ArticleInsects2020
Evaluation of the Models for Forecasting Dengue in Brazil from 2000 to 2017: An Ecological Time-Series Study.
Article in Insects, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
4 citing papers in PubMed.
- Dengue fever prediction based on meteorological features and deep learning models.Infectious Disease Modelling · 2026Article
- AI-based prediction of dengue incidence using climatic, environmental, and socio-demographic factors: an ensemble random forest approach with agile system development.BMC infectious diseases · 2026Article
- Silent circulation of Chikungunya virus among pregnant women and newborns in the Western Brazilian Amazon before the first outbreak of chikungunya fever.Revista do Instituto de Medicina Tropical de Sao Paulo · 2022Article
- Dengue-2 and Guadeloupe Mosquito Virus RNA Detected inInsects · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
We aimed to evaluate the accuracy of deterministic and stochastic statistical models by means of a protocol developed in a free programming environment for monthly time-series analysis of the incidence of confirmed dengue cases in the states and federal district of Brazil from January 2000 to December 2017. This was an ecological time-series study conducted to evaluate and validate the accuracy of 10 statistical models for predicting the new cases of dengue. Official data on the monthly cases of dengue from January 2000 to December 2016 were used to train the statistical models, while those for the period January-December 2017 were used to test the predictive capacity of the models by considering three forecasting horizons (12, 6, and 3 months). Deterministic models proved to be reliable for predicting dengue in a 12-month forecasting horizon, while stochastic models were reliable for predicting the disease in a 3-month forecasting horizon. We were able to reliably employ models for predicting dengue in the states and federal district of Brazil. Hence, we strongly recommend incorporating these models in state health services for predicting dengue and for decision-making with regard to the advanced planning of interventions before the emergence of epidemics.
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Registered trials
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