Comparison of ARIMA model and XGBoost model for prediction of human brucellosis in mainland China: a time-series study

ObjectivesHuman brucellosis is a public health problem endangering health and property in China. Predicting the trend and the seasonality of human brucellosis is of great significance for its prevention. In this study, a comparison between the autoregressive integrated moving average (ARIMA) model and the eXtreme Gradient Boosting (XGBoost) model was conducted to determine which was more suitable for predicting the occurrence of brucellosis in mainland China.DesignTime-series study.SettingMainland China.MethodsData on human brucellosis in mainland China were provided by the National Health and Family Planning Commission of China. The data were divided into a training set and a test set. The training set was composed of the monthly incidence of human brucellosis in mainland China from January 2008 to June 2018, and the test set was composed of the monthly incidence from July 2018 to June 2019. The mean absolute error (MAE), root mean square error (RMSE) and mean absolute percentage error (MAPE) were used to evaluate the effects of model fitting and prediction.ResultsThe number of human brucellosis patients in mainland China increased from 30 002 in 2008 to 40 328 in 2018. There was an increasing trend and obvious seasonal distribution in the original time series. For the training set, the MAE, RSME and MAPE of the ARIMA(0,1,1)×(0,1,1)12 model were 338.867, 450.223 and 10.323, respectively, and the MAE, RSME and MAPE of the XGBoost model were 189.332, 262.458 and 4.475, respectively. For the test set, the MAE, RSME and MAPE of the ARIMA(0,1,1)×(0,1,1)12 model were 529.406, 586.059 and 17.676, respectively, and the MAE, RSME and MAPE of the XGBoost model were 249.307, 280.645 and 7.643, respectively.ConclusionsThe performance of the XGBoost model was better than that of the ARIMA model. The XGBoost model is more suitable for prediction cases of human brucellosis in mainland China.

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Analysis of temporal trends of human brucellosis between 2013 and 2018 in Yazd Province, Iran to predict future trends in incidence: A time-series study using ARIMA model

Asian Pacific Journal of Tropical Medicine ◽

10.4103/1995-7645.281528 ◽

2020 ◽

Vol 13 (6) ◽

pp. 272

Author(s):

Saied Bokaie ◽

Vahid Rahmanian ◽

Karamatollah Rahmanian ◽

Saeed Hosseini ◽

AliakbarTaj Firouzeh

Keyword(s):

Time Series ◽

Arima Model ◽

Temporal Trends ◽

Human Brucellosis ◽

Future Trends ◽

Time Series Study ◽

Series Study

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Comparison of autoregressive integrated moving average model and generalised regression neural network model for prediction of haemorrhagic fever with renal syndrome in China: a time-series study

BMJ Open ◽

10.1136/bmjopen-2018-025773 ◽

2019 ◽

Vol 9 (6) ◽

pp. e025773 ◽

Cited By ~ 7

Author(s):

Ya-wen Wang ◽

Zhong-zhou Shen ◽

Yu Jiang

Keyword(s):

