scholarly journals Time-series modelling and forecasting of hand, foot and mouth disease cases in China from 2008 to 2018

2019 ◽  
Vol 147 ◽  
Author(s):  
C. W. Tian ◽  
H. Wang ◽  
X. M. Luo
Keyword(s):  
Time Series ◽  
Seasonal Variation ◽  
Moving Average ◽  
Foot And Mouth Disease ◽  
Mouth Disease ◽  
Sarima Model ◽  
Autoregressive Integrated Moving Average ◽  
Annual Peak ◽  
Time Series Modelling ◽  
Foot And Mouth

AbstractSeasonal autoregressive-integrated moving average (SARIMA) has been widely used to model and forecast incidence of infectious diseases in time-series analysis. This study aimed to model and forecast monthly cases of hand, foot and mouth disease (HFMD) in China. Monthly incidence HFMD cases in China from May 2008 to August 2018 were analysed with the SARIMA model. A seasonal variation of HFMD incidence was found from May 2008 to August 2018 in China, with a predominant peak from April to July and a trough from January to March. In addition, the annual peak occurred periodically with a large annual peak followed by a relatively small annual peak. A SARIMA model of SARIMA (1, 1, 2) (0, 1, 1)12 was identified, and the mean error rate and determination coefficient were 16.86% and 94.27%, respectively. There was an annual periodicity and seasonal variation of HFMD incidence in China, which could be predicted well by a SARIMA (1, 1, 2) (0, 1, 1)12 model.

scholarly journals Forecasting Hand-Foot-and-Mouth Disease Cases Using a Wavelet-based SARIMA-NNAR Hybrid Model

2020 ◽  
Author(s):  
Gongchao Yu ◽  
Huifen Feng ◽  
Shuang Feng ◽  
Jing Zhao ◽  
Jing Xu
Keyword(s):  
Time Series ◽  
Hybrid Model ◽  
Prevention And Control ◽  
Moving Average ◽  
Foot And Mouth Disease ◽  
Hybrid Models ◽  
Mouth Disease ◽  
High Morbidity ◽  
Foot And Mouth ◽  
And Control

Abstract Background: Hand-foot-and-mouth disease(HFMD) is one of the most common diseases in children, which has high morbidity. Reliable forecasting is significant for prevention and control. Recently, hybrid models have been becoming popular and wavelet analysis has been widely used. Better prediction accuracy may be achieved with wavelet-based hybrid models. Thus, our aim is to forecast number of HFMD cases with wavelet-based hybrid models.Materials and methods: We fitted a wavelet-based SARIMA(seasonal autoregressive integrated moving average)-NNAR(neural network nonlinear autoregressive) hybrid model with HFMD weekly cases from 2009 to 2016 in Zhengzhou, China. At the same time, single SARIMA model, simplex NNAR model and pure SARIMA-NNAR hybrid model were established as well for comparison and estimation.Results: The wavelet-based SARIMA-NNAR hybrid model had an excellent performance whether in fitting or in forecasting compared to other models. Its fitted and forecasting time series were approximate to the actual observed time series.Conclusions: This wavelet-based SARIMA-NNAR hybrid model that we fitted is suitable for forecasting number of HFMD cases. It will facilitate prevention and control of HFMD.

scholarly journals Trend analysis and forecast of daily reported incidence of hand, foot and mouth disease in Hubei, China by Prophet model

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Cong Xie ◽  
Haoyu Wen ◽  
Wenwen Yang ◽  
Jing Cai ◽  
Peng Zhang ◽  
...  
Keyword(s):  
Moving Average ◽  
Arima Model ◽  
Foot And Mouth Disease ◽  
Mouth Disease ◽  
Spring Festival ◽  
High Incidence ◽  
Foot And Mouth ◽  
Accurate Forecast ◽  
Changing Patterns ◽  
Two Peaks

AbstractHand, foot, and mouth disease (HFMD) is common among children below 5 years. HFMD has a high incidence in Hubei Province, China. In this study, the Prophet model was used to forecast the incidence of HFMD in comparison with the autoregressive-integrated moving average (ARIMA) model, and HFMD incidence was decomposed into trends, yearly, weekly seasonality and holiday effect. The Prophet model fitted better than the ARIMA model in daily reported incidence of HFMD. The HFMD incidence forecast by the Prophet model showed that two peaks occurred in 2019, with the higher peak in May and the lower peak in December. Periodically changing patterns of HFMD incidence were observed after decomposing the time-series into its major components. In specific, multi-year variability of HFMD incidence was found, and the slow-down increasing point of HFMD incidence was identified. Relatively high HFMD incidences appeared in May and on Mondays. The effect of Spring Festival on HFMD incidence was much stronger than that of other holidays. This study showed the potential of the Prophet model to detect seasonality in HFMD incidence. Our next goal is to incorporate climate variables into the Prophet model to produce an accurate forecast of HFMD incidence.

