Dengue Haemorrhagic Fever Outbreak Prediction using Elman Levenberg Neural Network and Genetic Algorithm

Saptarini, Ni Gusti Ayu Putu Harry; Dillak, Rocky Yefrenes; Pakan, Prisca Deviani

doi:10.1109/eiconcit.2018.8878529

Cited by 3 publications

(3 citation statements)

References 12 publications

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“…The investigated algorithms were: Moth Flame Optimization (MFO), Gray Wolf Optimizer (GWO), Firefly Algorithm (FA), and Artificial Bee Colony (ABC) algorithm. Saptarini et al (22), in turn, used Genetic Algorithm, as well as (68). Finally, we notice that, when it comes to predicting the count or incidence of arboviruses, there are a wide variety of model applications.…”

Section: Arboviruses (Counts) Predictionmentioning

confidence: 95%

“…In other words, dengue cases are generally considered as: dengue fever, dengue hemorrhagic fever and dengue shock syndrome, including local and imported cases. However, the studies of (22)(23)(24)(25)(26)(27) are only focused on prediction models for dengue hemorrhagic fever. Regarding the other two diseases transmitted by the Ae.…”

Section: Arboviruses (Counts) Predictionmentioning

confidence: 99%

“…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. Although neural networks have been extensively explored, in many studies, the authors did not explain the type of network they were investigating (23,45,46,61,66,71,72).…”

Section: Arboviruses (Counts) Predictionmentioning

confidence: 99%

See 2 more Smart Citations

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

Lima

Silva

Moreno

et al. 2022

Front. Public Health

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Arboviruses are a group of diseases that are transmitted by an arthropod vector. Since they are part of the Neglected Tropical Diseases that pose several public health challenges for countries around the world. The arboviruses' dynamics are governed by a combination of climatic, environmental, and human mobility factors. Arboviruses prediction models can be a support tool for decision-making by public health agents. In this study, we propose a systematic literature review to identify arboviruses prediction models, as well as models for their transmitter vector dynamics. To carry out this review, we searched reputable scientific bases such as IEE Xplore, PubMed, Science Direct, Springer Link, and Scopus. We search for studies published between the years 2015 and 2020, using a search string. A total of 429 articles were returned, however, after filtering by exclusion and inclusion criteria, 139 were included. Through this systematic review, it was possible to identify the challenges present in the construction of arboviruses prediction models, as well as the existing gap in the construction of spatiotemporal models.

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Section: Arboviruses (Counts) Predictionmentioning

confidence: 95%

Section: Arboviruses (Counts) Predictionmentioning

confidence: 99%

Section: Arboviruses (Counts) Predictionmentioning

confidence: 99%

See 1 more Smart Citation

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

Lima

Silva

Moreno

et al. 2022

Front. Public Health

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Predictive Analysis of Dengue Outbreak Based on an Improved Salp Swarm Algorithm

Mustaffa

Sulaiman

Rosli

et al. 2020

Cybernetics and Information Technologies

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The purpose of this study is to enhance the exploration capability of conventional Salp Swarm Algorithm (SSA) with the inducing of Levy Flight. With such modification, it will assist the SSA from trapping in local optimum. The proposed approach, which is later known as an improved SSA (iSSA) is employed in monthly dengue outbreak prediction. For that matter, monthly dataset of rainfall, humidity, temperature and number of dengue cases were employed, which render prediction information. The efficiency of the proposed algorithm is evaluated using Root Mean Square Error (RMSE), and compared against the conventional SSA and Ant Colony Optimization (ACO). The obtained results suggested that the iSSA was not only able to produce lower RMSE, but also capable to converge faster at lower rate as well.

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Big Geospatial Data and Data-Driven Methods for Urban Dengue Risk Forecasting: A Review

Dong

2022

Remote Sensing

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With advancements in big geospatial data and artificial intelligence, multi-source data and diverse data-driven methods have become common in dengue risk prediction. Understanding the current state of data and models in dengue risk prediction enables the implementation of efficient and accurate prediction in the future. Focusing on predictors, data sources, spatial and temporal scales, data-driven methods, and model evaluation, we performed a literature review based on 53 journal and conference papers published from 2018 to the present and concluded the following. (1) The predominant predictors include local climate conditions, historical dengue cases, vegetation indices, human mobility, population, internet search indices, social media indices, landscape, time index, and extreme weather events. (2) They are mainly derived from the official meteorological agency satellite-based datasets, public websites, department of health services and national electronic diseases surveillance systems, official statistics, and public transport datasets. (3) Country-level, province/state-level, city-level, district-level, and neighborhood-level are used as spatial scales, and the city-level scale received the most attention. The temporal scales include yearly, monthly, weekly, and daily, and both monthly and weekly are the most popular options. (4) Most studies define dengue risk forecasting as a regression task, and a few studies define it as a classification task. Data-driven methods can be categorized into single models, ensemble learning, and hybrid learning, with single models being further subdivided into time series, machine learning, and deep learning models. (5) Model evaluation concentrates primarily on the quantification of the difference/correlation between time-series observations and predicted values, the ability of models to determine whether a dengue outbreak occurs or not, and model uncertainty. Finally, we highlighted the importance of big geospatial data, data cloud computing, and other deep learning models in future dengue risk forecasting.

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Dengue Haemorrhagic Fever Outbreak Prediction using Elman Levenberg Neural Network and Genetic Algorithm

Cited by 3 publications

References 12 publications

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

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

Predictive Analysis of Dengue Outbreak Based on an Improved Salp Swarm Algorithm

Big Geospatial Data and Data-Driven Methods for Urban Dengue Risk Forecasting: A Review

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