Forecasting the monthly incidence of scarlet fever in Chongqing, China using the SARIMA model
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The incidence of scarlet fever has increased dramatically in recent years in Chongqing, China, but there has no effective method to forecast it. This study aimed to develop a forecasting model of the incidence of scarlet fever using a seasonal autoregressive integrated moving average (SARIMA) model. Monthly scarlet fever data between 2011 and 2019 in Chongqing, China were retrieved from the Notifiable Infectious Disease Surveillance System. From 2011 to 2019, a total of 5073 scarlet fever cases were reported in Chongqing, the male-to-female ratio was 1.44:1, children aged 3-9 years old accounted for 81.86% of the cases, while 42.70 and 42.58% of the reported cases were students and kindergarten children, respectively. The data from 2011 to 2018 were used to fit a SARIMA model and data in 2019 were used to validate the model. The normalised Bayesian information criterion (BIC), the coefficient of determination (R2) and the root mean squared error (RMSE) were used to evaluate the goodness-of-fit of the fitted model. The optimal SARIMA model was identified as (3, 1, 3) (3, 1, 0)12. The RMSE and mean absolute per cent error (MAPE) were used to assess the accuracy of the model. The RMSE and MAPE of the predicted values were 19.40 and 0.25 respectively, indicating that the predicted values matched the observed values reasonably well. Taken together, the SARIMA model could be employed to forecast scarlet fever incidence trend, providing support for scarlet fever control and prevention.Keywords:
Scarlet fever
This research study focuses on forecasting the future values of peak demand in electricity consumption for Luzon, Philippines based on monthly historical data spanning from 2001 to 2020. The data was obtained from the official website of the Philippines Department of Energy (DOE). The primary objective of this study is to employ the ARIMA (Autoregressive Integrated Moving Average) model-building procedure developed by Box and Jenkins to accomplish accurate peak demand forecasting. The methodology involved conducting various tests and evaluations to identify the ARIMA model with the least Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE). After careful analysis, the best-fitting ARIMA model was determined to be ARIMA (11, 1, 12). The findings of this study indicate that, according to the ARIMA (11, 1, 12) model, Luzon's peak demand is projected to reach 10,497.65 megawatts by December 2021. Furthermore, the model predicts that by the end of 2022, 2023, and 2024, Luzon's peak demand will be approximately 10,738.34 MW, 10,953.98 MW, and 11,148.43 MW per electrical grid, respectively. The accuracy of the ARIMA (11, 1, 12) model is found to be satisfactory, with a low MAPE value of 3.639% and the most negligible RMSE value of 517.132. The implications of these forecasted peak demand values are significant for decision-makers in the energy and utilities sector. The accurate predictions provided by the ARIMA model can aid in resource allocation, infrastructure planning, and overall operational strategies to effectively meet the anticipated high-demand periods. In conclusion, this study successfully forecasts Luzon's future values of peak demand in electricity consumption using the ARIMA (11, 1, 12) model. The findings highlight the importance of accurate peak demand forecasting and provide valuable insights for energy industry professionals.
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Summary. The incidence of re‐infection in scarlet fever patients has been examined in a material of 35 patients. Re‐infection occurred rarely—a total of two definite and three indefinite cases. All cases occurred in the same room in which nineteen children were accommodated during the period of examination. The author discusses the possible causes of the low incidence of re‐infection. Four of the re‐infected patients had complications.
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The spread of COVID-19, namely SARS-CoV-2, has created a disastrous situation around the world causing an unclear future. Machine Learning (ML) and Deep Learning (DL) have a vital role in tracking the disease, predicting the outgrowth of the epidemic, and outlining strategies and policies to control its spread. Despite the inaccuracies of medical forecasts, the numbers of COVID-19 cases forecasts provide us with valuable information for recognizing the present and preparing for the future. This study proposes a time series based deep learning model, specifically the Long Short-Term Memory (LSTM) model. The model will predict the active, confirmed, deaths and recovered cases for 7 days ahead for Egypt and Saudi Arabia based on real-time data. The Egypt prediction model achieves Mean Absolute Percentage Error (MAPE) of 3.26150, a Root Mean Square Error (RMSE) of 0.0144, a Mean Square Error (MSE) of 0.0002, and a Mean Absolute Error (MAE) of 0.0092. While the Saudi prediction model obtains a MAPE of 5.0553, a RMSE of 0.0170, a MSE of 0.0002, and a MAE of 0.0150.
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This paper analyzes and predicts the Average Global Temperature time series data. Three different variants of ARIMA models: Basic ARIMA, Trend based ARIMA and Wavelet based ARIMA have been used to predict the average global temperature. Out of all the three linear models, it has been observed that Trend based ARIMA method outperforms basic ARIMA method and Wavelet based ARIMA method outperforms Trend based ARIMA method. MAPE (Mean Absolute Percentage Error), MaxAPE (Maximum Absolute Percentage Error) and MAE (Mean Absolute Error) have been used as a performance measures to compare between the models.
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Haemorrhagic fever with renal syndrome (HFRS) is a serious threat to public health in China, accounting for almost 90% cases reported globally. Infectious disease prediction may help in disease prevention despite some uncontrollable influence factors. This study conducted a comparison between a hybrid model and two single models in forecasting the monthly incidence of HFRS in China.
