho Estimating the effective reproduction number for heterogeneous models using incidence data Royal Society Open Science effective reproduction number mathematical models meta-population models COVID-19 |
| title | Estimating the effective reproduction number for heterogeneous models using incidence data |
| title_full | Estimating the effective reproduction number for heterogeneous models using incidence data |
| title_fullStr | Estimating the effective reproduction number for heterogeneous models using incidence data |
| title_full_unstemmed | Estimating the effective reproduction number for heterogeneous models using incidence data |
| title_short | Estimating the effective reproduction number for heterogeneous models using incidence data |
| title_sort | estimating the effective reproduction number for heterogeneous models using incidence data |
| topic | effective reproduction number mathematical models meta-population models COVID-19 |
| url | https://royalsocietypublishing.org/doi/10.1098/rsos.220005 |
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