Time Series ◽

Hybrid Model ◽

Moving Average ◽

Arima Model ◽

Haemorrhagic Fever ◽

Autoregressive Integrated Moving Average ◽

Time Series Study ◽

Series Study ◽

Monthly Incidence ◽

Better Than

ObjectivesHaemorrhagic 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.DesignTime-series study.SettingThe People’s Republic of China.MethodsAutoregressive integrated moving average (ARIMA) model, generalised regression neural network (GRNN) model and hybrid ARIMA-GRNN model were constructed by R V.3.4.3 software. The monthly reported incidence of HFRS from January 2011 to May 2018 were adopted to evaluate models’ performance. Root mean square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) were adopted to evaluate these models’ effectiveness. Spatial stratified heterogeneity of the time series was tested by month and another GRNN model was built with a new series.ResultsThe monthly incidence of HFRS in the past several years showed a slight downtrend and obvious seasonal variation. A total of four plausible ARIMA models were built and ARIMA(2,1,1) (2,1,1)12model was selected as the optimal model in HFRS fitting. The smooth factors of the basic GRNN model and the hybrid model were 0.027 and 0.043, respectively. The single ARIMA model was the best in fitting part (MAPE=9.1154, MAE=89.0302, RMSE=138.8356) while the hybrid model was the best in prediction (MAPE=17.8335, MAE=152.3013, RMSE=196.4682). GRNN model was revised by building model with new series and the forecasting performance of revised model (MAPE=17.6095, MAE=163.8000, RMSE=169.4751) was better than original GRNN model (MAPE=19.2029, MAE=177.0356, RMSE=202.1684).ConclusionsThe hybrid ARIMA-GRNN model was better than single ARIMA and basic GRNN model in forecasting monthly incidence of HFRS in China. It could be considered as a decision-making tool in HFRS prevention and control.

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Evaluating the effectiveness of national measles elimination action in mainland China during 2004–2016: A multi-site interrupted time-series study

Vaccine ◽

10.1016/j.vaccine.2020.04.053 ◽

2020 ◽

Vol 38 (28) ◽

pp. 4440-4447

Author(s):

Lijun Xu ◽

Jun Chen ◽

Zhiying Zhan ◽

Tingting Chen ◽

Wenhui Liu ◽

...

Keyword(s):

Time Series ◽

Mainland China ◽

Interrupted Time Series ◽

Time Series Study ◽

Measles Elimination ◽

Series Study

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An alternative comprehensive index to quantify the interactive effect of temperature and relative humidity on hand, foot and mouth disease: A two-stage time series study including 143 cities in mainland China

The Science of The Total Environment ◽

10.1016/j.scitotenv.2020.140106 ◽

2020 ◽

Vol 740 ◽

pp. 140106 ◽

Cited By ~ 1

Author(s):

Chaonan Fan ◽

Fengfeng Liu ◽

Xing Zhao ◽

Yue Ma ◽

Fan Yang ◽

...

Keyword(s):

Time Series ◽

Relative Humidity ◽

Interactive Effect ◽

Mainland China ◽

Foot And Mouth Disease ◽

Effect Of Temperature ◽

Two Stage ◽

Time Series Study ◽

Comprehensive Index ◽

Series Study

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Tuberculosis forecasting and temporal trends by gender and age in a high endemic city of Northeast Brazil: Are we achieving the disease elimination stage?

10.21203/rs.3.rs-147043/v1 ◽

2021 ◽

Author(s):

Hamilton Leandro Andrade ◽

Luiz Arroyo ◽

Antônio Carlos Ramos ◽

Marcelino Neto ◽

Melina Yamamura ◽

...

Keyword(s):

Time Series ◽

Temporal Trend ◽

Moving Average ◽

Arima Model ◽

Temporal Trends ◽

Northeast Brazil ◽

Age Group ◽

Time Series Study ◽

Gender And Age ◽

Series Study

Abstract Objective: to describe the temporal trend of tuberculosis cases according to gender and age group and to make forecasts in an endemic municipality of northeast Brazil. Method: This was a Time Series study, carried out in a municipality in the northeast of Brazil. Population was composed of tuberculosis cases among residents of the municipality, reported between the years 2002 and 2018. An exploratory analysis of the monthly rates of tuberculosis detection, smoothed according to gender and age group, was performed. Subsequently, the progression of the trend and predictions of the disease were also characterized according to these aspects. For the trends forecast, the seasonal autoregressive linear integrated moving average – Seasonal ARIMA model and the usual Box-Jenkins method were used to choose the most appropriate models.Results: A total of 1,620 cases of tuberculosis were reported, with an incidence of 49.7 cases per 100,000 inhabitants in men and 34.0 per 100,000 in women. Regarding the incidence for both genders, there was a decreasing trend, which was similar for age. Evidence resulting from the application of the time series shows a decreasing trend between the years 2002–2018, however, it is unlikely that there will be a significant fall in the disease before 2022.

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