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Abstract Objective: Scarlet fever is an increasingly serious public health problem that has attracted widespread attention worldwide. In this study, two models were constructed based on time series to predict the number of scarlet fever incidence in Jiangsu province, China Methods: Two models, ARIMA model and TBATS model, were constructed to predict the number of scarlet fever incidence in Jiangsu province, China, in the first half of 2022 based on the number of scarlet fever incidence from 2013-2021, and root mean square error (RMSE) and mean absolute percentage error (MAPE) were used to select the models and evaluate the performance of the models. Results: The incidence of scarlet fever in Jiangsu province from 2013 to 2021 was significantly bi-seasonal and trendy, and the best ARIMA model established was ARIMA(1,0,1)(2,1,1) 12 , with RMSE=92.23 and MAPE=47.48% for the fitting part and RMSE=138.31 and MAPE=79.11 for the prediction part. The best The best TBATS model is TBATS(0.278,{0,0}, -, {<12,5>}) with RMSE=69.85 and MAPE=27.44% for the fitted part. The RMSE of the prediction part=57.11, MAPE=39.52%. The error of TBATS is smaller than that of ARIMA model for both fitting and forecasting. Conclusion: The TBATS model outperformed the most commonly used SARIMA model in predicting the number of scarlet fever incidence in Jiangsu Province, China, and can be used as a flexible and useful tool in the decision-making process of scarlet fever prevention and control in Jiangsu Province
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Brucellosis is a major public health problem that seriously affects developing countries and could cause significant economic losses to the livestock industry and great harm to human health. Reasonable prediction of the incidence is of great significance in controlling brucellosis and taking preventive measures.Our human brucellosis incidence data were extracted from Shanxi Provincial Center for Disease Control and Prevention. We used seasonal-trend decomposition using Loess (STL) and monthplot to analyse the seasonal characteristics of human brucellosis in Shanxi Province from 2007 to 2017. The autoregressive integrated moving average (ARIMA) model, a combined model of ARIMA and the back propagation neural network (ARIMA-BPNN), and a combined model of ARIMA and the Elman recurrent neural network (ARIMA-ERNN) were established separately to make predictions and identify the best model. Additionally, the mean squared error (MAE), mean absolute error (MSE) and mean absolute percentage error (MAPE) were used to evaluate the performance of the model.We observed that the time series of human brucellosis in Shanxi Province increased from 2007 to 2014 but decreased from 2015 to 2017. It had obvious seasonal characteristics, with the peak lasting from March to July every year. The best fitting and prediction effect was the ARIMA-ERNN model. Compared with those of the ARIMA model, the MAE, MSE and MAPE of the ARIMA-ERNN model decreased by 18.65, 31.48 and 64.35%, respectively, in fitting performance; in terms of prediction performance, the MAE, MSE and MAPE decreased by 60.19, 75.30 and 64.35%, respectively. Second, compared with those of ARIMA-BPNN, the MAE, MSE and MAPE of ARIMA-ERNN decreased by 9.60, 15.73 and 11.58%, respectively, in fitting performance; in terms of prediction performance, the MAE, MSE and MAPE decreased by 31.63, 45.79 and 29.59%, respectively.The time series of human brucellosis in Shanxi Province from 2007 to 2017 showed obvious seasonal characteristics. The fitting and prediction performances of the ARIMA-ERNN model were better than those of the ARIMA-BPNN and ARIMA models. This will provide some theoretical support for the prediction of infectious diseases and will be beneficial to public health decision making.
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Cases of hemorrhagic fever with renal syndrome (HFRS) are widely distributed in eastern Asia, especially in China, Russia, and Korea. It is proved to be a difficult task to eliminate HFRS completely because of the diverse animal reservoirs and effects of global warming. Reliable forecasting is useful for the prevention and control of HFRS.Two hybrid models, one composed of nonlinear autoregressive neural network (NARNN) and autoregressive integrated moving average (ARIMA) the other composed of generalized regression neural network (GRNN) and ARIMA were constructed to predict the incidence of HFRS in the future one year. Performances of the two hybrid models were compared with ARIMA model.The ARIMA, ARIMA-NARNN ARIMA-GRNN model fitted and predicted the seasonal fluctuation well. Among the three models, the mean square error (MSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) of ARIMA-NARNN hybrid model was the lowest both in modeling stage and forecasting stage. As for the ARIMA-GRNN hybrid model, the MSE, MAE and MAPE of modeling performance and the MSE and MAE of forecasting performance were less than the ARIMA model, but the MAPE of forecasting performance did not improve.Developing and applying the ARIMA-NARNN hybrid model is an effective method to make us better understand the epidemic characteristics of HFRS and could be helpful to the prevention and control of HFRS.
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The work presented in this paper establishes an enrichment in modeling and forecasting over-dues for Beverages manufacturing company. A time-series modeling technique used to forecast over-dues for ABinBEV (Beer manufacturing company). Our work demonstrates how historical over-dues data utilized to predict future over-dues. The historical over-dues information used to develop several Autoregressive Integrated Moving Average (ARIMA) models by using Root mean squared error (RMSE) and the most suitable ARIMA model found to be ARIMA (2, 1, 0). and validation performed by comparing the accuracy of the models with three types of accuracy criteria, which are Mean square error (MSE), Root Mean Squared Error (RMSE), and Mean absolute error (MAE).
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