scholarly journals Time-Series Statistical Model for Forecasting Revenue and Risk Management

2021 ◽  
Vol 10 (3) ◽  
Author(s):  
Mihir Kelkar ◽  
Cosmin Borsa ◽  
Lina Kim
Keyword(s):  
Time Series ◽  
Low Cost ◽  
Moving Average ◽  
Business Strategies ◽  
Annual Reports ◽  
Fiscal Year ◽  
Sarima Model ◽  
Autoregressive Integrated Moving Average ◽  
Low Cost Carrier ◽  
Southwest Airlines

Following a Low-Cost Carrier (LCC) model, Southwest Airlines has consistently demonstrated growing annual revenues up until the start of the COVID-19 pandemic. Southwest’s quarterly revenue shows that there exists a strong seasonal component with the revenue in the first quarter of the fiscal year (September) significantly higher than other quarters. Using the quarterly revenue data we constructed a time-series model: a seasonal autoregressive integrated moving average (SARIMA) model to forecast Southwest’s revenue over 2020. We then performed a cost and solvency risk analysis using the company’s financial results from its annual reports to analyze Southwest’s financial performance due to COVID-19, and proposed business strategies to keep Southwest financially stable.

scholarly journals Early Evidence of Inactivated Enterovirus 71 Vaccine Impact Against Hand, Foot, and Mouth Disease in a Major Center of Ongoing Transmission in China, 2011–2018: A Longitudinal Surveillance Study

2019 ◽  
Author(s):  
Jennifer R Head ◽  
Philip A Collender ◽  
Joseph A Lewnard ◽  
Nicholas K Skaff ◽  
Ling Li ◽  
...  
Keyword(s):  
Time Series ◽  
Incidence Rate ◽  
Enterovirus 71 ◽  
Foot And Mouth Disease ◽  
Mouth Disease ◽  
Asia Pacific ◽  
Full Time ◽  
Study Region ◽  
Foot And Mouth ◽  
Vaccine Impact

Abstract Background Enterovirus 71 (EV71) is a major causative agent of hand, foot, and mouth disease (HFMD), associated with severe manifestations of the disease. Pediatric immunization with inactivated EV71 vaccine was initiated in 2016 in the Asia-Pacific region, including China. We analyzed a time series of HFMD cases attributable to EV71, coxsackievirus A16 (CA16), and other enteroviruses in Chengdu, a major transmission center in China, to assess early impacts of immunization. Methods Reported HFMD cases were obtained from China’s notifiable disease surveillance system. We compared observed postvaccination incidence rates during 2017–2018 with counterfactual predictions made from a negative binomial regression and a random forest model fitted to prevaccine years (2011–2015). We fit a change point model to the full time series to evaluate whether the trend of EV71 HFMD changed following vaccination. Results Between 2011 and 2018, 279 352 HFMD cases were reported in the study region. The average incidence rate of EV71 HFMD in 2017–2018 was 60% (95% prediction interval [PI], 41%–72%) lower than predicted in the absence of immunization, corresponding to an estimated 6911 (95% PI, 3246–11 542) EV71 cases averted over 2 years. There were 52% (95% PI, 42%–60%) fewer severe HFMD cases than predicted. However, the incidence rate of non-CA16 and non-EV71 HFMD was elevated in 2018. We identified a significant decline in the trend of EV71 HFMD 4 months into the postvaccine period. Conclusions We provide the first real-world evidence that programmatic vaccination against EV71 is effective against childhood HFMD and present an approach to detect early vaccine impact or intended consequences from surveillance data.

scholarly journals Social mobility and gonorrhoea in Germany during 2020

2021 ◽  
pp. sextrans-2021-055159
Author(s):  
Mihály Sulyok ◽  
Mark Walker
Keyword(s):  
Time Series ◽  
Health Services ◽  
Infectious Diseases ◽  
Social Mobility ◽  
Social Factors ◽  
Moving Average ◽  
Social Activity ◽  
Autoregressive Integrated Moving Average ◽  
Time Series Modelling ◽  
The Relationship

ObjectivesThe incidence of STIs is likely to be related to levels of social activity and mobility. Novel datasets detailing levels of social activity were made widely available during the COVID-19 pandemic. These allow the relationship between activity and STI incidence to be examined.MethodsThe correlation between social activities and the reported number of gonorrhoea cases between March and December 2020 in Germany was studied. Regression through Autoregressive Integrated Moving Average (ARIMA) time series modelling identified those activities associated with case numbers.ResultsARIMA regression identified a significant association with ‘transit’ activity within the Apple data and ‘parks’ within Google.ConclusionsThis study illustrates the potential newly available measures of social activity provided for STI research. Reductions in STI incidence are likely to have occurred due to COVID-19 social restrictions. Although other studies report reductions in infectious diseases during this period, few examine the potential social factors mediating this. The results illustrate the continual need for sexual health services throughout the pandemic.

