Data Driven Prediction of Dengue Incidence in Thailand

Sumanasinghe, Nirosha; Mikler, Armin R.; Muthukudage, Jayantha; Tiwari, Chetan; Quiroz, Reynaldo

doi:10.1007/978-3-319-60663-7_9

Cited by 2 publications

(2 citation statements)

References 11 publications

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“…Dengue fever is caused by dengue viral infection, which is annually emerged in tropical and sub-tropical regions [33,34]. Dengue virus (DENV) is a member of the Flaviviruses, consisting of four serotypes.…”

Section: Introductionmentioning

confidence: 99%

Multiple Virtual Screening Strategies for the Discovery of Novel Compounds Active Against Dengue Virus: A Hit Identification Study

et al. 2019

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Dengue infection is caused by a mosquito-borne virus, particularly in children, which may even cause death. No effective prevention or therapeutic agents to cure this disease are available up to now. The dengue viral envelope (E) protein was discovered to be a promising target for inhibition in several steps of viral infection. Structure-based virtual screening has become an important technique to identify first hits in a drug screening process, as it is possible to reduce the number of compounds to be assayed, allowing to save resources. In the present study, pharmacophore models were generated using the common hits approach (CHA), starting from trajectories obtained from molecular dynamics (MD) simulations of the E protein complexed with the active inhibitor, flavanone (FN5Y). Subsequently, compounds presented in various drug databases were screened using the LigandScout 4.2 program. The obtained hits were analyzed in more detail by molecular docking, followed by extensive MD simulations of the complexes. The highest-ranked compound from this procedure was then synthesized and tested on its inhibitory efficiency by experimental assays.

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Section: Introductionmentioning

confidence: 99%

Multiple Virtual Screening Strategies for the Discovery of Novel Compounds Active Against Dengue Virus: A Hit Identification Study

et al. 2019

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“…The sophisticated methods such as a combination of flower pollination algorithm and least squares support vector machines (FPA-LSSVM) [21] and ANFIS with various parameter tuning methods [22] are applied to forecast dengue incidence in Indonesia. Modeling dengue incidence in Thailand includes a wide range of techniques such as exploratory analysis through the remote sensing GIS [23], Poisson autoregressive [24], cellular automata with the inclusion of genetic algorithm for chromosome weighting and ANN for determining probability in state change [25], SVR [26], Bayesian network [27], and generalized additive model (GAM) [28].…”

Section: Related Workmentioning

confidence: 99%

Computational Intelligence and Statistical Learning Performances on Predicting Dengue Incidence using Remote Sensing Data

Kerdprasop¹,

Kerdprasop²

2020

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

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Dengue is a viral infection disease transmitted to people through the bite of specific mosquito species living in a tropical zone. According to the World Health Organization, dengue has been listed among the top-ten diseases for 2019 as it makes 3.9 billion people in 128 countries be at risk of infection. One major cause of substantial dengue widespread is the globally warm climate that accelerates rapid growth of mosquito vectors. In this research, we aim to build data-driven models to predict dengue cases using satellite index data to represent temperature, humidity, and greenness over the surface area of Bangkok, which is our target area of dengue prediction because of its high infection cases. Oceanic Niño Index is also used as a predictor variable to represent climate variability. The modeling methods employ seven algorithms from two broad schemes of the machine learning field. Artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) are algorithms from the subfield of computational intelligence, whereas multiple linear regression (MLR), generalized linear model (GLM), support vector regression (SVR), classification and regression tree (CART), and chi-squared automatic interaction detection (CHAID) are from the statistical learning subfield. Performances of these algorithms are evaluated on the same set of out-of-sample data. The results are that ANFIS is the best model for predicting dengue outbreak in the capital city of Thailand.

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Data Driven Prediction of Dengue Incidence in Thailand

Cited by 2 publications

References 11 publications

Multiple Virtual Screening Strategies for the Discovery of Novel Compounds Active Against Dengue Virus: A Hit Identification Study

Multiple Virtual Screening Strategies for the Discovery of Novel Compounds Active Against Dengue Virus: A Hit Identification Study

Computational Intelligence and Statistical Learning Performances on Predicting Dengue Incidence using Remote Sensing Data

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