| Author | Alves, Mariane Branco | |
| Author | Erbisti, Rafael Santos | |
| Author | Nobre, Aline Araújo | |
| Author | Simões, Taynãna César | |
| Author | Tavares, Alessandre de Medeiros | |
| Author | Melo, Márcia Cristina | |
| Author | Pedreira, Rodrigo Moreira | |
| Author | Araújo, Jan Pierre Martins de | |
| Author | Carvalho, Marilia Sá | |
| Author | Honório, Nildimar Alves | |
| Access date | 2025-05-09T13:40:32Z | |
| Available date | 2025-05-09T13:40:32Z | |
| Document date | 2025 | |
| Citation | ALVES, Mariane Branco et al. ARBOALVO: a bayesian spatiotemporial learning and predictive model for dengue caises in the endemic Northeast city of Natal, Rio Grande do Norte, Brazil. PLoS Neglected Tropical Diseases, v. 19, n. 4, p. 1-24, 29 Apr. 2025. | |
| ISSN | 1935-2727 | |
| URI | https://www.arca.fiocruz.br/handle/icict/70328 | |
| Description | Produção científica do Laboratório de Interações Vírus-Hospedeiros. | pt_BR |
| Description | Author summary: | en_US |
| Description | Urban arbovirus transmission exhibits spatial and temporal heterogeneity. Focusing on dengue, we employed statistical models that account for spatiotemporal variability to provide a realistic assessment of transmission dynamics. By using dengue case data from Natal, RN (2015–2018) and incorporating entomological, climatic, and sociosanitary indicators, we forecasted case counts for the following four weeks. Our analysis identified significant correlations between increased dengue risk and key factors: reported cases in the previous week, the Aedes egg positivity index, and mean daytime temperature from preceding weeks. High-risk neighborhoods with persistent dengue transmission were pinpointed, emphasizing areas for targeted interventions. This Bayesian space-time approach supports operational control efforts by identifying priority areas and clarifying how various factors influence dengue transmission. The findings have significant public health implications, enabling more precise, proactive strategies to mitigate the incidence and impact of urban arboviruses. | en_US |
| Sponsorship | Funded by the Brazilian Ministry of Health. Fundação de Amparo à Pesquisa do Estado do Rio de Janeiro (FAPERJ) - grant number E-26/201.207/2022 (Nildimar Alves Honório). | |
| Language | eng | en_US |
| Publisher | Public Library of Science | |
| Rights | open access | |
| Title | ARBOALVO: a bayesian spatiotemporial learning and predictive model for dengue caises in the endemic Northeast city of Natal, Rio Grande do Norte, Brazil | en_US |
| Type | Preprint | |
| DOI | 10.1371/journal.pntd.0012984 | |
| Abstract | Background: Urban arbovirus transmission is spatially and temporally heterogeneous. Estimating the risk of dengue through statistical models that consider simultaneous variability in space and time provides more realistic estimates of transmission dynamics, facilitating the identification of priority areas for intervention focused on surveillance and control. These models also enable predictions to support timely interventions for arboviruses like dengue, chikungunya, and Zika. Methodology/principal findings: We analyzed dengue case reports by epidemiological week and neighborhood in Natal, RN from 2015 to 2018. Temporal conditional autoregressive models were fitted using the Integrated Nested Laplace approximation method. The predictors included a set of entomological, climatic and sociosanitary indicators with temporal lags, along with structures of temporal and spatial dependence. Additionally, we used an offset term to represent the expected number of dengue cases per neighborhood at each epidemiological week, under the hypothesis of homogeneity in the occurrence of cases across the municipality. We forecasted dengue case counts for the subsequent four weeks, addressing both zero occurrences and fluctuations during non-zero periods. Weekly risk dynamics were visualized through predictive maps, enabling the timely identification of neighborhoods with high and persistent dengue risk, that is, areas consistently exhibiting a high number of dengue cases that remained concentrated in the same location for several weeks. The optimal model revealed a significant rise in dengue occurrence probability during the observation week, associated with increased cases in the previous week, the Aedes egg positivity index from the prior four weeks, and the mean daytime temperature 6–8 weeks earlier. Dengue risk also rose with a one-standard-deviation increase in the density of the impoverished population per occupied area and the mean Aedes egg density index from the preceding 3–5 weeks. Conclusions/significance: The proposed Bayesian space-time analysis can contribute to the operational control of dengue and Aedes aegypti by identifying priority areas and forecasting dengue cases for the next four weeks. It also quantifies the effects of entomological, sociosanitary, climatic and demographic indicators on both the likelihood of dengue occurrence and the intensity of outbreaks. | en_US |
| Affilliation | Universidade Federal do Rio de Janeiro. Centro de Ciências Matemáticas e da Natureza. Instituto de Matemática. Departamento de Métodos Estatísticos. Rio de Janeiro, RJ, Brasil. | |
| Affilliation | Universidade Federal Fluminense. Instituto de Matemática e Estatística. Departamento de Estatística. Niterói, RJ, Brasil. | |
| Affilliation | Fundação Oswaldo Cruz. Presidência. Programa de Computação Científica. Rio de Janeiro, RJ, Brasil. | |
| Affilliation | Fundação Oswaldo Cruz. Instituto René Rachou. Belo Horizonte, MG, Brasil. | |
| Affilliation | Sem afiliação. | |
| Affilliation | Prefeitura Municipal do Natal. Secretaria Municipal de Saúde de Natal. Centro de Controle de Zoonoses. Natal, RN, Brasil. | |
| Affilliation | Prefeitura Municipal do Natal. Secretaria Municipal de Saúde de Natal. Centro de Controle de Zoonoses. Natal, RN, Brasil. | |
| Affilliation | Prefeitura Municipal do Natal. Secretaria Municipal de Saúde de Natal. Centro de Controle de Zoonoses. Natal, RN, Brasil. | |
| Affilliation | Fundação Oswaldo Cruz. Presidência. Programa de Computação Científica. Rio de Janeiro, RJ, Brasil. | |
| Affilliation | Fundação Oswaldo Cruz. Instituto Oswaldo Cruz. Laboratório de Interações Vírus-Hospedeiros. Rio de Janeiro, RJ, Brasil / Fundação Oswaldo Cruz. Presidência. Vice-Presidência de Ambiente, Atenção e Promoção da Saúde. Núcleo Operacional Sentinela de Mosquitos Vetores. Rio de Janeiro, RJ, Brasil. | |
| Subject | Dengue fever | en_US |
| Subject | Neighborhoods | en_US |
| Subject | Epidemiology | en_US |
| Subject | Eggs | en_US |
| Subject | Entomology | en_US |
| Subject | Medical risk factors | en_US |
| Subject | Arboviruses | en_US |
| Subject | Brazil | en_US |
| DeCS | Dengue | pt_BR |
| DeCS | Características de Residência | pt_BR |
| DeCS | Epidemiologia | pt_BR |
| DeCS | Ovos | pt_BR |
| DeCS | Entomologia | pt_BR |
| DeCS | Arbovírus | pt_BR |
| DeCS | Brasil | pt_BR |
| e-ISSN | 1935-2735 | |
| xmlui.metadata.dc.subject.ods | 03 Saúde e Bem-Estar | |
| xmlui.metadata.dc.subject.ods | 09 Indústria, inovação e infraestrutura | |
| xmlui.metadata.dc.subject.ods | 17 Parcerias e meios de implementação | |
