Comparisons of forecasting for hepatitis in Guangxi Province, China by using three neural networks models

Gan, Ruijing; Chen, Ni; Huang, Daizheng

doi:10.7717/peerj.2684

Cited by 8 publications

(6 citation statements)

References 24 publications

(19 reference statements)

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“…This pattern seems to be dependent on the etiology of liver diseases: hepatitis A and hepatitis E have prominent peaks in the spring and summer, 19 primary biliary cirrhosis peaks in June, 20 and hepatitis B peaks in January and Febru-ary. 21 Similarly, this study found seasonal variance associated with the incidence of ACLF, a fatal liver disease. These results revealed again the existence of seasonal pattern of liver diseases.…”

Section: Discussionsupporting

confidence: 61%

The Association between Meteorological Factors and the Prevalence of Acute-on-chronic Liver Failure: A Population-based Study, 2007–2016

Lin

Han

Li³

et al. 2019

Journal of Clinical and Translational Hepatology

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Background and Aims: The aim of this study was to investigate the effect(s) of meteorological factors on the prevalence of acute-on-chronic liver failure (ACLF) based on 10-years' worth of population data. Methods: We retrospectively collected ACLF case data from January 2007 to December 2016 from three major hospitals in Fuzhou City, China. Climatic data, including rainfall, mean temperature, differences in temperature (delta temperature) and mean humidity for each month were downloaded from the China Climatic Data Service Center. Following data collection, Poisson regression analysis was used to estimate the effect(s) of climatic factors on the risk of the prevalence of ACLF. Results: The population consisted of a total of 3510 cases, with a mean age of 44.7 ± 14.8 years-old and with 79.8% being male. Upon analyzing the population data, we found a growing trend and seasonal pattern of monthly counts of ACLF-related hospitalization throughout the past decade. Specifically, the primary peak of ACLF prevalence was in January and the secondary peak was in July. Poisson regression showed mean temperature (risk ratio = 0.991, 95%CI = 0.986-0.996) and mean humidity (risk ratio = 1.011, 95%CI = 1.006-1.017) to be independently correlated with the monthly cases of ACLF. The results suggest that every unit increase of mean temperature (1°C) and mean humidity (1%) are associated with 0.991-and 1.011-fold changes of ACLF cases, respectively. Rainfall and delta temperature did not appear to affect the prevalence of this disease. Conclusions: The hospitalization for ACLF peaks in January and July. Low temperature and high humidity appear to function as factors contributing to this seasonal pattern.

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Section: Discussionsupporting

confidence: 61%

The Association between Meteorological Factors and the Prevalence of Acute-on-chronic Liver Failure: A Population-based Study, 2007–2016

Lin

Han

Li³

et al. 2019

Journal of Clinical and Translational Hepatology

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“…The number of neurons of the output layer and the dimension of the input vector of the learning samples are the same. The formula of the output of the -th neurons is as follows: 28…”

Section: Methodsmentioning

confidence: 99%

<p>Application of a hybrid model in predicting the incidence of tuberculosis in a Chinese population</p>

Li¹,

Wang²,

Song³

et al. 2019

IDR

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Objective: To investigate suitable forecasting models for tuberculosis (TB) in a Chinese population by comparing the predictive value of the autoregressive integrated moving average (ARIMA) model and the ARIMA-generalized regression neural network (GRNN) hybrid model. Methods: We used the monthly incidence rate of TB in Lianyungang city from January 2007 through June 2016 to construct a fitting model, and we used the incidence rate from July 2016 to December 2016 to evaluate the forecasting accuracy. The root mean square error (RMSE), mean absolute percentage error (MAPE), mean absolute error (MAE) and mean error rate (MER) were used to assess the performance of these models in fitting and forecasting the incidence of TB. Results: The ARIMA (10, 1, 0) (0, 1, 1) 12 model was selected from plausible ARIMA models, and the optimal spread value of the ARIMA-GRNN hybrid model was 0.23. For the fitting dataset, the RMSE, MAPE, MAE and MER were 0.5594, 11.5000, 0.4202 and 0.1132, respectively, for the ARIMA (10, 1, 0) (0, 1, 1) 12 model, and 0.5259, 11.2181, 0.3992 and 0.1075, respectively, for the ARIMA-GRNN hybrid model. For the forecasting dataset, the RMSE, MAPE, MAE and MER were 0.2805, 8.8797, 0.2261 and 0.0851, respectively, for the ARIMA (10, 1, 0) (0, 1, 1) 12 model, and 0.2553, 5.7222, 0.1519 and 0.0571, respectively, for the ARIMA-GRNN hybrid model. Conclusions: The ARIMA-GRNN hybrid model was shown to be superior to the single ARIMA model in predicting the short-term TB incidence in the Chinese population, especially in fitting and forecasting the peak and trough incidence.

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“…Researchers have put forward various models in the fitting estimation of epidemic diseases. Different fitting models were selected according to data kinds which mainly include linear model [17]- [19] and nonlinear model [20], [21]. In this example, we found that the number of epidemic cases is very less than baidu searches data.…”

Section: Introductionmentioning

confidence: 99%

Using Partial Least Squares Regression to Fit Small Data of H7N9 Incidence Based on the Baidu Index

Gan

Tan

et al. 2020

IEEE Access

Self Cite

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The internet search data will help the disease control department to estimate the disease in advance. The H7N9 epidemic that occurred in Guangxi Province was used as an example to demonstrate its association with Baidu search data. At first,16 search terms which have high correlation with H7N9 disease were selected by expert determination and calculation. At the same time, the number of disease cases were downloaded from the website of Guangxi CDC. The partial least square regression was choosed to estimate after comparing the regression models for the number of epidemic cases is very less than baidu searches data. To filter independent variables, cross validation and variable importance in projection were applied. The results show that: 1.the proposed method is suitable for fitting the data of H7N9 disease with few samples, and the fitting degree is perfect. 2.it will help to screen out the important searching index which are more relate to H7N9 epidemic by using cross validation and variable import in project. 3.compared with the PCA methods, the proposed method presented great advantages in performance index, especially with the help of cross validation and variable importance in projection. INDEX TERMS Partial least squares regression, H7N9, Baidu index, variable importance in projection.

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Comparisons of forecasting for hepatitis in Guangxi Province, China by using three neural networks models

Cited by 8 publications

References 24 publications

The Association between Meteorological Factors and the Prevalence of Acute-on-chronic Liver Failure: A Population-based Study, 2007–2016

The Association between Meteorological Factors and the Prevalence of Acute-on-chronic Liver Failure: A Population-based Study, 2007–2016

<p>Application of a hybrid model in predicting the incidence of tuberculosis in a Chinese population</p>

Using Partial Least Squares Regression to Fit Small Data of H7N9 Incidence Based on the Baidu Index

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