Dengue Fever Outbreak Prediction in Surabaya using A Geographically Weighted Regression

Halim, Siana; Octavia, Tanti; Felecia,; Handojo, Andreas

doi:10.1109/times-icon47539.2019.9024438

Cited by 5 publications

(9 citation statements)

References 10 publications

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“…The same dataset has been modelled using the Geostatistical Weighted Regression (GWR) [13]. In [13], the predicted model can follow the pattern of the actual dataset. However, the MSE of the prediction for the years 2017-2018 is 8.59.…”

Section: Discussionmentioning

confidence: 99%

“…It is transmitted by Aedes mosquito which infected with a dengue virus. The spreading of this diseases is spatially correlated [13]. The MLP model does not accommodate the spatial dependent in the neural-network construction.…”

Section: Spatial Multilayer Perceptronmentioning

confidence: 99%

“…A four years dataset from Bandung stated that the incidence rate of dengue fever was not related to annual rainfall, population density, larva free index, and prevention Program [12]. The spreading of DF in Surabaya, Indonesia, is modelled using statistical learning [13,14]. Besides of statistical learning approach, many researchers also developed the model in the machine learning approach.…”

Section: Introductionmentioning

confidence: 99%

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Spatial Multi-Layer Perceptron Model for Predicting Dengue Fever Outbreaks in Surabaya

Halim¹,

Handojo²,

Widodo³

et al. 2020

Adv. sci. technol. eng. syst. j.

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Dengue fever (DF) is a tropical disease spread by mosquitoes of the Aedes type. Therefore, a DF outbreak needs to be predicted to minimize the spread and death caused by it. The spread of dengue fever is a spatial problem. In this paper, we adopted the Multi Linear Perceptron (MLP) to solve the spatial problem, and we called it a spatial multi-layer perceptron model (Spatial MLP). In this proposed model, we consider two types of input neurons in the Spatial MLP, a region and the neighbourhood of that region. The spatial inputs dynamically change to the region. Additionally, the neighbourhood numbers of a region are also varied. So, the spatial inputs are changed in terms of the number of inputs and the neighbourhoods. As a result, the proposed model is outperformed the traditional MLP since it can adapt to the neighbourhoods. We can conclude the spatial MLP model can manage the information and predict the dengue fever outbreak in Surabaya

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Section: Discussionmentioning

confidence: 99%

Section: Spatial Multilayer Perceptronmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Spatial Multi-Layer Perceptron Model for Predicting Dengue Fever Outbreaks in Surabaya

Halim¹,

Handojo²,

Widodo³

et al. 2020

Adv. sci. technol. eng. syst. j.

Self Cite

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“…Therefore, we observed a wide variety of studies that used at least one of the following variables as model attributes: temperature, rain, and relative humidity. However, some studies included other parameters in their models such as the number of rainy days (39)(40)(41), number of stormy days, and wind speed (41).…”

Section: Arboviruses (Counts) Predictionmentioning

confidence: 99%

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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“…In Surabaya's case, additional to the weather condition, we also explore the population density, the precipitation and the poverty percentage as the factors that may affect the DHF. In the previous study, we [12] used the geographically weighted regression to predict the dengue fever outbreak in Surabaya, to continue the exploration, in this paper, we model the outbreak prediction using statistical learning approaches.…”

Section: Introductionmentioning

confidence: 99%

Statistical Learning for Predicting Dengue Fever Rate in Surabaya

Halim

Felecia

Octavia

2020

jti

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Dengue fever happening most in tropical countries and considered as the fastest spreading mosquito-borne disease which is endemic and estimated to have 96 million cases annually. It is transmitted by Aedes mosquito which infected with a dengue virus. Therefore, predicting the dengue fever rate as become the subject of researches in many tropical countries. Some of them use statistical and machine learning approach to predict the rate of the disease so that the government can prevent that incident. In this study, we explore many models in the statistical learning approaches for predicting the dengue fever rate. We applied several methods in the predictive statistics such as regression, spatial regression, geographically weighted regression and robust geographically weighted regression to predict the dengue fever rate in Surabaya. We then analyse the results, compare them based on the mean square error. Those four models are chosen, to show the global estimator’s approaches, e.g. regression, and the local ones, e.g. geographically weighted regression. The model with the minimum mean square error is regarded as the most suitable model in the statistical learning area for solving the problem. Here, we look at the estimates of the dengue fever rate in the year 2012, to 2017, area, poverty percentage, precipitation, number of rainy days for predicting the dengue fever outbreak in the year 2018. In this study, the pattern of the predicted model can follow the pattern of the true dataset.

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Dengue Fever Outbreak Prediction in Surabaya using A Geographically Weighted Regression

Cited by 5 publications

References 10 publications

Spatial Multi-Layer Perceptron Model for Predicting Dengue Fever Outbreaks in Surabaya

Spatial Multi-Layer Perceptron Model for Predicting Dengue Fever Outbreaks in Surabaya

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

Statistical Learning for Predicting Dengue Fever Rate in Surabaya

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