How to Efficiently Predict Dengue Incidence in Kuala Lumpur

Abstract

Mosquito-borne diseases are rapidly spreading in all regions of the world with an estimation of 2.5 billion people globally are at risk. The recent surge in dengue outbreaks has caused severe affliction to Malaysian society. Hence, the ability to predict a dengue outbreak and mitigate its damage and loss proactively is very critical. In this paper, we study the possibility of applying machine learning (ML) and deep learning (DL) approaches to predict the number of confirmed dengue fever (DF) cases in Kuala Lumpur. We identified several contribution factors correlate to a dengue outbreak. In addition to the two frequently used factors (daily mean temperature and daily rainfall), we also took into account the enhanced vegetation index (EVI), humidity and wind speed as input factors to our prediction engines. We collected and cleansed data on these factors and the daily DF incidents in Kuala Lumpur from 2002 to 2012. We then used these data to train and evaluate our 3 ML/DL models. Among the three models, GA_RNN was the best performer and achieved a MAE of 10.95 for DF incidence prediction.

Temporal and Spatiotemporal Arboviruses Forecasting by Machine Learning: A Systematic Review

“…When we evaluated the studies regarding the types of models used in the predictions, we observed that the vast majority of authors investigated moving average models (27), such as the Autoregressive Integrated Moving Average (ARIMA) (17,23,29,35,41,43,46,56,(61)(62)(63), Seasonal Autoregressive Integrated Moving Average (SARIMA) (55,(63)(64)(65)(66), Autoregressive Integrated Moving Average with Explanatory Variable (ARIMAX) (67). Several works have also presented a wide variety of models using artificial neural networks, mainly the LSTM (59,(68)(69)(70). But models using backpropagation neural networks (BPNN), GANN networks (60), Elman Recurrent Neural Network Levenberg Marquardt Algorithm (ERMN/LMA) (22), and Deep feed-forward neural networks (28) were also investigated.…”

A Deep Learning-based Dengue Mosquito Detection Method Using Faster R-CNN and Image Processing Techniques

“…Consequently, in defining input images, the active contouring algorithm was more efficient, but in order to connect an image of the object, the algorithm requires several complex sets of image operations. In [30], the anticipation number of confirmed dengue fever (DF) with three different prediction models based on machine learning and deep learning approach has been applied. Among www.aetic.theiaer.org the three different models, the GA-RNN model provides the best performance.…”

Utilização de técnicas de Machine Learning e de Deep Learning para a predição de casos de internações causadas por dengue em municípios da Paraíba

“…Dengue é uma doença endêmica, causada pelo vírus DENV, e transmitida através do mosquito Aedes aegypti. Atualmente, existem quatro tipos sorológicos do vírus (1, 2, 3 e 4) em circulação no Brasil [Pham et al 2018]. Embora não seja uma doença nova, ainda não existe vacina eficaz para a imunização da população contra todos os tipos do vírus.…”