scholarly journals Spatiotemporal characteristics and the epidemiology of tuberculosis from 2004 to 2017 in China by the nationwide surveillance system

2020 ◽  
Author(s):  
Zhongbao Zuo ◽  
Miaochan Wang ◽  
Huaizhong Cui ◽  
Ying Wang ◽  
Jing Wu ◽  
...  
Keyword(s):  
Time Series ◽  
Regression Analysis ◽  
Moving Average ◽  
Multivariate Time Series ◽  
Joinpoint Regression ◽  
Sarima Model ◽  
Autoregressive Integrated Moving Average ◽  
The Past ◽  
Leading Role ◽  
Joinpoint Regression Analysis

Abstract BackgroundChina has always been one of the countries with the most serious tuberculosis epidemic in the world. Our study was to observe the Spatial-temporal characteristics and the epidemiology of tuberculosis in China from 2004 to 2017 with Joinpoint regression analysis, Seasonal Autoregressive integrated moving average (SARIMA) model, geographic cluster, and multivariate time series model.MethodsThe data of TB from January 2004 to December 2017 were obtained from the notifiable infectious disease reporting system supplied by the Chinese Center for Disease Control and Prevention. The incidence trend of TB was observed by the Joinpoint regression analysis. The Seasonal autoregressive integrated moving average (SARIMA) model was used to predict the monthly incidence. Geographic clusters was employed to analyze the Spatial autocorrelation analysis was performed to detect. The heterogeneous transmission of TB was detected by the multivariate time series model. ResultsWe included 13,991,850 TB cases from January 2004 to December 2017, with a yearly average morbidity of 999,417 cases. The final selected model was the 0 Joinpoint model (P=0.0001) with an annual average percent change (AAPC) of -3.3 (95% CI: -4.3 to -2.2, P<0.001). A seasonality was observed across the fourteen years, and the seasonal peaks were in January and March every year. The best SARIMA model was (0, 1, 1) X (0, 1, 1)12 which can be written as (1-B) (1-B12) Xt = (1-0.42349B) (1-0.43338B12) εt, with a minimum AIC (880.5) and SBC (886.4). The predicted value and the original incidence data of 2017 were well matched. The MSE, RMSE, MAE, and MAPE of the modelling performance were 201.76, 14.2, 8.4 and 0.06, respectively. The provinces with a high incidence were located in the northwest (Xinjiang, Tibet) and south (Guangxi, Guizhou, Hainan) of China. The hotspot of TB transmission was mainly located at southern region of China from 2004 to 2008, including Hainan, Guangxi, Guizhou, and Chongqing, which disappeared in the later years. The autoregressive component had a leading role in the incidence of TB which accounted for 81.5% - 84.5% of the patients on average. The endemic component was about twice as large in the western provinces as the average while the spatial-temporal component was less important there. Most of the high incidences (>70 cases per 100,000) were influenced by the autoregressive component for the past fourteen years. ConclusionIn a word, China still has a high TB incidence. However, the incidence rate of TB was significantly decreasing from 2004 to 2017 in China. Seasonal peaks were in January and March every year. Obvious geographical clusters were observed in Tibet and Xinjiang Province. The spatial heterogeneity of TB driving transmission was distinguished from the multivariate time series model. For every provinces over the past fourteen years, the autoregressive component played a leading role in the incidence of TB which need us to enhance the early protective implementation.

scholarly journals Estimates of reference evapotranspiration in the municipality of Ariquemes (RO) using neural networks GMDH-type

2019 ◽  
Vol 23 (5) ◽  
pp. 324-329
Author(s):  
Roberto L. da S. Carvalho ◽  
Angel R. S. Delgado
Keyword(s):  
Time Series ◽  
Reference Evapotranspiration ◽  
Moving Average ◽  
Group Method ◽  
Data Handling ◽  
Sarima Model ◽  
Autoregressive Integrated Moving Average ◽  
Irrigation Methods ◽  
Artificial Neural Network Ann ◽  
Climatic Time Series

ABSTRACT Reference evapotranspiration is a climatological variable of great importance for water use dimensioning in irrigation methods. In order to contribute to the climatic understanding of Ariquemes, Rodônia state, Brazil, the study aims to model the behavior of the time series of reference evapotranspiration using a GMDH-type (Group Method of Data Handling) artificial neural network (ANN) and to compare it with the SARIMA (Seasonal Autoregressive Integrated Moving Average) methodology. Data from the National Institute of Meteorology - INMET, obtained at the Automatic Weather Station of Ariquemes, from January 2011 to January 2014, were used. Data analysis was performed using software R version 3.3.1 through the GMDH-type ANN package. Modeling by GMDH-type ANN led to results similar to the results of the SARIMA model, thus constituting an option to predict climatic time series. GMDH-type models with larger numbers of inputs and layers presented lowest mean square error.

Sign in / Sign up

Export Citation Format

Share Document