Seasonality of Acute Lyme Disease in Children

Due to the life cycle of its vector, Lyme disease has known seasonal variation. However, investigations focused on children have been limited. Our objective was to evaluate the seasonality of pediatric Lyme disease in three endemic regions in the United States. We enrolled children presenting to one of eight Pedi Lyme Net participating emergency departments. Cases were classified based on presenting symptoms: early (single erythema migrans (EM) lesion), early-disseminated (multiple EM lesions, headache, cranial neuropathy, or carditis), or late (arthritis). We defined a case of Lyme disease by the presence of an EM lesion or a positive two-tier Lyme disease serology. To measure seasonal variability, we estimated Fourier regression models to capture cyclical patterns in Lyme disease incidence. While most children with early or early-disseminated Lyme disease presented during the summer months, children with Lyme arthritis presented throughout the year. Clinicians should consider Lyme disease when evaluating children with acute arthritis throughout the year.

Hybrid Fourier Regression – Multilayer Perceptrons Neural Network for forecasting

Forecasting methods are advantageous tools to predict the future, especially for agricultural commodities production. This study aims to compare the forecasting method between Fourier Regression, Multilayer Perceptrons Neural Networks (MPNN), and introducing a new forecasting method hybrid Fourier Regression – Multilayer Perceptrons Neural Networks Model proposed by the author. These methods are applied to forecast the production of big chili commodities since it is one of the essential vegetable commodities with a high household and industrial consumption in Indonesia. The big chili production data used is monthly from January 2010 to June 2017 (in quintal units) sourced from Statistics Indonesia. The results show hybrid Fourier Regression – Multilayer Perceptrons Neural Networks Model is more accurate to forecast big chili production than Fourier Regression and Multilayer Perceptrons (MPNN). The MAPE produced by Fourier Regression-MPNN is the lowest compared to the other methods, which is 4.45. In summary, the use of the hybrid Fourier Regression-MPNN method in forecasting big chili production can help the government to find out the potential production of big chili in the next few quarters. Furthermore, the results are useful for considering some government policies about big chili needs such as making a decision to export or import big chili commodities.

Spline-Fourier’s Method for Modelling Inflation in Indonesia

Regression method is a statistical method for modelling dependent variable with independent variable. Nonparametric regression is an approach to regression analysis that is suitable for data that have an unknown curve shape. Modelling by using nonparametric regression method does not require any assumptions. Spline and Fourier methods are some of the estimators in nonparametric regression. The spline method requires optimal knots to obtain the best model. The most commonly used method to determine the optimal knots is Generalized Cross Validation (GCV). The Fourier method is a method based on the cosine and sinus series. The Fourier method is particularly suitable for data that experience repetitive patterns. This study modeled the Inflation rate in Indonesia from January 2007 to August 2017. The dependent variable is inflation rate, while the independent variable is time. From the result, linear spline regression estimation with three knots that generates R square of 60%. The best Fourier model is Fourier with K = 100 that generates R square of 80.12%. The best Spline model is with 9 knots generates R square of 87.65%, so, for inflation modelling in Indonesia, the spline regression model generates a simpler model with better R-square than Fourier regression.