Multiplicative Mixed-Effects Modelling of Dengue Incidence: An Analysis of the 2019 Outbreak in the Dominican Republic
Dengue is a vector-borne disease that is endemic to several countries, including the Dominican Republic, which has experienced dengue outbreaks for over four decades. With outbreaks growing in incidence in recent years, it is becoming increasingly important to develop better tools to understand driv...
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MDPI AG
2023-02-01
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| Series: | Axioms |
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| Online Access: | https://www.mdpi.com/2075-1680/12/2/150 |
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| author | Adelaide Freitas Helena Sofia Rodrigues Natália Martins Adela Iutis Michael A. Robert Demian Herrera Manuel Colomé-Hidalgo |
| author_facet | Adelaide Freitas Helena Sofia Rodrigues Natália Martins Adela Iutis Michael A. Robert Demian Herrera Manuel Colomé-Hidalgo |
| author_sort | Adelaide Freitas |
| collection | DOAJ |
| description | Dengue is a vector-borne disease that is endemic to several countries, including the Dominican Republic, which has experienced dengue outbreaks for over four decades. With outbreaks growing in incidence in recent years, it is becoming increasingly important to develop better tools to understand drivers of dengue transmission. Such tools are critical for providing timely information to assist healthcare authorities in preparing human, material, and medical resources for outbreaks. Here, we investigate associations between meteorological variables and dengue transmission in the Dominican Republic in 2019, the year in which the country’s largest outbreak to date ocurred. We apply generalized linear mixed modelling with gamma family and log link to model the weekly dengue incidence rate. Because correlations in lags between climate variables and dengue cases exhibited different behaviour among provinces, a backward-type selection method was executed to find a final model with lags in the explanatory variables. We find that in the best models, meteorological conditions such as temperature and rainfall have an impact with a delay of 2–5 weeks in the development of an outbreak, ensuring breeding conditions for mosquitoes. |
| first_indexed | 2024-03-11T09:10:17Z |
| format | Article |
| id | doaj.art-a4d465084f17424290d04b248015ca35 |
| institution | Directory Open Access Journal |
| issn | 2075-1680 |
| language | English |
| last_indexed | 2024-03-11T09:10:17Z |
| publishDate | 2023-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Axioms |
| spelling | doaj.art-a4d465084f17424290d04b248015ca352023-11-16T19:05:58ZengMDPI AGAxioms2075-16802023-02-0112215010.3390/axioms12020150Multiplicative Mixed-Effects Modelling of Dengue Incidence: An Analysis of the 2019 Outbreak in the Dominican RepublicAdelaide Freitas0Helena Sofia Rodrigues1Natália Martins2Adela Iutis3Michael A. Robert4Demian Herrera5Manuel Colomé-Hidalgo6Department of Mathematics, University of Aveiro, 3810-193 Aveiro, PortugalCenter for Research and Development in Mathematics and Applications (CIDMA), University of Aveiro, 3810-193 Aveiro, PortugalDepartment of Mathematics, University of Aveiro, 3810-193 Aveiro, PortugalDepartment of Mathematics, University of Aveiro, 3810-193 Aveiro, PortugalDepartment of Mathematics, Virginia Tech, Blacksburg, VA 24061, USACentro de Investigación en Salud Dr. Hugo Mendoza, Hospital Pediátrico Dr. Hugo Mendoza, Santo Domingo 10117, Dominican RepublicInstituto Tecnológico de Santo Domingo (INTEC), Santo Domingo 10602, Dominican RepublicDengue is a vector-borne disease that is endemic to several countries, including the Dominican Republic, which has experienced dengue outbreaks for over four decades. With outbreaks growing in incidence in recent years, it is becoming increasingly important to develop better tools to understand drivers of dengue transmission. Such tools are critical for providing timely information to assist healthcare authorities in preparing human, material, and medical resources for outbreaks. Here, we investigate associations between meteorological variables and dengue transmission in the Dominican Republic in 2019, the year in which the country’s largest outbreak to date ocurred. We apply generalized linear mixed modelling with gamma family and log link to model the weekly dengue incidence rate. Because correlations in lags between climate variables and dengue cases exhibited different behaviour among provinces, a backward-type selection method was executed to find a final model with lags in the explanatory variables. We find that in the best models, meteorological conditions such as temperature and rainfall have an impact with a delay of 2–5 weeks in the development of an outbreak, ensuring breeding conditions for mosquitoes.https://www.mdpi.com/2075-1680/12/2/150dengueDominican Republicclimate variableslagsgeneralized linear mixed models |
| spellingShingle | Adelaide Freitas Helena Sofia Rodrigues Natália Martins Adela Iutis Michael A. Robert Demian Herrera Manuel Colomé-Hidalgo Multiplicative Mixed-Effects Modelling of Dengue Incidence: An Analysis of the 2019 Outbreak in the Dominican Republic Axioms dengue Dominican Republic climate variables lags generalized linear mixed models |
| title | Multiplicative Mixed-Effects Modelling of Dengue Incidence: An Analysis of the 2019 Outbreak in the Dominican Republic |
| title_full | Multiplicative Mixed-Effects Modelling of Dengue Incidence: An Analysis of the 2019 Outbreak in the Dominican Republic |
| title_fullStr | Multiplicative Mixed-Effects Modelling of Dengue Incidence: An Analysis of the 2019 Outbreak in the Dominican Republic |
| title_full_unstemmed | Multiplicative Mixed-Effects Modelling of Dengue Incidence: An Analysis of the 2019 Outbreak in the Dominican Republic |
| title_short | Multiplicative Mixed-Effects Modelling of Dengue Incidence: An Analysis of the 2019 Outbreak in the Dominican Republic |
| title_sort | multiplicative mixed effects modelling of dengue incidence an analysis of the 2019 outbreak in the dominican republic |
| topic | dengue Dominican Republic climate variables lags generalized linear mixed models |
| url | https://www.mdpi.com/2075-1680/12/2/150 |
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