Analysis of significant factors for dengue fever incidence prediction

Siriyasatien, Padet; Phumee, Atchara; Ongruk, Phatsavee; Jampachaisri, Katechan; Kesorn, Kraisak

doi:10.1186/s12859-016-1034-5

Cited by 53 publications

(42 citation statements)

References 30 publications

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“…Johansson et al stated that climate data did slightly improve the accuracy of the seasonal autoregressive dengue models for Mexico [13]. Female mosquitoes and seasons strongly correlated with the number of dengue cases in some provinces in Thailand [26]. Bangkok is one of the densest cities in the world, and it is likely to affect the pattern of dengue fever.…”

Section: Discussionmentioning

confidence: 99%

The time series seasonal patterns of dengue fever and associated weather variables in Bangkok (2003-2017)

Polwiang

2020

BMC Infect Dis

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Background: In Thailand, dengue fever is one of the most well-known public health problems. The objective of this study was to examine the epidemiology of dengue and determine the seasonal pattern of dengue and its associate to climate factors in Bangkok, Thailand, from 2003 to 2017. Methods: The dengue cases in Bangkok were collected monthly during the study period. The time-series data were extracted into the trend, seasonal, and random components using the seasonal decomposition procedure based on loess. The Spearman correlation analysis and artificial neuron network (ANN) were used to determine the association between climate variables (humidity, temperature, and rainfall) and dengue cases in Bangkok. Results: The seasonal-decomposition procedure showed that the seasonal component was weaker than the trend component for dengue cases during the study period. The Spearman correlation analysis showed that rainfall and humidity played a role in dengue transmission with correlation efficiency equal to 0.396 and 0.388, respectively. ANN showed that precipitation was the most crucial factor. The time series multivariate Poisson regression model revealed that increasing 1% of rainfall corresponded to an increase of 3.3% in the dengue cases in Bangkok. There were three models employed to forecast the dengue case, multivariate Poisson regression, ANN, and ARIMA. Each model displayed different accuracy, and multivariate Poisson regression was the most accurate approach in this study. Conclusion: This work demonstrates the significance of weather in dengue transmission in Bangkok and compares the accuracy of the different mathematical approaches to predict the dengue case. A single model may insufficient to forecast precisely a dengue outbreak, and climate factor may not only indicator of dengue transmissibility.

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Section: Discussionmentioning

confidence: 99%

The time series seasonal patterns of dengue fever and associated weather variables in Bangkok (2003-2017)

Polwiang

2020

BMC Infect Dis

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“…8,9 This allows the network to incorporate the intricate associations among variables into algorithms. 10 ANNs have been used in the medical field to perform a variety of difficult predictions: long-term functional recovery after spinal cord injury, 11 the incidence of dangerous viral infections, 12 and the toxicity of thrombolytic nanoparticles. 13 …”

Section: Introductionmentioning

confidence: 99%

Neural network prediction of severe lower intestinal bleeding and the need for surgical intervention

Loftus

Brakenridge

Croft

et al. 2017

Journal of Surgical Research

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Background The prognosis for patients with severe acute lower intestinal bleeding (ALIB) may be assessed by complex artificial neural networks (ANNs) or user-friendly regression-based models. Comparisons between these modalities are limited, and predicting the need for surgical intervention remains elusive. We hypothesized that ANNs would outperform the Strate rule to predict severe bleeding and would also predict the need for surgical intervention. Methods We performed a 4-y retrospective analysis of 147 adult patients who underwent endoscopy, angiography, or surgery for ALIB. Baseline characteristics, Strate risk factors, management parameters, and outcomes were analyzed. The primary outcomes were severe bleeding and surgical intervention. ANNs were created in SPSS. Models were compared by area under the receiver operating characteristic curve (AUROC) with 95% confidence intervals. Results The number of Strate risk factors for each patient correlated significantly with the outcome of severe bleeding (r = 0.29, P < 0.001). However, the Strate model was less accurate than an ANN (AUROC 0.66 [0.57–0.75] versus 0.98 [0.95–1.00], respectively) which incorporated six variables present on admission: hemoglobin, systolic blood pressure, outpatient prescription for Aspirin 325 mg daily, Charlson comorbidity index, base deficit ≥5 mEq/L, and international normalized ratio ≥1.5. A similar ANN including hemoglobin nadir and the occurrence of a 20% decrease in hematocrit was effective in predicting the need for surgery (AUROC 0.95 [0.90–1.00]). Conclusions The Strate prediction rule effectively stratified risk for severe ALIB, but was less accurate than an ANN. A separate ANN accurately predicted the need for surgery by combining risk factors for severe bleeding with parameters quantifying blood loss. Optimal prognostication may be achieved by integrating pragmatic regression-based calculators for quick decisions at the bedside and highly accurate ANNs when time and resources permit.

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“…Another work is done by Siriyasatien et al in 2016 [14]. Tis research says that the power of predictive models can be seen from dome factors such as Akaike's Information Criterion (AIC), Bayesian Information Criterion (BIC) and Mean Absolute Percentage Error (MAPE).…”

Section: A Related Workmentioning

confidence: 99%

Analysis of Performance Prediction Models in Predicting Dengue Fever Patients Number in Each Group of Malang Indonesia

Riksakomara¹,

Condro²,

Samopa³

et al. 2016

Fifth International Conference on Advances in Computing, Control and Networking - ACCN 2016

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Dengue Fever is one of acute and deadly diseases that commonly happens in tropical area. The spread of it is also influenced by geographical condition. Indonesia, Particularly in Malang that is a tropical area with a geographical condition supports the development of this disease. It needs a fat-moving action to the early step precaution so that the number of patients can be reduced. As the primary decision for an early prevention, it needs predictions about several cases of dengue fever of some period in the future. The result of this prediction is needed by Public Health Office of Malang as one of instances that responsible of dengue fever cases. This research analyses performance as a prediction model in getting the predictions in a number of dengue fever cases in Malang, Indonesia for some different group of data. The models suggested are Multiplicative Holt-Winters, Additive Holt-Winters, Multiplicative Decomposition and Autoregressive Moving Average (ARIMA). Those models are applied in special data to some cases in Malang which is categorized in 3 groups, namely Lowlands (Malang Rendah), Mediumlands (Malang Sedang), Highlands (Malang Tinggi). The result shows that Multiplicative Holt-Winters model is the best model for lowlands, and mediumlands. Meanwhile, for highlands is best-used with Multiplicative Decomposition

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Analysis of significant factors for dengue fever incidence prediction

Cited by 53 publications

References 30 publications

The time series seasonal patterns of dengue fever and associated weather variables in Bangkok (2003-2017)

The time series seasonal patterns of dengue fever and associated weather variables in Bangkok (2003-2017)

Neural network prediction of severe lower intestinal bleeding and the need for surgical intervention

Analysis of Performance Prediction Models in Predicting Dengue Fever Patients Number in Each Group of Malang Indonesia

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