Developing a Dengue Forecasting Model: A Case Study in Iligan City

Olmoguez, Ian Lindley G; Catindig, Mia Amor C.; Amongos, Minchie Fel Lou; Lazan, Fatima G.

doi:10.14569/ijacsa.2019.0100936

Cited by 3 publications

(3 citation statements)

References 5 publications

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“…Random forests have been used to forecast dengue risk in several countries including Costa Rica [29], Philippines [30,31], Pakistan [32], Peru and Puerto Rico [33]. However, time or seasonal variables were not always included in the models nor were sociodemographic predictors, which have been found to improve forecast accuracy in HIV [34] and Ebola [35] epidemic models.…”

Section: Introductionmentioning

confidence: 99%

Machine learning and dengue forecasting: Comparing random forests and artificial neural networks for predicting dengue burden at national and sub-national scales in Colombia

Zhao

Charland²,

Carabalí

et al. 2020

PLoS Negl Trop Dis

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The robust estimate and forecast capability of random forests (RF) has been widely recognized, however this ensemble machine learning method has not been widely used in mosquito-borne disease forecasting. In this study, two sets of RF models were developed at the national (pooled department-level data) and department level in Colombia to predict weekly dengue cases for 12-weeks ahead. A pooled national model based on artificial neural networks (ANN) was also developed and used as a comparator to the RF models. The various predictors included historic dengue cases, satellite-derived estimates for vegetation, precipitation, and air temperature, as well as population counts, income inequality, and education. Our RF model trained on the pooled national data was more accurate for department-specific weekly dengue cases estimation compared to a local model trained only on the department's data. Additionally, the forecast errors of the national RF model were smaller to those of the national pooled ANN model and were increased with the forecast horizon increasing from one-week-ahead (mean absolute error, MAE: 9.32) to 12-weeks ahead (MAE: 24.56). There was considerable variation in the relative importance of predictors dependent on forecast horizon. The environmental and meteorological predictors were relatively important for short-term dengue forecast horizons while socio-demographic predictors were relevant for longer-term forecast horizons. This study demonstrates the potential of RF in dengue forecasting with a feasible approach of using a national pooled model to forecast at finer spatial PLOS NEGLECTED TROPICAL DISEASES

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Section: Introductionmentioning

confidence: 99%

Machine learning and dengue forecasting: Comparing random forests and artificial neural networks for predicting dengue burden at national and sub-national scales in Colombia

Zhao

Charland²,

Carabalí

et al. 2020

PLoS Negl Trop Dis

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“…In another case study at Iligan City in Mindanao, researchers used multiple linear regression (MLR) analysis, Poisson regression, and random forest in developing a best-fitting model for dengue incidence in the area. Considering the monthly climatic factors -relative rainfall maximum temperature and average (relative humidity -together with the monthly time period from 2008 to 2017 (Olmoguez et al, 2019), results show that the MLR model, having 18% accuracy percentage and 67.14% error result, has the form (1) However, they further conclude that the Random Forest performed better with a 73% accuracy percentage and 33.58% error result.…”

Section: Introductionmentioning

confidence: 96%

Forecasting and on the Influence of Climatic Factors on Rising Dengue Incidence in Baguio City, Philippines

Marigmen

Addawe

2022

JCIA

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Dengue fever or dengue has been a concern for individuals living in Baguio City, Philippines. Every year, incidence counts rise during rainy seasons experienced from June to October. Several researches suggest that meteorological factors have great influence on the life, growth, and reproduction of dengue-carrying mosquitoes, resulting in higher dengue incidence in the area. With the continuing rise of dengue incidence in Baguio City, we aim to forecast dengue incidence in the area for the year 2019, starting from January until the end of the outbreak period in the area. Here, we use the projections package of R as it involves the serial interval distribution and 𝑅𝑡 value of dengue incidence. We also aim to use multiple linear regression analysis to determine if meteorological factors have significant effects in the rise of dengue incidence in the city. With the inclusion of time-varying reproduction number and serial interval distribution of dengue, we projected that dengue incidence may reach up to 101 cases by June 16, 2021, and without further actions, cases may rise up to 529 cases by August 29, 2021. Based on the average two-year period, such increase is attributed to relative humidity and average temperature as these are the most significant factors associated with dengue incidence based on the MLR analysis. The highest and mean maximum temperatures remain as key meteorological variables that influence dengue incidence in the city. As early as possible, local officials are recommended to uphold proper safety and health procedures in preventing the spread of dengue in Baguio City.

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“…Six of these events occurred in regions where dengue had never been transmitted [3]. Like other countries, the Philippines has seen an upsurge in dengue cases, which has made the disease a significant public health concern [4].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of a Prototype Mobile Application Based on an Expert System for the Diagnosis of Diseases Transmitted by the Aedes Aegypti Mosquito

Jaúregui-Velarde,

Molina-Velarde,

Yactayo-Arias

et al. 2023

Int. J. Onl. Eng.

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The Aedes aegypti mosquito transmits the dengue, zika, and chikungunya viruses, endangering the health and lives of people in affected countries due to a lack of timely diagnosis. The objective of this study is to design and evaluate the feasibility of a mobile application based on an expert system for early diagnosis of diseases transmitted by the Aedes aegypti mosquito. The Buchanan methodology was used to develop the application. The results obtained show that the proposed mobile application has a diagnostic accuracy of 83%, a sensitivity of 91%, a specificity of 63%, and an error rate of 17%. The technical aspects of the application were also evaluated through a questionnaire administered to five computer experts. The results showed that the technical aspects of the application received an average rating of 3.91 out of a maximum of 5, with a standard deviation of 0.482. In addition, the usability of the application was evaluated using the standardized System Usability Scale (SUS), which was administered to a total of 15 users. The results of this evaluation showed that the application received an average score of 83 on the SUS scale, indicating a positive level of usability. In conclusion, the results support the effectiveness and potential of the application for the early diagnosis of diseases transmitted by the Aedes aegypti mosquito, providing a useful tool for the rapid detection of these diseases. Although it requires more attention to specificity and error rate to improve the accuracy of the diagnosis.

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Developing a Dengue Forecasting Model: A Case Study in Iligan City

Cited by 3 publications

References 5 publications

Machine learning and dengue forecasting: Comparing random forests and artificial neural networks for predicting dengue burden at national and sub-national scales in Colombia

Machine learning and dengue forecasting: Comparing random forests and artificial neural networks for predicting dengue burden at national and sub-national scales in Colombia

Forecasting and on the Influence of Climatic Factors on Rising Dengue Incidence in Baguio City, Philippines

Evaluation of a Prototype Mobile Application Based on an Expert System for the Diagnosis of Diseases Transmitted by the Aedes Aegypti Mosquito

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