Data-driven computational intelligence applied to dengue outbreak forecasting: a case study at the scale of the city of Natal, RN-Brazil

Abstract

Dengue is recognized as a health problem that causes significant socioeconomic impacts throughout the world, affecting millions of people each year. A commonly used method for monitoring the dengue vector is to count the eggs that Aedes aegypti mosquitoes have laid in spatially distributed ovitraps. Given this approach, the present study uses a database collected from 397 ovitraps allocated across the city of Natal, RN—Brazil. The Egg Density Index for each neighborhood was computed weekly, over four complete years (from 2016 to 2019), and simultaneously analyzed with the dengue case incidence. Our results illustrate that the incidence of dengue is related to the socioeconomic level of the neighborhoods in the city of Natal. A deep learning algorithm was used to predict future dengue case incidence, either based on the previous weeks of dengue incidence or the number of eggs present in the ovitraps. The analysis reveals that ovitrap data allows earlier prediction (four to six weeks) compared to dengue incidence itself (one week). Therefore, the results validate that the quantification of Aedes aegypti eggs can be valuable for the early planning of public health interventions.

Dengue Prediction in Latin America Using Machine Learning and the One Health Perspective: A Literature Review

“…There are some studies that focus specifically on ML techniques for dengue predictive purposes in the Americas; several studies compare the effectiveness of different predictive ML techniques. One of the more interesting is [ 97 ], which used a neural network model based on Long Short-Term Memory (LSTM) to predict future dengue cases. The predictors were collected weekly from 2016 to 2019, consisting of dengue incidence and the Egg Density Index from 397 ovitraps dispersed over the municipality of Natal, Brazil.…”

The practicality of Malaysia dengue outbreak forecasting model as an early warning system

“…Since the intervention was implemented after 4 weeks that larvae have existed, there is ample time for dengue transmission. We then can expect a subsequent initial increase in dengue cases being notified from the locality 3–4 weeks after the existence of larvae ( Sanchez-Gendriz et al., 2022 ; Rohani et al., 2018 ). The reason is these intervention methods cannot prevent dengue outbreaks by themselves as they are not sustainable.…”

“…Six forecasting methods were used, which were Autoregressive Distributed Lag (ADL) model, Hierarchical Forecasting (Bottom-up and Optimal combination), and Machine Learning (ML) methods (Artificial Neural Network (ANN), Support Vector Machine (SVM), and Random Forest). The ADL and ML forecasting methods were used because based on past studies ( Chiung et al., 2018 ; Guo et al., 2017 ; Liu, Yin, et al., 2021 ; Ong et al., 2018 ; Patil & Pandya, 2021 ; Sanchez-Gendriz et al., 2022 ) showed their relevance in predicting dengue outbreaks. They had also proved their value in various forecasting evaluation studies (see e.g., Makridakis et al., 2018 ).…”

“…Consequently, many studies have been conducted to predict dengue outbreaks using mathematical models (either statistical or artificial intelligence or both) in attempts to create an early warning system to prevent dengue. Sanchez-Gendriz et al. (2022) developed a data-driven computational intelligence approach to forecasting the dengue outbreak at Natal, Brazil, using Regression and Neural Network models.…”

Integrating gated recurrent unit in graph neural network to improve infectious disease prediction: an attempt

“…In fact, the neural networks were also applied to the prediction problem of other epidemics. Sanchez-Gendriz et al ( 14 ) applied Long Short-Term Memory (LSTM) network in the prediction of dengue outbreak in Natal, demonstrates the potential of neural network in disease surveillance at a local scale. And It is worthwhile to research the potential of neural network in epidemic time series data prediction.…”