Association between Meteorological Factors and Mumps and Models for Prediction in Chongqing, China

Zhang, Hong; Su, Kun; Zhong, Xiaoni

doi:10.3390/ijerph19116625

Cited by 2 publications

(4 citation statements)

References 32 publications

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“…The ARIMA (Autoregressive Integrated Moving Average) model is the most frequently used method in time series analysis, based on the Box-Jenkins Model (1960), and can be used to predict the future values of time series using past values and can also analyze the multiple relationships between the independent and dependent variables 12 , 13 . ARIMA model was composed of autoregression (AR) with a lag number denoted by p, integrate (I) with a lag number denoted by d, and moving average (MA) with a lag number denoted by q. AR indicates that current observations are correlated with previous ones, which provides a possibility of predicting diseases with a time trend.…”

Section: Methodsmentioning

confidence: 99%

Predicting monthly hospital outpatient visits based on meteorological environmental factors using the ARIMA model

Bai

Lü

Dong

et al. 2023

Sci Rep

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Accurate forecasting of hospital outpatient visits is beneficial to the rational planning and allocation of medical resources to meet medical needs. Several studies have suggested that outpatient visits are related to meteorological environmental factors. We aimed to use the autoregressive integrated moving average (ARIMA) model to analyze the relationship between meteorological environmental factors and outpatient visits. Also, outpatient visits can be forecast for the future period. Monthly outpatient visits and meteorological environmental factors were collected from January 2015 to July 2021. An ARIMAX model was constructed by incorporating meteorological environmental factors as covariates to the ARIMA model, by evaluating the stationary $${R}^{2}$$ R 2 , coefficient of determination $${R}^{2}$$ R 2 , mean absolute percentage error (MAPE), and normalized Bayesian information criterion (BIC). The ARIMA $${(0, 1, 1) (0, 1, 0)}_{12}$$ ( 0 , 1 , 1 ) ( 0 , 1 , 0 ) 12 model with the covariates of $$\text{SO}_{2}$$ SO 2 , $${PM}_{2.5}$$ PM 2.5 , and $$\text{CO}$$ CO was the optimal model. Monthly outpatient visits in 2019 can be predicted using average data from past years. The relative error between the predicted and actual values for 2019 was 2.77%. Our study suggests that $$\text{SO}_{2}$$ SO 2 , $${PM}_{2.5}$$ PM 2.5 , and $$\text{CO}$$ CO concentration have a significant impact on outpatient visits. The model built has excellent predictive performance and can provide some references for the scientific management of hospitals to allocate staff and resources.

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Section: Methodsmentioning

confidence: 99%

Predicting monthly hospital outpatient visits based on meteorological environmental factors using the ARIMA model

Bai

Lü

Dong

et al. 2023

Sci Rep

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show abstract

“…The ARIMA model is commonly used for forecasting the incidence of scarlet fever. Considering the time autocorrelation between the data points and the seasonal transmission pattern of scarlet fever, the model expression is ARIMA(p,d,q)(P,D,Q) (S) [10], where d and D represent the nonseasonal and seasonal difference orders, respectively; p and q represent the autoregressive and moving average orders, respectively; P and Q represent the seasonal autoregressive and moving average orders, respectively; and S is the period of the sequence [8]. ARIMAX is a multivariable version of ARIMA that combined scarlet fever reported cases with the scarlet fever CSI, thus using the BSI as the external variable [26].…”

Section: Model Descriptionmentioning

confidence: 99%

“…We performed multiple fittings and searched for the optimal ARIMA(p,d,q)(P,D,Q) (S) model using the minimum AIC [26]. Then, we plotted the cross-correlation function (CCF) plot between scarlet fever and the CSI to determine the appropriate lag order for the ARIMAX model [8]. If the coefficient for a specific lag order exceeded 2 SD, the CSI at that lag order was considered correlated with scarlet fever.…”

Section: Model Selectionmentioning

confidence: 99%

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Early Warning and Prediction of Scarlet Fever in China Using the Baidu Search Index and Autoregressive Integrated Moving Average With Explanatory Variable (ARIMAX) Model: Time Series Analysis

Luo,

Zhou,

Yang

et al. 2023

J Med Internet Res

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Background Internet-derived data and the autoregressive integrated moving average (ARIMA) and ARIMA with explanatory variable (ARIMAX) models are extensively used for infectious disease surveillance. However, the effectiveness of the Baidu search index (BSI) in predicting the incidence of scarlet fever remains uncertain. Objective Our objective was to investigate whether a low-cost BSI monitoring system could potentially function as a valuable complement to traditional scarlet fever surveillance in China. Methods ARIMA and ARIMAX models were developed to predict the incidence of scarlet fever in China using data from the National Health Commission of the People’s Republic of China between January 2011 and August 2022. The procedures included establishing a keyword database, keyword selection and filtering through Spearman rank correlation and cross-correlation analyses, construction of the scarlet fever comprehensive search index (CSI), modeling with the training sets, predicting with the testing sets, and comparing the prediction performances. Results The average monthly incidence of scarlet fever was 4462.17 (SD 3011.75) cases, and annual incidence exhibited an upward trend until 2019. The keyword database contained 52 keywords, but only 6 highly relevant ones were selected for modeling. A high Spearman rank correlation was observed between the scarlet fever reported cases and the scarlet fever CSI (rs=0.881). We developed the ARIMA(4,0,0)(0,1,2)(12) model, and the ARIMA(4,0,0)(0,1,2)(12) + CSI (Lag=0) and ARIMAX(1,0,2)(2,0,0)(12) models were combined with the BSI. The 3 models had a good fit and passed the residuals Ljung-Box test. The ARIMA(4,0,0)(0,1,2)(12), ARIMA(4,0,0)(0,1,2)(12) + CSI (Lag=0), and ARIMAX(1,0,2)(2,0,0)(12) models demonstrated favorable predictive capabilities, with mean absolute errors of 1692.16 (95% CI 584.88-2799.44), 1067.89 (95% CI 402.02-1733.76), and 639.75 (95% CI 188.12-1091.38), respectively; root mean squared errors of 2036.92 (95% CI 929.64-3144.20), 1224.92 (95% CI 559.04-1890.79), and 830.80 (95% CI 379.17-1282.43), respectively; and mean absolute percentage errors of 4.33% (95% CI 0.54%-8.13%), 3.36% (95% CI –0.24% to 6.96%), and 2.16% (95% CI –0.69% to 5.00%), respectively. The ARIMAX models outperformed the ARIMA models and had better prediction performances with smaller values. Conclusions This study demonstrated that the BSI can be used for the early warning and prediction of scarlet fever, serving as a valuable supplement to traditional surveillance systems.

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Association between Meteorological Factors and Mumps and Models for Prediction in Chongqing, China

Cited by 2 publications

References 32 publications

Predicting monthly hospital outpatient visits based on meteorological environmental factors using the ARIMA model

Predicting monthly hospital outpatient visits based on meteorological environmental factors using the ARIMA model

Early Warning and Prediction of Scarlet Fever in China Using the Baidu Search Index and Autoregressive Integrated Moving Average With Explanatory Variable (ARIMAX) Model: Time Series Analysis

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