ARBOALVO: A Bayesian spatiotemporal learning and predictive model for dengue cases in the endemic Northeast city of Natal, Rio Grande do Norte, Brazil
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.
Authors
- Taynãna César Simões (ORCID: https://orcid.org/0000-0002-5849-343X)
- Mariane Branco Alves (ORCID: https://orcid.org/0000-0002-2489-9300)
- Rafael Santos Erbisti (ORCID: https://orcid.org/0000-0002-6869-8391)
- Rodrigo Moreira Pedreira
- Marília Sá Carvalho (ORCID: https://orcid.org/0000-0002-9566-0284)
- Aline Araújo Nobre (ORCID: https://orcid.org/0000-0001-6306-9257)
- Nildimar Alves Honório (ORCID: https://orcid.org/0000-0002-2535-3844)
- Alessandre de Medeiros Tavares (ORCID: https://orcid.org/0000-0001-6505-5828)
- Marly de Fátima Carvalho de Melo (ORCID: https://orcid.org/0000-0001-8370-0819)
- Jan Pierre Martins de Araújo
Institutions
- Universidade Federal do Rio de Janeiro (BR)
- Universidade Federal Fluminense (BR)
- Secretaria Municipal de Saúde (BR)
- Fundação Oswaldo Cruz (BR)
Publication Details
- Journal
- PLoS neglected tropical diseases
- Published
- 2025-04-29
- DOI
- https://doi.org/10.1371/journal.pntd.0012984
- Citations
- 1
- Primary Topic
- Mosquito-borne diseases and control
- Type
- article
- Field-Weighted Citation Impact
- 1.88
Funders
- Fundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro
- Ministério da Saúde