Identifying the main drivers of transmission in the early phase of the COVID-19 pandemic in Portugal
Abstract In this study, we employed a modeling approach to describe how changes in age-specific epidemiological characteristics, such as behaviour, i.e. contact patterns, susceptibility and infectivity, influence the basic reproduction number $$R_0$$ R 0 , while accounting for heterogeneity in trans...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2024-12-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-024-76604-6 |
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| _version_ | 1846101311974539264 |
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| author | Constantino Caetano Leonardo Angeli Irma Varela-Lasheras Pietro Coletti Luisa Morgado Pedro Lima Lander Willem Baltazar Nunes Niel Hens |
| author_facet | Constantino Caetano Leonardo Angeli Irma Varela-Lasheras Pietro Coletti Luisa Morgado Pedro Lima Lander Willem Baltazar Nunes Niel Hens |
| author_sort | Constantino Caetano |
| collection | DOAJ |
| description | Abstract In this study, we employed a modeling approach to describe how changes in age-specific epidemiological characteristics, such as behaviour, i.e. contact patterns, susceptibility and infectivity, influence the basic reproduction number $$R_0$$ R 0 , while accounting for heterogeneity in transmission. We computed sensitivity measures related to $$R_0$$ R 0 , that describe the relative contribution of each age group towards overall transmission. Additionally, we proposed a new indicator that provides the expected relative change in the number of new infections, given a public health intervention. Studying the outbreak of COVID-19 in Portugal during March 2020, our results show that the main drivers of transmission were individuals 30–59 years old. Furthermore, by studying the impact of imposed changes in susceptibility and infectivity, our results demonstrate that a 10% decrease in susceptibility for the 30–39 years old results in a incidence reduction after 3 generations of approximately 17% in this age group and 4–6% reduction as an indirect effect in the remaining age groups. The presented methodology provides tools to inform the allocation strategy of mitigation measures in an outbreak of an infectious disease. Its inherent versatility enables the easy incorporation of data specific to various populations, facilitating a comparative analysis of epidemic control effects across different countries. |
| format | Article |
| id | doaj-art-14fb327a0342418bb715fcc1d7f31a1f |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-14fb327a0342418bb715fcc1d7f31a1f2024-12-29T12:15:40ZengNature PortfolioScientific Reports2045-23222024-12-011411710.1038/s41598-024-76604-6Identifying the main drivers of transmission in the early phase of the COVID-19 pandemic in PortugalConstantino Caetano0Leonardo Angeli1Irma Varela-Lasheras2Pietro Coletti3Luisa Morgado4Pedro Lima5Lander Willem6Baltazar Nunes7Niel Hens8Departamento de Epidemiologia, Instituto Nacional de Saúde Doutor Ricardo JorgeInteruniversity Institute for Biostatistics and Statistical Bioinformatics, Data Science Institute, Hasselt UniversityDepartamento de Epidemiologia, Instituto Nacional de Saúde Doutor Ricardo JorgeInteruniversity Institute for Biostatistics and Statistical Bioinformatics, Data Science Institute, Hasselt UniversityCenter for Computational and Stochastic Mathematics, Instituto Superior TécnicoCenter for Computational and Stochastic Mathematics, Instituto Superior TécnicoCentre for Health Economic Research and Modelling Infectious Diseases, Vaccine and Infectious Disease Institute, University of AntwerpDepartamento de Epidemiologia, Instituto Nacional de Saúde Doutor Ricardo JorgeInteruniversity Institute for Biostatistics and Statistical Bioinformatics, Data Science Institute, Hasselt UniversityAbstract In this study, we employed a modeling approach to describe how changes in age-specific epidemiological characteristics, such as behaviour, i.e. contact patterns, susceptibility and infectivity, influence the basic reproduction number $$R_0$$ R 0 , while accounting for heterogeneity in transmission. We computed sensitivity measures related to $$R_0$$ R 0 , that describe the relative contribution of each age group towards overall transmission. Additionally, we proposed a new indicator that provides the expected relative change in the number of new infections, given a public health intervention. Studying the outbreak of COVID-19 in Portugal during March 2020, our results show that the main drivers of transmission were individuals 30–59 years old. Furthermore, by studying the impact of imposed changes in susceptibility and infectivity, our results demonstrate that a 10% decrease in susceptibility for the 30–39 years old results in a incidence reduction after 3 generations of approximately 17% in this age group and 4–6% reduction as an indirect effect in the remaining age groups. The presented methodology provides tools to inform the allocation strategy of mitigation measures in an outbreak of an infectious disease. Its inherent versatility enables the easy incorporation of data specific to various populations, facilitating a comparative analysis of epidemic control effects across different countries.https://doi.org/10.1038/s41598-024-76604-6 |
| spellingShingle | Constantino Caetano Leonardo Angeli Irma Varela-Lasheras Pietro Coletti Luisa Morgado Pedro Lima Lander Willem Baltazar Nunes Niel Hens Identifying the main drivers of transmission in the early phase of the COVID-19 pandemic in Portugal Scientific Reports |
| title | Identifying the main drivers of transmission in the early phase of the COVID-19 pandemic in Portugal |
| title_full | Identifying the main drivers of transmission in the early phase of the COVID-19 pandemic in Portugal |
| title_fullStr | Identifying the main drivers of transmission in the early phase of the COVID-19 pandemic in Portugal |
| title_full_unstemmed | Identifying the main drivers of transmission in the early phase of the COVID-19 pandemic in Portugal |
| title_short | Identifying the main drivers of transmission in the early phase of the COVID-19 pandemic in Portugal |
| title_sort | identifying the main drivers of transmission in the early phase of the covid 19 pandemic in portugal |
| url | https://doi.org/10.1038/s41598-024-76604-6 |
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